{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mt9dL5dIir8X"
   },
   "source": [
    "##### Copyright 2022 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2023-11-07T23:36:54.210948Z",
     "iopub.status.busy": "2023-11-07T23:36:54.210380Z",
     "iopub.status.idle": "2023-11-07T23:36:54.214649Z",
     "shell.execute_reply": "2023-11-07T23:36:54.214013Z"
    },
    "id": "ufPx7EiCiqgR"
   },
   "outputs": [],
   "source": [
    "#@title 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",
    "# https://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": "4StGz9ynOEL6"
   },
   "source": [
    "# 加载视频数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KwQtSOz0VrVX"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>     <a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/load_data/video\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a> </td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/video.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/load_data/video.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a>\n",
    "</td>\n",
    "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/load_data/video.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a> </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F-SqCosJ6-0H"
   },
   "source": [
    "本教程演示如何使用 [UCF101 人体动作数据集](https://en.wikipedia.org/wiki/Audio_Video_Interleave)加载和预处理 [AVI](https://tensorflow.google.cn/datasets/catalog/ucf101) 视频数据。当您对数据进行预处理后，就可以将其用于视频分类/识别、字幕或聚类等任务。原始数据集包含从 YouTube 收集的具有 101 个类别的真实动作视频，包括演奏大提琴、刷牙和化眼妆。您将学习如何：\n",
    "\n",
    "- 从 ZIP 文件加载数据。\n",
    "\n",
    "- 从视频文件中读取帧序列。\n",
    "\n",
    "- 呈现视频数据。\n",
    "\n",
    "- 封装帧生成器 [`tf.data.Dataset`](https://tensorflow.google.cn/guide/data)。\n",
    "\n",
    "本视频加载和预处理教程是 TensorFlow 视频教程系列的第一部分。下面是其他三个教程：\n",
    "\n",
    "- [构建用于视频分类的 3D CNN 模型](https://tensorflow.google.cn/tutorials/video/video_classification)：请注意，本教程使用分解 3D 数据的空间和时间方面的 (2+1)D CNN；如果使用 MRI 扫描等体数据，请考虑使用 3D CNN 而不是 (2+1)D CNN。\n",
    "- [用于流式动作识别的 MoViNet](https://tensorflow.google.cn/hub/tutorials/movinet)：熟悉 TF Hub 上提供的 MoViNet 模型。\n",
    "- [使用 MoViNet 进行视频分类的迁移学习](https://tensorflow.google.cn/tutorials/video/transfer_learning_with_movinet)：本教程介绍了如何使用预训练的视频分类模型，该模型是在具有 UCF-101 数据集的不同数据集上训练的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PnpPjKVD68eH"
   },
   "source": [
    "## 安装\n",
    "\n",
    "首先，安装和导入一些必要的库，包括：用于检查 ZIP 文件内容的 [remotezip](https://github.com/gtsystem/python-remotezip)，用于使用进度条的 [tqdm](https://github.com/tqdm/tqdm)，用于处理视频文件的 [OpenCV](https://opencv.org/)，以及用于在 Jupyter 笔记本中嵌入数据的 [`tensorflow_docs`](https://github.com/tensorflow/docs/tree/master/tools/tensorflow_docs)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:36:54.218873Z",
     "iopub.status.busy": "2023-11-07T23:36:54.218402Z",
     "iopub.status.idle": "2023-11-07T23:36:56.447637Z",
     "shell.execute_reply": "2023-11-07T23:36:56.446631Z"
    },
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    },
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     ]
    }
   ],
   "source": [
    "# The way this tutorial uses the `TimeDistributed` layer requires TF>=2.10\n",
    "!pip install -U \"tensorflow>=2.10.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:36:56.452588Z",
     "iopub.status.busy": "2023-11-07T23:36:56.451786Z",
     "iopub.status.idle": "2023-11-07T23:37:05.928385Z",
     "shell.execute_reply": "2023-11-07T23:37:05.927127Z"
    },
    "id": "P5SBasQcbwQA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting remotezip\r\n",
      "  Using cached remotezip-0.12.1-py3-none-any.whl\r\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting opencv-python\r\n",
      "  Using cached opencv_python-4.8.1.78-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\r\n",
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    },
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     "output_type": "stream",
     "text": [
      "Collecting tabulate (from remotezip)\r\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: tabulate, opencv-python, remotezip\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed opencv-python-4.8.1.78 remotezip-0.12.1 tabulate-0.9.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip install remotezip tqdm opencv-python\n",
    "!pip install -q git+https://github.com/tensorflow/docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:05.933285Z",
     "iopub.status.busy": "2023-11-07T23:37:05.932593Z",
     "iopub.status.idle": "2023-11-07T23:37:08.479243Z",
     "shell.execute_reply": "2023-11-07T23:37:08.478441Z"
    },
    "id": "9RYQIJ9C6BVH"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-07 23:37:06.494330: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-07 23:37:06.494378: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-07 23:37:06.496111: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "import random\n",
    "import pathlib\n",
    "import itertools\n",
    "import collections\n",
    "\n",
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import remotezip as rz\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# Some modules to display an animation using imageio.\n",
    "import imageio\n",
    "from IPython import display\n",
    "from urllib import request\n",
    "from tensorflow_docs.vis import embed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbhwWLLM7FXo"
   },
   "source": [
    "## 下载 UCF101 数据集的子集\n",
    "\n",
    "[UCF101 数据集](https://tensorflow.google.cn/datasets/catalog/ucf101)包含 101 类不同动作的视频，主要用于动作识别。您将在此演示中使用这些类别的一个子集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.483891Z",
     "iopub.status.busy": "2023-11-07T23:37:08.483413Z",
     "iopub.status.idle": "2023-11-07T23:37:08.487397Z",
     "shell.execute_reply": "2023-11-07T23:37:08.486696Z"
    },
    "id": "gVIgj-jIA8U8"
   },
   "outputs": [],
   "source": [
    "URL = 'https://storage.googleapis.com/thumos14_files/UCF101_videos.zip'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2tm8aBzw6Md7"
   },
   "source": [
    "上面的网址包含一个带有 UCF 101 数据集的 ZIP 文件。创建一个使用 `remotezip` 库的函数来检查该 URL 中 ZIP 文件的内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.490999Z",
     "iopub.status.busy": "2023-11-07T23:37:08.490395Z",
     "iopub.status.idle": "2023-11-07T23:37:08.494943Z",
     "shell.execute_reply": "2023-11-07T23:37:08.494203Z"
    },
    "id": "lY-x7TaZlK6O"
   },
   "outputs": [],
   "source": [
    "def list_files_from_zip_url(zip_url):\n",
    "  \"\"\" List the files in each class of the dataset given a URL with the zip file.\n",
    "\n",
    "    Args:\n",
    "      zip_url: A URL from which the files can be extracted from.\n",
    "\n",
    "    Returns:\n",
    "      List of files in each of the classes.\n",
    "  \"\"\"\n",
    "  files = []\n",
    "  with rz.RemoteZip(zip_url) as zip:\n",
    "    for zip_info in zip.infolist():\n",
    "      files.append(zip_info.filename)\n",
    "  return files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.498486Z",
     "iopub.status.busy": "2023-11-07T23:37:08.497844Z",
     "iopub.status.idle": "2023-11-07T23:37:08.936747Z",
     "shell.execute_reply": "2023-11-07T23:37:08.936050Z"
    },
    "id": "lYErXAdUr-rk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['UCF101/v_ApplyEyeMakeup_g01_c01.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g01_c02.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g01_c03.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g01_c04.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g01_c05.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g01_c06.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g02_c01.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g02_c02.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g02_c03.avi',\n",
       " 'UCF101/v_ApplyEyeMakeup_g02_c04.avi']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = list_files_from_zip_url(URL)\n",
    "files = [f for f in files if f.endswith('.avi')]\n",
    "files[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rQ4l8D9dFPS7"
   },
   "source": [
    "先从几个视频和有限数量的类开始训练。运行上述代码块后，请注意类名包含在每个视频的文件名中。\n",
    "\n",
    "定义从文件名中检索类名的 `get_class` 函数。然后，创建一个名为 `get_files_per_class` 的函数，它会将所有文件的列表（上面的 `files`）转换为列出每个类的文件的字典："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.940881Z",
     "iopub.status.busy": "2023-11-07T23:37:08.940607Z",
     "iopub.status.idle": "2023-11-07T23:37:08.944524Z",
     "shell.execute_reply": "2023-11-07T23:37:08.943852Z"
    },
    "id": "yyyivOX0sO19"
   },
   "outputs": [],
   "source": [
    "def get_class(fname):\n",
    "  \"\"\" Retrieve the name of the class given a filename.\n",
    "\n",
    "    Args:\n",
    "      fname: Name of the file in the UCF101 dataset.\n",
    "\n",
    "    Returns:\n",
    "      Class that the file belongs to.\n",
    "  \"\"\"\n",
    "  return fname.split('_')[-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.948040Z",
     "iopub.status.busy": "2023-11-07T23:37:08.947408Z",
     "iopub.status.idle": "2023-11-07T23:37:08.951631Z",
     "shell.execute_reply": "2023-11-07T23:37:08.950932Z"
    },
    "id": "1qnH0xKzlyw_"
   },
   "outputs": [],
   "source": [
    "def get_files_per_class(files):\n",
    "  \"\"\" Retrieve the files that belong to each class.\n",
    "\n",
    "    Args:\n",
    "      files: List of files in the dataset.\n",
    "\n",
    "    Returns:\n",
    "      Dictionary of class names (key) and files (values). \n",
    "  \"\"\"\n",
    "  files_for_class = collections.defaultdict(list)\n",
    "  for fname in files:\n",
    "    class_name = get_class(fname)\n",
    "    files_for_class[class_name].append(fname)\n",
    "  return files_for_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VxSt5YgSGrWn"
   },
   "source": [
    "获得每个类的文件列表后，您可以选择要使用多少个类，以及每个类需要多少视频，以创建数据集。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.955114Z",
     "iopub.status.busy": "2023-11-07T23:37:08.954564Z",
     "iopub.status.idle": "2023-11-07T23:37:08.957742Z",
     "shell.execute_reply": "2023-11-07T23:37:08.957121Z"
    },
    "id": "qPdURg74uUTk"
   },
   "outputs": [],
   "source": [
    "NUM_CLASSES = 10\n",
    "FILES_PER_CLASS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.960872Z",
     "iopub.status.busy": "2023-11-07T23:37:08.960411Z",
     "iopub.status.idle": "2023-11-07T23:37:08.968798Z",
     "shell.execute_reply": "2023-11-07T23:37:08.968171Z"
    },
    "id": "GUs0xtXsr9i3"
   },
   "outputs": [],
   "source": [
    "files_for_class = get_files_per_class(files)\n",
    "classes = list(files_for_class.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.972023Z",
     "iopub.status.busy": "2023-11-07T23:37:08.971485Z",
     "iopub.status.idle": "2023-11-07T23:37:08.975448Z",
     "shell.execute_reply": "2023-11-07T23:37:08.974819Z"
    },
    "id": "-YqFARvqwon9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num classes: 101\n",
      "Num videos for class[0]: 145\n"
     ]
    }
   ],
   "source": [
    "print('Num classes:', len(classes))\n",
    "print('Num videos for class[0]:', len(files_for_class[classes[0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yFAFqKqE92bQ"
   },
   "source": [
    "创建一个名为 `select_subset_of_classes` 的新函数，它会选择数据集中存在的类的子集并在每个类中选择特定数量的文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.978886Z",
     "iopub.status.busy": "2023-11-07T23:37:08.978250Z",
     "iopub.status.idle": "2023-11-07T23:37:08.982368Z",
     "shell.execute_reply": "2023-11-07T23:37:08.981726Z"
    },
    "id": "O3jek4QimIj-"
   },
   "outputs": [],
   "source": [
    "def select_subset_of_classes(files_for_class, classes, files_per_class):\n",
    "  \"\"\" Create a dictionary with the class name and a subset of the files in that class.\n",
    "\n",
    "    Args:\n",
    "      files_for_class: Dictionary of class names (key) and files (values).\n",
    "      classes: List of classes.\n",
    "      files_per_class: Number of files per class of interest.\n",
    "\n",
    "    Returns:\n",
    "      Dictionary with class as key and list of specified number of video files in that class.\n",
    "  \"\"\"\n",
    "  files_subset = dict()\n",
    "\n",
    "  for class_name in classes:\n",
    "    class_files = files_for_class[class_name]\n",
    "    files_subset[class_name] = class_files[:files_per_class]\n",
    "\n",
    "  return files_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.985743Z",
     "iopub.status.busy": "2023-11-07T23:37:08.985247Z",
     "iopub.status.idle": "2023-11-07T23:37:08.990012Z",
     "shell.execute_reply": "2023-11-07T23:37:08.989414Z"
    },
    "id": "5cjcz6Gpcb-W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ApplyEyeMakeup',\n",
       " 'ApplyLipstick',\n",
       " 'Archery',\n",
       " 'BabyCrawling',\n",
       " 'BalanceBeam',\n",
       " 'BandMarching',\n",
       " 'BaseballPitch',\n",
       " 'BasketballDunk',\n",
       " 'Basketball',\n",
       " 'BenchPress']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_subset = select_subset_of_classes(files_for_class, classes[:NUM_CLASSES], FILES_PER_CLASS)\n",
    "list(files_subset.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ALrlDS1lZx3E"
   },
   "source": [
    "定义将视频拆分为训练集、验证集和测试集的辅助函数。视频从带有 ZIP 文件的网址下载，并放置在各自的子目录中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:08.993551Z",
     "iopub.status.busy": "2023-11-07T23:37:08.993001Z",
     "iopub.status.idle": "2023-11-07T23:37:08.997488Z",
     "shell.execute_reply": "2023-11-07T23:37:08.996911Z"
    },
    "id": "AH9sWS_6nRz3"
   },
   "outputs": [],
   "source": [
    "def download_from_zip(zip_url, to_dir, file_names):\n",
    "  \"\"\" Download the contents of the zip file from the zip URL.\n",
    "\n",
    "    Args:\n",
    "      zip_url: A URL with a zip file containing data.\n",
    "      to_dir: A directory to download data to.\n",
    "      file_names: Names of files to download.\n",
    "  \"\"\"\n",
    "  with rz.RemoteZip(zip_url) as zip:\n",
    "    for fn in tqdm.tqdm(file_names):\n",
    "      class_name = get_class(fn)\n",
    "      zip.extract(fn, str(to_dir / class_name))\n",
    "      unzipped_file = to_dir / class_name / fn\n",
    "\n",
    "      fn = pathlib.Path(fn).parts[-1]\n",
    "      output_file = to_dir / class_name / fn\n",
    "      unzipped_file.rename(output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pejRTChA6mrp"
   },
   "source": [
    "以下函数会返回尚未放入数据子集的剩余数据。它允许您将剩余的数据放在下一个指定的数据子集中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:09.001016Z",
     "iopub.status.busy": "2023-11-07T23:37:09.000514Z",
     "iopub.status.idle": "2023-11-07T23:37:09.004807Z",
     "shell.execute_reply": "2023-11-07T23:37:09.004113Z"
    },
    "id": "6ARYc-WLqqNF"
   },
   "outputs": [],
   "source": [
    "def split_class_lists(files_for_class, count):\n",
    "  \"\"\" Returns the list of files belonging to a subset of data as well as the remainder of\n",
    "    files that need to be downloaded.\n",
    "    \n",
    "    Args:\n",
    "      files_for_class: Files belonging to a particular class of data.\n",
    "      count: Number of files to download.\n",
    "\n",
    "    Returns:\n",
    "      Files belonging to the subset of data and dictionary of the remainder of files that need to be downloaded.\n",
    "  \"\"\"\n",
    "  split_files = []\n",
    "  remainder = {}\n",
    "  for cls in files_for_class:\n",
    "    split_files.extend(files_for_class[cls][:count])\n",
    "    remainder[cls] = files_for_class[cls][count:]\n",
    "  return split_files, remainder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LlEQ_I0TLd1X"
   },
   "source": [
    "下面的 `download_ufc_101_subset` 函数允许您下载 UCF101 数据集的子集并将其拆分为训练集、验证集和测试集。您可以指定要使用的类的数量。`splits` 参数允许您传入一个字典，其中键值是子集的名称（例如：“train”）和您希望每个类拥有的视频数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:09.008390Z",
     "iopub.status.busy": "2023-11-07T23:37:09.007662Z",
     "iopub.status.idle": "2023-11-07T23:37:09.014597Z",
     "shell.execute_reply": "2023-11-07T23:37:09.013973Z"
    },
    "id": "IHH2Y1M06xoz"
   },
   "outputs": [],
   "source": [
    "def download_ucf_101_subset(zip_url, num_classes, splits, download_dir):\n",
    "  \"\"\" Download a subset of the UCF101 dataset and split them into various parts, such as\n",
    "    training, validation, and test.\n",
    "\n",
    "    Args:\n",
    "      zip_url: A URL with a ZIP file with the data.\n",
    "      num_classes: Number of labels.\n",
    "      splits: Dictionary specifying the training, validation, test, etc. (key) division of data \n",
    "              (value is number of files per split).\n",
    "      download_dir: Directory to download data to.\n",
    "\n",
    "    Return:\n",
    "      Mapping of the directories containing the subsections of data.\n",
    "  \"\"\"\n",
    "  files = list_files_from_zip_url(zip_url)\n",
    "  for f in files:\n",
    "    path = os.path.normpath(f)\n",
    "    tokens = path.split(os.sep)\n",
    "    if len(tokens) <= 2:\n",
    "      files.remove(f) # Remove that item from the list if it does not have a filename\n",
    "  \n",
    "  files_for_class = get_files_per_class(files)\n",
    "\n",
    "  classes = list(files_for_class.keys())[:num_classes]\n",
    "\n",
    "  for cls in classes:\n",
    "    random.shuffle(files_for_class[cls])\n",
    "    \n",
    "  # Only use the number of classes you want in the dictionary\n",
    "  files_for_class = {x: files_for_class[x] for x in classes}\n",
    "\n",
    "  dirs = {}\n",
    "  for split_name, split_count in splits.items():\n",
    "    print(split_name, \":\")\n",
    "    split_dir = download_dir / split_name\n",
    "    split_files, files_for_class = split_class_lists(files_for_class, split_count)\n",
    "    download_from_zip(zip_url, split_dir, split_files)\n",
    "    dirs[split_name] = split_dir\n",
    "\n",
    "  return dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:37:09.017951Z",
     "iopub.status.busy": "2023-11-07T23:37:09.017394Z",
     "iopub.status.idle": "2023-11-07T23:38:10.084080Z",
     "shell.execute_reply": "2023-11-07T23:38:10.083351Z"
    },
    "id": "NuD-xU8Q66Vm"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train :\n"
     ]
    },
    {
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      "test :\n"
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    },
    {
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    },
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    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "download_dir = pathlib.Path('./UCF101_subset/')\n",
    "subset_paths = download_ucf_101_subset(URL,\n",
    "                                       num_classes = NUM_CLASSES,\n",
    "                                       splits = {\"train\": 30, \"val\": 10, \"test\": 10},\n",
    "                                       download_dir = download_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MBMRm9Ub3Zrk"
   },
   "source": [
    "下载数据后，您现在应该拥有了一个 UCF101 数据集子集的副本。运行以下代码即可打印您在所有数据子集中拥有的视频总数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:10.088152Z",
     "iopub.status.busy": "2023-11-07T23:38:10.087456Z",
     "iopub.status.idle": "2023-11-07T23:38:10.094960Z",
     "shell.execute_reply": "2023-11-07T23:38:10.094349Z"
    },
    "id": "zupvOLYP4D4q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total videos: 500\n"
     ]
    }
   ],
   "source": [
    "video_count_train = len(list(download_dir.glob('train/*/*.avi')))\n",
    "video_count_val = len(list(download_dir.glob('val/*/*.avi')))\n",
    "video_count_test = len(list(download_dir.glob('test/*/*.avi')))\n",
    "video_total = video_count_train + video_count_val + video_count_test\n",
    "print(f\"Total videos: {video_total}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JmJG1SlXiOX8"
   },
   "source": [
    "您现在还可以预览数据文件的目录。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:10.098506Z",
     "iopub.status.busy": "2023-11-07T23:38:10.097877Z",
     "iopub.status.idle": "2023-11-07T23:38:10.232872Z",
     "shell.execute_reply": "2023-11-07T23:38:10.231758Z"
    },
    "id": "y9be0WlDiNM0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./UCF101_subset\r\n",
      "./UCF101_subset/val\r\n",
      "./UCF101_subset/val/BenchPress\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g06_c05.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g11_c01.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g06_c01.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g02_c07.avi\r\n",
      "./UCF101_subset/val/BenchPress/UCF101\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g08_c01.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g14_c04.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g07_c04.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g08_c07.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g03_c06.avi\r\n",
      "./UCF101_subset/val/BenchPress/v_BenchPress_g24_c05.avi\r\n",
      "./UCF101_subset/val/BabyCrawling\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g16_c05.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g15_c01.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g19_c04.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g13_c05.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g10_c04.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g05_c01.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g24_c03.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g15_c05.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/UCF101\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g14_c01.avi\r\n",
      "./UCF101_subset/val/BabyCrawling/v_BabyCrawling_g02_c03.avi\r\n",
      "./UCF101_subset/val/BasketballDunk\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g16_c06.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g14_c02.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g07_c01.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/UCF101\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g19_c03.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g17_c06.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g13_c04.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g01_c06.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g13_c02.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g03_c01.avi\r\n",
      "./UCF101_subset/val/BasketballDunk/v_BasketballDunk_g08_c01.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g21_c04.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g15_c02.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g22_c03.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g25_c07.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c01.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g23_c06.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/UCF101\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g04_c01.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c03.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g20_c06.avi\r\n",
      "./UCF101_subset/val/ApplyEyeMakeup/v_ApplyEyeMakeup_g13_c05.avi\r\n",
      "./UCF101_subset/val/Basketball\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g21_c01.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g15_c03.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g16_c02.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g13_c02.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g18_c04.avi\r\n",
      "./UCF101_subset/val/Basketball/UCF101\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g09_c02.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g24_c02.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g19_c05.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g25_c04.avi\r\n",
      "./UCF101_subset/val/Basketball/v_Basketball_g08_c02.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g22_c02.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g21_c01.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g01_c02.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g07_c02.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g02_c03.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g05_c02.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/UCF101\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g22_c06.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g18_c02.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g23_c03.avi\r\n",
      "./UCF101_subset/val/ApplyLipstick/v_ApplyLipstick_g19_c02.avi\r\n",
      "./UCF101_subset/val/BalanceBeam\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g02_c01.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g09_c04.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g15_c03.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g01_c01.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g24_c03.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/UCF101\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g16_c03.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g19_c03.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g06_c01.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g11_c04.avi\r\n",
      "./UCF101_subset/val/BalanceBeam/v_BalanceBeam_g06_c05.avi\r\n",
      "./UCF101_subset/val/BaseballPitch\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g23_c05.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g02_c04.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g03_c06.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g06_c03.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g07_c04.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/UCF101\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g13_c01.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g17_c03.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g07_c06.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g15_c06.avi\r\n",
      "./UCF101_subset/val/BaseballPitch/v_BaseballPitch_g06_c05.avi\r\n",
      "./UCF101_subset/val/BandMarching\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g12_c04.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g12_c06.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g19_c01.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g22_c04.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g14_c03.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g11_c05.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g10_c02.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g18_c05.avi\r\n",
      "./UCF101_subset/val/BandMarching/UCF101\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g15_c01.avi\r\n",
      "./UCF101_subset/val/BandMarching/v_BandMarching_g09_c07.avi\r\n",
      "./UCF101_subset/val/Archery\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g02_c07.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g15_c06.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g17_c02.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g04_c03.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g06_c03.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g09_c06.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g11_c04.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g22_c02.avi\r\n",
      "./UCF101_subset/val/Archery/UCF101\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g25_c02.avi\r\n",
      "./UCF101_subset/val/Archery/v_Archery_g13_c01.avi\r\n",
      "./UCF101_subset/test\r\n",
      "./UCF101_subset/test/BenchPress\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g13_c03.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g17_c04.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g04_c01.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g19_c02.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g15_c01.avi\r\n",
      "./UCF101_subset/test/BenchPress/UCF101\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g15_c03.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g06_c07.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g06_c03.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g22_c01.avi\r\n",
      "./UCF101_subset/test/BenchPress/v_BenchPress_g19_c06.avi\r\n",
      "./UCF101_subset/test/BabyCrawling\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g14_c03.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g24_c01.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g08_c04.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g18_c06.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g20_c05.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g01_c03.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/UCF101\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g25_c05.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g17_c05.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g06_c02.avi\r\n",
      "./UCF101_subset/test/BabyCrawling/v_BabyCrawling_g23_c01.avi\r\n",
      "./UCF101_subset/test/BasketballDunk\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g06_c03.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g22_c01.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g10_c03.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g04_c01.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g05_c03.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g07_c03.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g07_c05.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/UCF101\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g18_c03.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g23_c01.avi\r\n",
      "./UCF101_subset/test/BasketballDunk/v_BasketballDunk_g24_c02.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g05_c02.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g11_c02.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g12_c01.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g15_c04.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c05.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g15_c06.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/UCF101\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c04.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g24_c04.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g17_c04.avi\r\n",
      "./UCF101_subset/test/ApplyEyeMakeup/v_ApplyEyeMakeup_g16_c01.avi\r\n",
      "./UCF101_subset/test/Basketball\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g25_c06.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g09_c04.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g06_c04.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g19_c01.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g05_c04.avi\r\n",
      "./UCF101_subset/test/Basketball/UCF101\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g01_c05.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g01_c01.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g04_c02.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g18_c02.avi\r\n",
      "./UCF101_subset/test/Basketball/v_Basketball_g23_c06.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g21_c05.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g12_c04.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/UCF101\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g15_c01.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g08_c02.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g07_c04.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g01_c04.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g06_c01.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g25_c04.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g11_c04.avi\r\n",
      "./UCF101_subset/test/ApplyLipstick/v_ApplyLipstick_g09_c04.avi\r\n",
      "./UCF101_subset/test/BalanceBeam\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g18_c03.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g21_c05.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g23_c04.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g07_c02.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g10_c04.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g17_c03.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/UCF101\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g22_c04.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g18_c01.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g13_c04.avi\r\n",
      "./UCF101_subset/test/BalanceBeam/v_BalanceBeam_g08_c02.avi\r\n",
      "./UCF101_subset/test/BaseballPitch\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g25_c03.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g09_c06.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g24_c06.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g18_c02.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/UCF101\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g06_c01.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g17_c05.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g25_c05.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g20_c02.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g18_c06.avi\r\n",
      "./UCF101_subset/test/BaseballPitch/v_BaseballPitch_g04_c01.avi\r\n",
      "./UCF101_subset/test/BandMarching\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g02_c02.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g03_c07.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g06_c03.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g05_c02.avi\r\n",
      "./UCF101_subset/test/BandMarching/UCF101\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g23_c01.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g01_c07.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g24_c05.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g08_c02.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g10_c04.avi\r\n",
      "./UCF101_subset/test/BandMarching/v_BandMarching_g09_c05.avi\r\n",
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      "./UCF101_subset/train/BalanceBeam/v_BalanceBeam_g25_c03.avi\r\n",
      "./UCF101_subset/train/BalanceBeam/v_BalanceBeam_g22_c02.avi\r\n",
      "./UCF101_subset/train/BalanceBeam/v_BalanceBeam_g21_c01.avi\r\n",
      "./UCF101_subset/train/BalanceBeam/v_BalanceBeam_g17_c05.avi\r\n",
      "./UCF101_subset/train/BalanceBeam/v_BalanceBeam_g12_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g04_c03.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g10_c05.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g19_c01.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g14_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g19_c05.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g06_c07.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g13_c03.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g16_c03.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g08_c01.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g22_c02.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g13_c07.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g25_c01.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/UCF101\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g17_c01.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g09_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g05_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g16_c05.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g24_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g19_c03.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g01_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g16_c01.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g24_c02.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g10_c01.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g21_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g01_c02.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g17_c07.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g23_c07.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g18_c04.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g10_c03.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g08_c03.avi\r\n",
      "./UCF101_subset/train/BaseballPitch/v_BaseballPitch_g15_c02.avi\r\n",
      "./UCF101_subset/train/BandMarching\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g21_c06.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g04_c02.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g20_c02.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g20_c06.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g16_c04.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g17_c01.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g07_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g03_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g24_c01.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g20_c04.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g19_c05.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g15_c07.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g06_c01.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g18_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g01_c05.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g23_c05.avi\r\n",
      "./UCF101_subset/train/BandMarching/UCF101\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g25_c05.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g21_c04.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g01_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g19_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g07_c01.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g25_c01.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g05_c04.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g14_c05.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g24_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g09_c03.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g19_c07.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g04_c04.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g18_c01.avi\r\n",
      "./UCF101_subset/train/BandMarching/v_BandMarching_g08_c06.avi\r\n",
      "./UCF101_subset/train/Archery\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g16_c03.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g14_c02.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g08_c05.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g06_c05.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g05_c04.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g18_c06.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g02_c05.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g10_c03.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g22_c04.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g13_c05.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g01_c04.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g20_c01.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g05_c02.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g23_c05.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g21_c02.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g03_c04.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g23_c07.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g07_c05.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g10_c01.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g20_c05.avi\r\n",
      "./UCF101_subset/train/Archery/UCF101\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g04_c01.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g02_c03.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g16_c01.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g13_c07.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g13_c03.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g09_c02.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g11_c06.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g07_c03.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g02_c01.avi\r\n",
      "./UCF101_subset/train/Archery/v_Archery_g10_c05.avi\r\n"
     ]
    }
   ],
   "source": [
    "!find ./UCF101_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4uslY4dScyu"
   },
   "source": [
    "## 从每个视频文件创建帧"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D1vvyT0F7JAZ"
   },
   "source": [
    "`frames_from_video_file` 函数会将视频拆分为帧，从视频文件中读取随机选择的 `n_frames` 跨度，并将它们作为 NumPy `array` 返回。要减少内存和计算开销，请选择<strong>少量</strong>帧。此外，请从每个视频中选取**相同**数量的帧，这样可以更轻松地处理批量数据。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:10.237753Z",
     "iopub.status.busy": "2023-11-07T23:38:10.237012Z",
     "iopub.status.idle": "2023-11-07T23:38:10.241737Z",
     "shell.execute_reply": "2023-11-07T23:38:10.241145Z"
    },
    "id": "vNBCiV3bMzpD"
   },
   "outputs": [],
   "source": [
    "def format_frames(frame, output_size):\n",
    "  \"\"\"\n",
    "    Pad and resize an image from a video.\n",
    "    \n",
    "    Args:\n",
    "      frame: Image that needs to resized and padded. \n",
    "      output_size: Pixel size of the output frame image.\n",
    "\n",
    "    Return:\n",
    "      Formatted frame with padding of specified output size.\n",
    "  \"\"\"\n",
    "  frame = tf.image.convert_image_dtype(frame, tf.float32)\n",
    "  frame = tf.image.resize_with_pad(frame, *output_size)\n",
    "  return frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:10.244919Z",
     "iopub.status.busy": "2023-11-07T23:38:10.244405Z",
     "iopub.status.idle": "2023-11-07T23:38:10.251123Z",
     "shell.execute_reply": "2023-11-07T23:38:10.250548Z"
    },
    "id": "9ujLDC9G7JyE"
   },
   "outputs": [],
   "source": [
    "def frames_from_video_file(video_path, n_frames, output_size = (224,224), frame_step = 15):\n",
    "  \"\"\"\n",
    "    Creates frames from each video file present for each category.\n",
    "\n",
    "    Args:\n",
    "      video_path: File path to the video.\n",
    "      n_frames: Number of frames to be created per video file.\n",
    "      output_size: Pixel size of the output frame image.\n",
    "\n",
    "    Return:\n",
    "      An NumPy array of frames in the shape of (n_frames, height, width, channels).\n",
    "  \"\"\"\n",
    "  # Read each video frame by frame\n",
    "  result = []\n",
    "  src = cv2.VideoCapture(str(video_path))  \n",
    "\n",
    "  video_length = src.get(cv2.CAP_PROP_FRAME_COUNT)\n",
    "\n",
    "  need_length = 1 + (n_frames - 1) * frame_step\n",
    "\n",
    "  if need_length > video_length:\n",
    "    start = 0\n",
    "  else:\n",
    "    max_start = video_length - need_length\n",
    "    start = random.randint(0, max_start + 1)\n",
    "\n",
    "  src.set(cv2.CAP_PROP_POS_FRAMES, start)\n",
    "  # ret is a boolean indicating whether read was successful, frame is the image itself\n",
    "  ret, frame = src.read()\n",
    "  result.append(format_frames(frame, output_size))\n",
    "\n",
    "  for _ in range(n_frames - 1):\n",
    "    for _ in range(frame_step):\n",
    "      ret, frame = src.read()\n",
    "    if ret:\n",
    "      frame = format_frames(frame, output_size)\n",
    "      result.append(frame)\n",
    "    else:\n",
    "      result.append(np.zeros_like(result[0]))\n",
    "  src.release()\n",
    "  result = np.array(result)[..., [2, 1, 0]]\n",
    "\n",
    "  return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ENtlwhxwyTe"
   },
   "source": [
    "## 呈现视频数据\n",
    "\n",
    "`frames_from_video_file` 函数会将一组帧作为 NumPy 数组返回。尝试在 Patrick Gillett 的 [Wikimedia](https://commons.wikimedia.org/wiki/Category:Videos_of_sports){:.external} 的新视频中使用此函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:10.254355Z",
     "iopub.status.busy": "2023-11-07T23:38:10.253866Z",
     "iopub.status.idle": "2023-11-07T23:38:12.552176Z",
     "shell.execute_reply": "2023-11-07T23:38:12.551125Z"
    },
    "id": "Z2hgSghlykzA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\r\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\r\n",
      "\r",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 38 55.0M   38 21.4M    0     0  21.4M      0  0:00:02 --:--:--  0:00:02 21.4M"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 91 55.0M   91 50.5M    0     0  25.2M      0  0:00:02  0:00:02 --:--:-- 25.2M"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "100 55.0M  100 55.0M    0     0  25.5M      0  0:00:02  0:00:02 --:--:-- 25.5M\r\n"
     ]
    }
   ],
   "source": [
    "!curl -O https://upload.wikimedia.org/wikipedia/commons/8/86/End_of_a_jam.ogv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:12.556994Z",
     "iopub.status.busy": "2023-11-07T23:38:12.556258Z",
     "iopub.status.idle": "2023-11-07T23:38:12.560369Z",
     "shell.execute_reply": "2023-11-07T23:38:12.559693Z"
    },
    "id": "xdHvHw3hym-U"
   },
   "outputs": [],
   "source": [
    "video_path = \"End_of_a_jam.ogv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:12.563544Z",
     "iopub.status.busy": "2023-11-07T23:38:12.563028Z",
     "iopub.status.idle": "2023-11-07T23:38:15.826918Z",
     "shell.execute_reply": "2023-11-07T23:38:15.826210Z"
    },
    "id": "u845YODXyqo5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 224, 224, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_video = frames_from_video_file(video_path, n_frames = 10)\n",
    "sample_video.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:15.831084Z",
     "iopub.status.busy": "2023-11-07T23:38:15.830533Z",
     "iopub.status.idle": "2023-11-07T23:38:15.834839Z",
     "shell.execute_reply": "2023-11-07T23:38:15.834231Z"
    },
    "id": "zFHGHiFgGjv2"
   },
   "outputs": [],
   "source": [
    "def to_gif(images):\n",
    "  converted_images = np.clip(images * 255, 0, 255).astype(np.uint8)\n",
    "  imageio.mimsave('./animation.gif', converted_images, fps=10)\n",
    "  return embed.embed_file('./animation.gif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:15.838603Z",
     "iopub.status.busy": "2023-11-07T23:38:15.837958Z",
     "iopub.status.idle": "2023-11-07T23:38:16.185223Z",
     "shell.execute_reply": "2023-11-07T23:38:16.184559Z"
    },
    "id": "7hiwUJenEN3p"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_gif(sample_video)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3dktTnDVG7xf"
   },
   "source": [
    "除了查看此视频外，您还可以显示 UCF-101 数据。为此，请运行以下代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MghJzJsWme0t"
   },
   "outputs": [],
   "source": [
    "# docs-infra: no-execute\n",
    "ucf_sample_video = frames_from_video_file(next(subset_paths['train'].glob('*/*.avi')), 50)\n",
    "to_gif(ucf_sample_video)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NlvuC5_E7XrF"
   },
   "source": [
    "接下来，定义 `FrameGenerator` 类以创建一个可迭代对象，该对象可以将数据输入 TensorFlow 数据流水线。生成器 (`__call__`) 函数产生由 `frames_from_video_file` 生成的帧数组以及与帧集相关联的标签的独热编码向量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:16.191625Z",
     "iopub.status.busy": "2023-11-07T23:38:16.190968Z",
     "iopub.status.idle": "2023-11-07T23:38:16.197597Z",
     "shell.execute_reply": "2023-11-07T23:38:16.196989Z"
    },
    "id": "MVmfLTlw7Ues"
   },
   "outputs": [],
   "source": [
    "class FrameGenerator:\n",
    "  def __init__(self, path, n_frames, training = False):\n",
    "    \"\"\" Returns a set of frames with their associated label. \n",
    "\n",
    "      Args:\n",
    "        path: Video file paths.\n",
    "        n_frames: Number of frames. \n",
    "        training: Boolean to determine if training dataset is being created.\n",
    "    \"\"\"\n",
    "    self.path = path\n",
    "    self.n_frames = n_frames\n",
    "    self.training = training\n",
    "    self.class_names = sorted(set(p.name for p in self.path.iterdir() if p.is_dir()))\n",
    "    self.class_ids_for_name = dict((name, idx) for idx, name in enumerate(self.class_names))\n",
    "\n",
    "  def get_files_and_class_names(self):\n",
    "    video_paths = list(self.path.glob('*/*.avi'))\n",
    "    classes = [p.parent.name for p in video_paths] \n",
    "    return video_paths, classes\n",
    "\n",
    "  def __call__(self):\n",
    "    video_paths, classes = self.get_files_and_class_names()\n",
    "\n",
    "    pairs = list(zip(video_paths, classes))\n",
    "\n",
    "    if self.training:\n",
    "      random.shuffle(pairs)\n",
    "\n",
    "    for path, name in pairs:\n",
    "      video_frames = frames_from_video_file(path, self.n_frames) \n",
    "      label = self.class_ids_for_name[name] # Encode labels\n",
    "      yield video_frames, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xsvhPIkpzx-r"
   },
   "source": [
    "在将 `FrameGenerator` 对象封装为 TensorFlow Dataset 对象之前对其进行测试。此外，对于训练数据集，请确保启用训练模式，以便对数据进行重排。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:16.201318Z",
     "iopub.status.busy": "2023-11-07T23:38:16.200705Z",
     "iopub.status.idle": "2023-11-07T23:38:16.335372Z",
     "shell.execute_reply": "2023-11-07T23:38:16.334632Z"
    },
    "id": "P5jwagZxzxOf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (10, 224, 224, 3)\n",
      "Label: 3\n"
     ]
    }
   ],
   "source": [
    "fg = FrameGenerator(subset_paths['train'], 10, training=True)\n",
    "\n",
    "frames, label = next(fg())\n",
    "\n",
    "print(f\"Shape: {frames.shape}\")\n",
    "print(f\"Label: {label}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E7MRRFSks7l1"
   },
   "source": [
    "最后，创建一个 TensorFlow 数据输入流水线。您从生成器对象创建的此流水线允许您将数据输入深度学习模型。在此视频流水线中，每个元素都是一组单独的帧及其关联标签。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:16.339352Z",
     "iopub.status.busy": "2023-11-07T23:38:16.338760Z",
     "iopub.status.idle": "2023-11-07T23:38:16.372911Z",
     "shell.execute_reply": "2023-11-07T23:38:16.372277Z"
    },
    "id": "HM4NboJr7ck4"
   },
   "outputs": [],
   "source": [
    "# Create the training set\n",
    "output_signature = (tf.TensorSpec(shape = (None, None, None, 3), dtype = tf.float32),\n",
    "                    tf.TensorSpec(shape = (), dtype = tf.int16))\n",
    "train_ds = tf.data.Dataset.from_generator(FrameGenerator(subset_paths['train'], 10, training=True),\n",
    "                                          output_signature = output_signature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9oF_8m8IZvcY"
   },
   "source": [
    "检查标签是否重排。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:16.376816Z",
     "iopub.status.busy": "2023-11-07T23:38:16.376231Z",
     "iopub.status.idle": "2023-11-07T23:38:17.506484Z",
     "shell.execute_reply": "2023-11-07T23:38:17.505732Z"
    },
    "id": "3XYVmsgiZsJD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int16)\n",
      "tf.Tensor(1, shape=(), dtype=int16)\n",
      "tf.Tensor(9, shape=(), dtype=int16)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(9, shape=(), dtype=int16)\n",
      "tf.Tensor(0, shape=(), dtype=int16)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(0, shape=(), dtype=int16)\n",
      "tf.Tensor(5, shape=(), dtype=int16)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(4, shape=(), dtype=int16)\n",
      "tf.Tensor(9, shape=(), dtype=int16)\n",
      "tf.Tensor(2, shape=(), dtype=int16)\n"
     ]
    }
   ],
   "source": [
    "for frames, labels in train_ds.take(10):\n",
    "  print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:17.510515Z",
     "iopub.status.busy": "2023-11-07T23:38:17.509847Z",
     "iopub.status.idle": "2023-11-07T23:38:17.538019Z",
     "shell.execute_reply": "2023-11-07T23:38:17.537358Z"
    },
    "id": "Pi8-WkOkEXw5"
   },
   "outputs": [],
   "source": [
    "# Create the validation set\n",
    "val_ds = tf.data.Dataset.from_generator(FrameGenerator(subset_paths['val'], 10),\n",
    "                                        output_signature = output_signature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:17.541630Z",
     "iopub.status.busy": "2023-11-07T23:38:17.541363Z",
     "iopub.status.idle": "2023-11-07T23:38:18.037436Z",
     "shell.execute_reply": "2023-11-07T23:38:18.036637Z"
    },
    "id": "V6qXc-6i7eyK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set of frames: (10, 224, 224, 3)\n",
      "Shape of training labels: ()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of validation set of frames: (10, 224, 224, 3)\n",
      "Shape of validation labels: ()\n"
     ]
    }
   ],
   "source": [
    "# Print the shapes of the data\n",
    "train_frames, train_labels = next(iter(train_ds))\n",
    "print(f'Shape of training set of frames: {train_frames.shape}')\n",
    "print(f'Shape of training labels: {train_labels.shape}')\n",
    "\n",
    "val_frames, val_labels = next(iter(val_ds))\n",
    "print(f'Shape of validation set of frames: {val_frames.shape}')\n",
    "print(f'Shape of validation labels: {val_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bIrFpUIxvTLe"
   },
   "source": [
    "## 配置数据集以提高性能\n",
    "\n",
    "使用缓冲预提取，以便从磁盘产生数据，而不会阻塞 I/O。下面是可以在加载数据时使用的两个重要函数：\n",
    "\n",
    "- `Dataset.cache`：在第一个周期期间从磁盘加载图像后，它会将这些图像保留在内存中。该函数确保在训练模型时数据集不会成为瓶颈。如果数据集太大无法装入内存，您也可以使用此方法创建高性能的磁盘缓存。\n",
    "\n",
    "- `Dataset.prefetch`：在训练时重叠数据预处理和模型执行。有关详细信息，请参阅[使用 `tf.data` 提升性能](https://tensorflow.google.cn/guide/data_performance)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:18.041346Z",
     "iopub.status.busy": "2023-11-07T23:38:18.040798Z",
     "iopub.status.idle": "2023-11-07T23:38:18.056490Z",
     "shell.execute_reply": "2023-11-07T23:38:18.055800Z"
    },
    "id": "QSxjFtxAvY3_"
   },
   "outputs": [],
   "source": [
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "\n",
    "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size = AUTOTUNE)\n",
    "val_ds = val_ds.cache().shuffle(1000).prefetch(buffer_size = AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VaY-hyr-Fbfr"
   },
   "source": [
    "要准备馈送到模型的数据，请使用批处理，如下所示。请注意，在处理视频数据（例如 AVI 文件）时，数据应形成五维对象。这些维度如下：`[batch_size, number_of_frames, height, width, channels]`。相比之下，图像将具有四个维度：`[batch_size, height, width, channels]`。下图说明了如何表示视频数据的形状。\n",
    "\n",
    "![视频数据形状](https://tensorflow.google.cn/images/tutorials/video/video_data_shape.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:18.059837Z",
     "iopub.status.busy": "2023-11-07T23:38:18.059583Z",
     "iopub.status.idle": "2023-11-07T23:38:58.087845Z",
     "shell.execute_reply": "2023-11-07T23:38:58.087041Z"
    },
    "id": "pp2Qc6XSFmeB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set of frames: (2, 10, 224, 224, 3)\n",
      "Shape of training labels: (2,)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of validation set of frames: (2, 10, 224, 224, 3)\n",
      "Shape of validation labels: (2,)\n"
     ]
    }
   ],
   "source": [
    "train_ds = train_ds.batch(2)\n",
    "val_ds = val_ds.batch(2)\n",
    "\n",
    "train_frames, train_labels = next(iter(train_ds))\n",
    "print(f'Shape of training set of frames: {train_frames.shape}')\n",
    "print(f'Shape of training labels: {train_labels.shape}')\n",
    "\n",
    "val_frames, val_labels = next(iter(val_ds))\n",
    "print(f'Shape of validation set of frames: {val_frames.shape}')\n",
    "print(f'Shape of validation labels: {val_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hqjXn1FgsMqZ"
   },
   "source": [
    "## 后续步骤\n",
    "\n",
    "现在，您已经创建了带有标签的视频帧的 TensorFlow `Dataset`，您可以将其与深度学习模型一起使用。以下使用预训练的 [EfficientNet](https://arxiv.org/abs/1905.11946){:.external} 的分类模型可在几分钟内训练到较高准确率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-07T23:38:58.091919Z",
     "iopub.status.busy": "2023-11-07T23:38:58.091262Z",
     "iopub.status.idle": "2023-11-07T23:40:34.187647Z",
     "shell.execute_reply": "2023-11-07T23:40:34.186872Z"
    },
    "id": "qzqgPBUuForj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "    8192/16705208 [..............................] - 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5480448/16705208 [========>.....................] - 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16705208/16705208 [==============================] - 0s 0us/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-07 23:39:10.086724: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential/time_distributed/efficientnetb0/block2b_drop/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "      1/Unknown - 11s 11s/step - loss: 2.5740 - accuracy: 0.0000e+00"
     ]
    },
    {
     "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\r",
      "      2/Unknown - 11s 53ms/step - loss: 2.5284 - accuracy: 0.0000e+00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1699400352.000239  555168 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "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\r",
      "      3/Unknown - 12s 52ms/step - loss: 2.3643 - accuracy: 0.1667    "
     ]
    },
    {
     "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\r",
      "      4/Unknown - 12s 51ms/step - loss: 2.3322 - accuracy: 0.1250"
     ]
    },
    {
     "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\r",
      "      5/Unknown - 12s 51ms/step - loss: 2.3414 - accuracy: 0.2000"
     ]
    },
    {
     "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\r",
      "      6/Unknown - 12s 51ms/step - loss: 2.3178 - accuracy: 0.1667"
     ]
    },
    {
     "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\r",
      "      7/Unknown - 12s 51ms/step - loss: 2.2400 - accuracy: 0.2143"
     ]
    },
    {
     "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\r",
      "      9/Unknown - 12s 49ms/step - loss: 2.3296 - accuracy: 0.1667"
     ]
    },
    {
     "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\r",
      "     11/Unknown - 12s 47ms/step - loss: 2.3215 - accuracy: 0.1818"
     ]
    },
    {
     "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\r",
      "     13/Unknown - 12s 45ms/step - loss: 2.2755 - accuracy: 0.1538"
     ]
    },
    {
     "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\r",
      "     15/Unknown - 12s 44ms/step - loss: 2.3007 - accuracy: 0.1333"
     ]
    },
    {
     "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\r",
      "     17/Unknown - 12s 43ms/step - loss: 2.2216 - accuracy: 0.2353"
     ]
    },
    {
     "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\r",
      "     19/Unknown - 12s 43ms/step - loss: 2.1702 - accuracy: 0.2632"
     ]
    },
    {
     "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\r",
      "     21/Unknown - 12s 42ms/step - loss: 2.1293 - accuracy: 0.3095"
     ]
    },
    {
     "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\r",
      "     23/Unknown - 12s 42ms/step - loss: 2.1142 - accuracy: 0.3261"
     ]
    },
    {
     "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\r",
      "     25/Unknown - 12s 42ms/step - loss: 2.0910 - accuracy: 0.3400"
     ]
    },
    {
     "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\r",
      "     27/Unknown - 13s 41ms/step - loss: 2.0807 - accuracy: 0.3148"
     ]
    },
    {
     "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\r",
      "     29/Unknown - 13s 41ms/step - loss: 2.0191 - accuracy: 0.3621"
     ]
    },
    {
     "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\r",
      "     31/Unknown - 13s 41ms/step - loss: 1.9720 - accuracy: 0.3871"
     ]
    },
    {
     "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\r",
      "     33/Unknown - 13s 41ms/step - loss: 1.9815 - accuracy: 0.3788"
     ]
    },
    {
     "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\r",
      "     35/Unknown - 13s 41ms/step - loss: 1.9664 - accuracy: 0.3714"
     ]
    },
    {
     "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\r",
      "     37/Unknown - 13s 40ms/step - loss: 1.9182 - accuracy: 0.3919"
     ]
    },
    {
     "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\r",
      "     39/Unknown - 13s 40ms/step - loss: 1.9102 - accuracy: 0.3846"
     ]
    },
    {
     "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\r",
      "     41/Unknown - 13s 40ms/step - loss: 1.8868 - accuracy: 0.4024"
     ]
    },
    {
     "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\r",
      "     43/Unknown - 13s 40ms/step - loss: 1.8677 - accuracy: 0.4186"
     ]
    },
    {
     "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\r",
      "     45/Unknown - 13s 40ms/step - loss: 1.8436 - accuracy: 0.4333"
     ]
    },
    {
     "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\r",
      "     47/Unknown - 13s 40ms/step - loss: 1.8252 - accuracy: 0.4574"
     ]
    },
    {
     "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\r",
      "     49/Unknown - 13s 40ms/step - loss: 1.8053 - accuracy: 0.4694"
     ]
    },
    {
     "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\r",
      "     51/Unknown - 13s 40ms/step - loss: 1.7899 - accuracy: 0.4706"
     ]
    },
    {
     "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\r",
      "     53/Unknown - 14s 40ms/step - loss: 1.7864 - accuracy: 0.4717"
     ]
    },
    {
     "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\r",
      "     55/Unknown - 14s 40ms/step - loss: 1.7814 - accuracy: 0.4727"
     ]
    },
    {
     "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\r",
      "     57/Unknown - 14s 40ms/step - loss: 1.7691 - accuracy: 0.4825"
     ]
    },
    {
     "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\r",
      "     59/Unknown - 14s 40ms/step - loss: 1.7440 - accuracy: 0.5000"
     ]
    },
    {
     "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\r",
      "     61/Unknown - 14s 40ms/step - loss: 1.7258 - accuracy: 0.5082"
     ]
    },
    {
     "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\r",
      "     63/Unknown - 14s 40ms/step - loss: 1.6940 - accuracy: 0.5238"
     ]
    },
    {
     "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\r",
      "     65/Unknown - 14s 40ms/step - loss: 1.6647 - accuracy: 0.5385"
     ]
    },
    {
     "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\r",
      "     67/Unknown - 14s 40ms/step - loss: 1.6412 - accuracy: 0.5522"
     ]
    },
    {
     "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\r",
      "     69/Unknown - 14s 40ms/step - loss: 1.6216 - accuracy: 0.5652"
     ]
    },
    {
     "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\r",
      "     71/Unknown - 14s 39ms/step - loss: 1.6102 - accuracy: 0.5634"
     ]
    },
    {
     "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\r",
      "     73/Unknown - 14s 39ms/step - loss: 1.5878 - accuracy: 0.5753"
     ]
    },
    {
     "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\r",
      "     75/Unknown - 14s 39ms/step - loss: 1.5761 - accuracy: 0.5800"
     ]
    },
    {
     "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\r",
      "     77/Unknown - 14s 39ms/step - loss: 1.5631 - accuracy: 0.5844"
     ]
    },
    {
     "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\r",
      "     79/Unknown - 15s 39ms/step - loss: 1.5436 - accuracy: 0.5949"
     ]
    },
    {
     "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\r",
      "     81/Unknown - 15s 39ms/step - loss: 1.5281 - accuracy: 0.6049"
     ]
    },
    {
     "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\r",
      "     83/Unknown - 15s 39ms/step - loss: 1.5085 - accuracy: 0.6145"
     ]
    },
    {
     "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\r",
      "     85/Unknown - 15s 39ms/step - loss: 1.4928 - accuracy: 0.6235"
     ]
    },
    {
     "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\r",
      "     87/Unknown - 15s 39ms/step - loss: 1.4845 - accuracy: 0.6207"
     ]
    },
    {
     "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\r",
      "     89/Unknown - 15s 39ms/step - loss: 1.4795 - accuracy: 0.6236"
     ]
    },
    {
     "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\r",
      "     91/Unknown - 15s 39ms/step - loss: 1.4659 - 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\r",
      "     93/Unknown - 15s 39ms/step - loss: 1.4562 - accuracy: 0.6290"
     ]
    },
    {
     "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\r",
      "     95/Unknown - 15s 39ms/step - loss: 1.4380 - accuracy: 0.6368"
     ]
    },
    {
     "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\r",
      "     97/Unknown - 15s 39ms/step - loss: 1.4274 - accuracy: 0.6392"
     ]
    },
    {
     "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\r",
      "     99/Unknown - 15s 39ms/step - loss: 1.4110 - accuracy: 0.6465"
     ]
    },
    {
     "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\r",
      "    101/Unknown - 15s 39ms/step - loss: 1.3947 - accuracy: 0.6485"
     ]
    },
    {
     "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\r",
      "    103/Unknown - 15s 39ms/step - loss: 1.3825 - accuracy: 0.6553"
     ]
    },
    {
     "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\r",
      "    105/Unknown - 16s 39ms/step - loss: 1.3660 - accuracy: 0.6619"
     ]
    },
    {
     "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\r",
      "    107/Unknown - 16s 39ms/step - loss: 1.3531 - accuracy: 0.6636"
     ]
    },
    {
     "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\r",
      "    109/Unknown - 16s 39ms/step - loss: 1.3460 - accuracy: 0.6651"
     ]
    },
    {
     "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\r",
      "    111/Unknown - 16s 39ms/step - loss: 1.3284 - accuracy: 0.6712"
     ]
    },
    {
     "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\r",
      "    113/Unknown - 16s 39ms/step - loss: 1.3188 - accuracy: 0.6726"
     ]
    },
    {
     "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\r",
      "    115/Unknown - 16s 39ms/step - loss: 1.3019 - accuracy: 0.6783"
     ]
    },
    {
     "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\r",
      "    117/Unknown - 16s 39ms/step - loss: 1.2920 - accuracy: 0.6838"
     ]
    },
    {
     "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\r",
      "    119/Unknown - 16s 39ms/step - loss: 1.2779 - accuracy: 0.6891"
     ]
    },
    {
     "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\r",
      "    121/Unknown - 16s 39ms/step - loss: 1.2704 - accuracy: 0.6860"
     ]
    },
    {
     "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\r",
      "    123/Unknown - 16s 39ms/step - loss: 1.2560 - accuracy: 0.6911"
     ]
    },
    {
     "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\r",
      "    125/Unknown - 16s 39ms/step - loss: 1.2415 - accuracy: 0.6960"
     ]
    },
    {
     "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\r",
      "    127/Unknown - 16s 39ms/step - loss: 1.2312 - accuracy: 0.7008"
     ]
    },
    {
     "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\r",
      "    129/Unknown - 16s 39ms/step - loss: 1.2233 - accuracy: 0.7016"
     ]
    },
    {
     "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\r",
      "    131/Unknown - 17s 39ms/step - loss: 1.2125 - accuracy: 0.7061"
     ]
    },
    {
     "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\r",
      "    133/Unknown - 17s 39ms/step - loss: 1.2011 - accuracy: 0.7105"
     ]
    },
    {
     "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\r",
      "    135/Unknown - 17s 39ms/step - loss: 1.1909 - accuracy: 0.7148"
     ]
    },
    {
     "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\r",
      "    137/Unknown - 17s 39ms/step - loss: 1.1826 - accuracy: 0.7190"
     ]
    },
    {
     "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\r",
      "    139/Unknown - 17s 39ms/step - loss: 1.1737 - accuracy: 0.7230"
     ]
    },
    {
     "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\r",
      "    141/Unknown - 17s 39ms/step - loss: 1.1639 - accuracy: 0.7270"
     ]
    },
    {
     "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\r",
      "    143/Unknown - 17s 39ms/step - loss: 1.1576 - accuracy: 0.7308"
     ]
    },
    {
     "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\r",
      "    145/Unknown - 17s 39ms/step - loss: 1.1509 - accuracy: 0.7345"
     ]
    },
    {
     "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\r",
      "    147/Unknown - 17s 39ms/step - loss: 1.1424 - accuracy: 0.7381"
     ]
    },
    {
     "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\r",
      "    149/Unknown - 17s 39ms/step - loss: 1.1353 - accuracy: 0.7416"
     ]
    },
    {
     "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\r",
      "150/150 [==============================] - 23s 80ms/step - loss: 1.1324 - accuracy: 0.7433 - val_loss: 0.5808 - val_accuracy: 0.8200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.4550 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.3261 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.2783 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.2511 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.2691 - accuracy: 0.9444"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.3477 - accuracy: 0.9091"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.3500 - accuracy: 0.8846"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.3807 - accuracy: 0.8667"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.4394 - accuracy: 0.8824"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.4317 - accuracy: 0.8947"
     ]
    },
    {
     "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",
      " 21/150 [===>..........................] - ETA: 4s - loss: 0.4269 - accuracy: 0.9048"
     ]
    },
    {
     "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",
      " 23/150 [===>..........................] - ETA: 4s - loss: 0.4365 - accuracy: 0.9130"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.4172 - accuracy: 0.9200"
     ]
    },
    {
     "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",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.3962 - accuracy: 0.9259"
     ]
    },
    {
     "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",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.3954 - accuracy: 0.9310"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.3917 - accuracy: 0.9355"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.4005 - accuracy: 0.9242"
     ]
    },
    {
     "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",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.4017 - accuracy: 0.9286"
     ]
    },
    {
     "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",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.4020 - accuracy: 0.9324"
     ]
    },
    {
     "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",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.4055 - accuracy: 0.9359"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.3979 - accuracy: 0.9390"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.4028 - accuracy: 0.9419"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.4072 - accuracy: 0.9444"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/150 [========>.....................] - ETA: 3s - loss: 0.4046 - accuracy: 0.9468"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/150 [========>.....................] - ETA: 3s - loss: 0.3920 - accuracy: 0.9490"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.4020 - accuracy: 0.9412"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.3937 - accuracy: 0.9434"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.3952 - accuracy: 0.9455"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.3956 - accuracy: 0.9474"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.4020 - accuracy: 0.9492"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.3995 - accuracy: 0.9508"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.4105 - accuracy: 0.9444"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.4095 - accuracy: 0.9462"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.4004 - accuracy: 0.9478"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.3976 - accuracy: 0.9493"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.4087 - accuracy: 0.9437"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 2s - loss: 0.4031 - accuracy: 0.9452"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.4006 - accuracy: 0.9467"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.4079 - accuracy: 0.9481"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.4042 - accuracy: 0.9494"
     ]
    },
    {
     "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",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.3974 - accuracy: 0.9506"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.3939 - accuracy: 0.9518"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.3930 - accuracy: 0.9529"
     ]
    },
    {
     "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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.3923 - accuracy: 0.9540"
     ]
    },
    {
     "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",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.3887 - accuracy: 0.9551"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.3902 - accuracy: 0.9560"
     ]
    },
    {
     "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",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.3924 - accuracy: 0.9570"
     ]
    },
    {
     "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",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.3934 - accuracy: 0.9579"
     ]
    },
    {
     "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",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.3934 - accuracy: 0.9588"
     ]
    },
    {
     "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",
      " 99/150 [==================>...........] - ETA: 1s - loss: 0.3875 - accuracy: 0.9596"
     ]
    },
    {
     "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",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.3831 - accuracy: 0.9604"
     ]
    },
    {
     "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",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.3789 - accuracy: 0.9612"
     ]
    },
    {
     "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",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.3751 - accuracy: 0.9619"
     ]
    },
    {
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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",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.3717 - accuracy: 0.9626"
     ]
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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",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.3667 - accuracy: 0.9633"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.3617 - accuracy: 0.9640"
     ]
    },
    {
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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",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.3585 - accuracy: 0.9646"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.3566 - accuracy: 0.9652"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.3533 - accuracy: 0.9658"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.3566 - accuracy: 0.9622"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.3533 - accuracy: 0.9628"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.3494 - accuracy: 0.9634"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.3460 - accuracy: 0.9640"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.3441 - accuracy: 0.9646"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.3428 - accuracy: 0.9651"
     ]
    },
    {
     "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",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.3382 - accuracy: 0.9656"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.3525 - accuracy: 0.9624"
     ]
    },
    {
     "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",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.3508 - accuracy: 0.9630"
     ]
    },
    {
     "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",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.3493 - accuracy: 0.9635"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.3491 - accuracy: 0.9640"
     ]
    },
    {
     "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",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.3462 - accuracy: 0.9645"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.3495 - accuracy: 0.9615"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.3476 - accuracy: 0.9621"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.3439 - accuracy: 0.9626"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.3410 - accuracy: 0.9631"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 51ms/step - loss: 0.3391 - accuracy: 0.9633 - val_loss: 0.3407 - val_accuracy: 0.9300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.0556 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.2627 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.2339 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.2172 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.1864 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.1897 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.1933 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.2020 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.2068 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.2280 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/150 [===>..........................] - ETA: 4s - loss: 0.2157 - accuracy: 1.0000"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/150 [===>..........................] - ETA: 4s - loss: 0.2029 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.2050 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.1967 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.1937 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.1869 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.1803 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.1840 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.1801 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.1761 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.1854 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.1911 - accuracy: 0.9884"
     ]
    },
    {
     "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",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.1952 - accuracy: 0.9889"
     ]
    },
    {
     "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",
      " 47/150 [========>.....................] - ETA: 3s - loss: 0.1900 - accuracy: 0.9894"
     ]
    },
    {
     "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",
      " 49/150 [========>.....................] - ETA: 3s - loss: 0.1906 - accuracy: 0.9898"
     ]
    },
    {
     "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",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.1921 - accuracy: 0.9902"
     ]
    },
    {
     "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",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.1964 - accuracy: 0.9906"
     ]
    },
    {
     "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",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.1988 - accuracy: 0.9909"
     ]
    },
    {
     "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",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.2053 - accuracy: 0.9912"
     ]
    },
    {
     "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",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.2019 - accuracy: 0.9915"
     ]
    },
    {
     "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",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.2006 - accuracy: 0.9918"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.1993 - accuracy: 0.9921"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.1980 - accuracy: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.2092 - accuracy: 0.9851"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.2070 - accuracy: 0.9855"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.2051 - accuracy: 0.9859"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 2s - loss: 0.2010 - accuracy: 0.9863"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.1982 - accuracy: 0.9867"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.1955 - accuracy: 0.9870"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.2031 - accuracy: 0.9873"
     ]
    },
    {
     "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",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.2011 - accuracy: 0.9877"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.1981 - accuracy: 0.9880"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.1960 - accuracy: 0.9882"
     ]
    },
    {
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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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.1965 - accuracy: 0.9885"
     ]
    },
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      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.1937 - accuracy: 0.9888"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.1914 - accuracy: 0.9890"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.1892 - accuracy: 0.9892"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.1926 - accuracy: 0.9895"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.1915 - accuracy: 0.9897"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/150 [==================>...........] - ETA: 1s - loss: 0.1884 - accuracy: 0.9899"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.1867 - accuracy: 0.9901"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.1859 - accuracy: 0.9903"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.1842 - accuracy: 0.9905"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.1817 - accuracy: 0.9907"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.1834 - accuracy: 0.9908"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.1819 - accuracy: 0.9910"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.1800 - accuracy: 0.9912"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.1784 - accuracy: 0.9913"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.1770 - accuracy: 0.9915"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.1782 - accuracy: 0.9916"
     ]
    },
    {
     "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",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.1823 - accuracy: 0.9917"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.1812 - accuracy: 0.9919"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.1792 - accuracy: 0.9920"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.1766 - accuracy: 0.9921"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.1814 - accuracy: 0.9922"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.1811 - accuracy: 0.9924"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.1807 - accuracy: 0.9925"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.1812 - accuracy: 0.9926"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.1808 - accuracy: 0.9927"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.1827 - accuracy: 0.9928"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.1870 - accuracy: 0.9894"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.1873 - accuracy: 0.9895"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/150 [============================>.] - ETA: 0s - loss: 0.1865 - accuracy: 0.9897"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "147/150 [============================>.] - ETA: 0s - loss: 0.1847 - accuracy: 0.9898"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/150 [============================>.] - ETA: 0s - loss: 0.1830 - accuracy: 0.9899"
     ]
    },
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/150 [==============================] - 8s 51ms/step - loss: 0.1864 - accuracy: 0.9900 - val_loss: 0.2591 - val_accuracy: 0.9500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.1205 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.1329 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.1008 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.1419 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.1239 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.1168 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.1139 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.1130 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.1171 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.1234 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 21/150 [===>..........................] - ETA: 5s - loss: 0.1173 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 23/150 [===>..........................] - ETA: 4s - loss: 0.1146 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.1157 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.1202 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.1252 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.1217 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.1212 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.1167 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.1187 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.1166 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.1150 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.1147 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.1149 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 47/150 [========>.....................] - ETA: 3s - loss: 0.1129 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 49/150 [========>.....................] - ETA: 3s - loss: 0.1100 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.1071 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.1065 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.1073 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.1042 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.1037 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.1150 - accuracy: 0.9918"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.1152 - accuracy: 0.9921"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.1153 - accuracy: 0.9923"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.1142 - accuracy: 0.9925"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.1145 - accuracy: 0.9928"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.1135 - accuracy: 0.9930"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 2s - loss: 0.1138 - accuracy: 0.9932"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.1136 - accuracy: 0.9933"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.1193 - accuracy: 0.9935"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.1226 - accuracy: 0.9937"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.1206 - accuracy: 0.9938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.1214 - accuracy: 0.9940"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.1195 - accuracy: 0.9941"
     ]
    },
    {
     "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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.1186 - accuracy: 0.9943"
     ]
    },
    {
     "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",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.1179 - accuracy: 0.9944"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.1162 - accuracy: 0.9945"
     ]
    },
    {
     "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",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.1149 - accuracy: 0.9946"
     ]
    },
    {
     "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",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.1134 - accuracy: 0.9947"
     ]
    },
    {
     "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",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.1133 - accuracy: 0.9948"
     ]
    },
    {
     "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",
      " 99/150 [==================>...........] - ETA: 1s - loss: 0.1126 - accuracy: 0.9949"
     ]
    },
    {
     "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",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.1190 - accuracy: 0.9950"
     ]
    },
    {
     "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",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.1189 - accuracy: 0.9951"
     ]
    },
    {
     "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",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.1201 - accuracy: 0.9952"
     ]
    },
    {
     "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",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.1189 - accuracy: 0.9953"
     ]
    },
    {
     "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",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.1182 - accuracy: 0.9954"
     ]
    },
    {
     "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",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.1168 - accuracy: 0.9955"
     ]
    },
    {
     "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",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.1184 - accuracy: 0.9956"
     ]
    },
    {
     "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",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.1181 - accuracy: 0.9957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.1249 - accuracy: 0.9915"
     ]
    },
    {
     "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",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.1257 - accuracy: 0.9916"
     ]
    },
    {
     "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",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.1276 - accuracy: 0.9917"
     ]
    },
    {
     "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",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.1261 - accuracy: 0.9919"
     ]
    },
    {
     "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",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.1245 - accuracy: 0.9920"
     ]
    },
    {
     "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",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.1242 - accuracy: 0.9921"
     ]
    },
    {
     "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",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.1226 - accuracy: 0.9922"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.1228 - accuracy: 0.9924"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.1226 - accuracy: 0.9925"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.1212 - accuracy: 0.9926"
     ]
    },
    {
     "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",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.1239 - accuracy: 0.9927"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.1252 - accuracy: 0.9928"
     ]
    },
    {
     "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",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.1242 - accuracy: 0.9929"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.1235 - accuracy: 0.9930"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.1229 - accuracy: 0.9931"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.1216 - accuracy: 0.9932"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.1211 - accuracy: 0.9933"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 51ms/step - loss: 0.1216 - accuracy: 0.9933 - val_loss: 0.2290 - val_accuracy: 0.9300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.1072 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.0846 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.0697 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.0653 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.0561 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.0596 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.0585 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.0528 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.0638 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.0605 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 21/150 [===>..........................] - ETA: 5s - loss: 0.0712 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 23/150 [===>..........................] - ETA: 4s - loss: 0.0697 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.0665 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.0662 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.0657 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.0670 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.0647 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.0641 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.0633 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.0645 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.0629 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.0610 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.0596 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/150 [========>.....................] - ETA: 4s - loss: 0.0609 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/150 [========>.....................] - ETA: 3s - loss: 0.0668 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.0678 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.0698 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.0696 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.0674 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.0684 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.0700 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.0692 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.0685 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.0709 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.0726 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.0713 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 3s - loss: 0.0757 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.0740 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.0730 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.0749 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.0755 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.0755 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.0743 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.0739 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.0739 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.0731 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.0725 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.0717 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.0709 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 99/150 [==================>...........] - ETA: 2s - loss: 0.0702 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.0707 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.0702 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.0739 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.0750 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.0759 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0752 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.0765 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.0755 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.0744 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.0750 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.0752 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.0748 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.0792 - accuracy: 0.9960"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.0797 - accuracy: 0.9961"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.0816 - accuracy: 0.9961"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.0804 - accuracy: 0.9962"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.0800 - accuracy: 0.9962"
     ]
    },
    {
     "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",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.0835 - accuracy: 0.9963"
     ]
    },
    {
     "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",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.0847 - accuracy: 0.9964"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.0846 - accuracy: 0.9964"
     ]
    },
    {
     "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",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.0843 - accuracy: 0.9965"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.0844 - accuracy: 0.9965"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.0853 - accuracy: 0.9966"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.0862 - accuracy: 0.9966"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.0860 - accuracy: 0.9966"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 52ms/step - loss: 0.0864 - accuracy: 0.9967 - val_loss: 0.2058 - val_accuracy: 0.9300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.0460 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.0682 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.0565 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.0455 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.0418 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.0421 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.0388 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.0461 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.0508 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.0542 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 21/150 [===>..........................] - ETA: 4s - loss: 0.0567 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 23/150 [===>..........................] - ETA: 4s - loss: 0.0546 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.0595 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.0596 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.0600 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.0598 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.0580 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.0598 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.0663 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.0683 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.0770 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.0817 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.0799 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 47/150 [========>.....................] - ETA: 4s - loss: 0.0783 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 49/150 [========>.....................] - ETA: 3s - loss: 0.0780 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.0769 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.0753 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.0768 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.0756 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.0747 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.0733 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.0733 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.0752 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.0737 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.0726 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.0716 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 3s - loss: 0.0699 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.0699 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.0689 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.0676 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.0663 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.0655 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.0650 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.0656 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.0673 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.0667 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.0660 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.0653 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.0641 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/150 [==================>...........] - ETA: 1s - loss: 0.0633 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.0625 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.0621 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.0626 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.0632 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.0624 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0654 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.0655 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.0656 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.0655 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.0652 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.0661 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.0652 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.0645 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.0638 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.0635 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.0631 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.0636 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.0636 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.0644 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.0655 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.0651 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.0652 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.0647 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.0640 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.0644 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 52ms/step - loss: 0.0642 - accuracy: 1.0000 - val_loss: 0.1802 - val_accuracy: 0.9600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.0191 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.0574 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.0608 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.0482 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.0755 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.0715 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.0694 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.0643 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.0613 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.0595 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/150 [===>..........................] - ETA: 5s - loss: 0.0585 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/150 [===>..........................] - ETA: 5s - loss: 0.0654 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.0613 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.0602 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.0673 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.0663 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.0641 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.0627 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.0597 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.0579 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.0563 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.0563 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.0585 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 47/150 [========>.....................] - ETA: 4s - loss: 0.0565 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 49/150 [========>.....................] - ETA: 4s - loss: 0.0578 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.0610 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.0596 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.0588 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.0595 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.0582 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.0570 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.0559 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.0548 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.0545 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.0531 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.0527 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 73/150 [=============>................] - ETA: 3s - loss: 0.0516 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.0514 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.0516 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.0511 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.0500 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.0509 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.0502 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.0497 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.0498 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.0489 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.0489 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.0488 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.0480 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 99/150 [==================>...........] - ETA: 2s - loss: 0.0474 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.0477 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.0472 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.0468 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.0482 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.0482 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0475 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.0472 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.0467 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.0480 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.0480 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.0483 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.0483 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.0477 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.0481 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.0513 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.0512 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.0512 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.0505 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.0503 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.0512 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.0509 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.0513 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.0508 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.0510 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.0505 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 52ms/step - loss: 0.0510 - accuracy: 1.0000 - val_loss: 0.1728 - val_accuracy: 0.9400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.0603 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.0502 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.0467 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.0373 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.0393 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.0381 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.0428 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.0436 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.0410 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.0410 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 21/150 [===>..........................] - ETA: 5s - loss: 0.0395 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 23/150 [===>..........................] - ETA: 5s - loss: 0.0379 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.0407 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.0433 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.0427 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.0418 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.0399 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.0400 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.0396 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.0413 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.0405 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.0400 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.0412 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 47/150 [========>.....................] - ETA: 4s - loss: 0.0443 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 49/150 [========>.....................] - ETA: 4s - loss: 0.0448 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.0437 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.0424 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.0421 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.0421 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.0413 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.0419 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.0407 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.0412 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.0405 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.0402 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.0402 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 3s - loss: 0.0403 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 3s - loss: 0.0393 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.0388 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.0386 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.0383 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.0381 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.0379 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.0376 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.0370 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.0368 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.0368 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.0362 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.0365 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 99/150 [==================>...........] - ETA: 2s - loss: 0.0361 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.0356 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.0358 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.0387 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.0384 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.0397 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0394 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.0407 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.0406 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.0418 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.0418 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.0415 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.0423 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/150 [========================>.....] - ETA: 1s - loss: 0.0417 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.0414 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.0410 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.0405 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.0404 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.0406 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.0402 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.0404 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.0402 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.0403 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.0401 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.0401 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.0402 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 53ms/step - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.1640 - val_accuracy: 0.9400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.0043 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.0419 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.0297 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.0303 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.0251 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.0230 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.0248 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.0283 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.0291 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.0300 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 21/150 [===>..........................] - ETA: 5s - loss: 0.0282 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 23/150 [===>..........................] - ETA: 5s - loss: 0.0277 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.0355 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.0336 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.0316 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.0300 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.0289 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.0298 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.0305 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.0309 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.0327 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.0327 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.0327 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/150 [========>.....................] - ETA: 4s - loss: 0.0325 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/150 [========>.....................] - ETA: 4s - loss: 0.0335 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.0341 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.0339 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.0329 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.0349 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.0345 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.0342 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.0339 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.0332 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.0326 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.0348 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.0351 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 3s - loss: 0.0351 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 2s - loss: 0.0347 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.0346 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.0340 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.0334 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.0342 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.0342 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.0338 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.0340 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.0334 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.0330 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.0334 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.0331 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/150 [==================>...........] - ETA: 2s - loss: 0.0328 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.0324 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.0320 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.0315 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.0331 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.0337 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0333 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.0334 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.0332 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.0330 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.0331 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.0327 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.0327 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "125/150 [========================>.....] - ETA: 0s - loss: 0.0323 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.0323 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.0325 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.0325 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.0324 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.0325 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.0325 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.0323 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.0325 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.0322 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.0331 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.0338 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.0335 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 53ms/step - loss: 0.0332 - accuracy: 1.0000 - val_loss: 0.1593 - val_accuracy: 0.9400\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/150 [..............................] - ETA: 6s - loss: 0.0092 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  3/150 [..............................] - ETA: 5s - loss: 0.0199 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  5/150 [>.............................] - ETA: 5s - loss: 0.0285 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  7/150 [>.............................] - ETA: 5s - loss: 0.0321 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "  9/150 [>.............................] - ETA: 5s - loss: 0.0345 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 11/150 [=>............................] - ETA: 5s - loss: 0.0305 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 13/150 [=>............................] - ETA: 5s - loss: 0.0280 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 15/150 [==>...........................] - ETA: 5s - loss: 0.0251 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/150 [==>...........................] - ETA: 5s - loss: 0.0233 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 19/150 [==>...........................] - ETA: 5s - loss: 0.0221 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 21/150 [===>..........................] - ETA: 5s - loss: 0.0228 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/150 [===>..........................] - ETA: 5s - loss: 0.0241 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 25/150 [====>.........................] - ETA: 4s - loss: 0.0244 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 27/150 [====>.........................] - ETA: 4s - loss: 0.0237 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/150 [====>.........................] - ETA: 4s - loss: 0.0241 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 31/150 [=====>........................] - ETA: 4s - loss: 0.0234 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/150 [=====>........................] - ETA: 4s - loss: 0.0239 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 35/150 [======>.......................] - ETA: 4s - loss: 0.0289 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 37/150 [======>.......................] - ETA: 4s - loss: 0.0277 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 39/150 [======>.......................] - ETA: 4s - loss: 0.0269 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 41/150 [=======>......................] - ETA: 4s - loss: 0.0264 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 43/150 [=======>......................] - ETA: 4s - loss: 0.0253 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 45/150 [========>.....................] - ETA: 4s - loss: 0.0261 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 47/150 [========>.....................] - ETA: 4s - loss: 0.0266 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 49/150 [========>.....................] - ETA: 4s - loss: 0.0258 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 51/150 [=========>....................] - ETA: 3s - loss: 0.0252 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 53/150 [=========>....................] - ETA: 3s - loss: 0.0244 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 55/150 [==========>...................] - ETA: 3s - loss: 0.0238 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 57/150 [==========>...................] - ETA: 3s - loss: 0.0243 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 59/150 [==========>...................] - ETA: 3s - loss: 0.0239 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 61/150 [===========>..................] - ETA: 3s - loss: 0.0234 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 63/150 [===========>..................] - ETA: 3s - loss: 0.0237 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 65/150 [============>.................] - ETA: 3s - loss: 0.0241 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/150 [============>.................] - ETA: 3s - loss: 0.0238 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 69/150 [============>.................] - ETA: 3s - loss: 0.0250 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 71/150 [=============>................] - ETA: 3s - loss: 0.0252 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 73/150 [=============>................] - ETA: 3s - loss: 0.0267 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 75/150 [==============>...............] - ETA: 3s - loss: 0.0285 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      " 77/150 [==============>...............] - ETA: 2s - loss: 0.0284 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/150 [==============>...............] - ETA: 2s - loss: 0.0281 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/150 [===============>..............] - ETA: 2s - loss: 0.0279 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/150 [===============>..............] - ETA: 2s - loss: 0.0281 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/150 [================>.............] - ETA: 2s - loss: 0.0297 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 87/150 [================>.............] - ETA: 2s - loss: 0.0297 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/150 [================>.............] - ETA: 2s - loss: 0.0292 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/150 [=================>............] - ETA: 2s - loss: 0.0289 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/150 [=================>............] - ETA: 2s - loss: 0.0288 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/150 [==================>...........] - ETA: 2s - loss: 0.0288 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/150 [==================>...........] - ETA: 2s - loss: 0.0300 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/150 [==================>...........] - ETA: 2s - loss: 0.0299 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/150 [===================>..........] - ETA: 1s - loss: 0.0294 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "103/150 [===================>..........] - ETA: 1s - loss: 0.0291 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "105/150 [====================>.........] - ETA: 1s - loss: 0.0289 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "107/150 [====================>.........] - ETA: 1s - loss: 0.0288 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "109/150 [====================>.........] - ETA: 1s - loss: 0.0287 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "111/150 [=====================>........] - ETA: 1s - loss: 0.0285 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "113/150 [=====================>........] - ETA: 1s - loss: 0.0285 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "115/150 [======================>.......] - ETA: 1s - loss: 0.0281 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/150 [======================>.......] - ETA: 1s - loss: 0.0289 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "119/150 [======================>.......] - ETA: 1s - loss: 0.0291 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "121/150 [=======================>......] - ETA: 1s - loss: 0.0291 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/150 [=======================>......] - ETA: 1s - loss: 0.0289 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "125/150 [========================>.....] - ETA: 1s - loss: 0.0288 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "127/150 [========================>.....] - ETA: 0s - loss: 0.0294 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "129/150 [========================>.....] - ETA: 0s - loss: 0.0290 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/150 [=========================>....] - ETA: 0s - loss: 0.0288 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/150 [=========================>....] - ETA: 0s - loss: 0.0284 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "135/150 [==========================>...] - ETA: 0s - loss: 0.0282 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/150 [==========================>...] - ETA: 0s - loss: 0.0281 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/150 [==========================>...] - ETA: 0s - loss: 0.0280 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "141/150 [===========================>..] - ETA: 0s - loss: 0.0282 - accuracy: 1.0000"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/150 [===========================>..] - ETA: 0s - loss: 0.0281 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "145/150 [============================>.] - ETA: 0s - loss: 0.0279 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "147/150 [============================>.] - ETA: 0s - loss: 0.0276 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "149/150 [============================>.] - ETA: 0s - loss: 0.0275 - accuracy: 1.0000"
     ]
    },
    {
     "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",
      "150/150 [==============================] - 8s 53ms/step - loss: 0.0275 - accuracy: 1.0000 - val_loss: 0.1507 - val_accuracy: 0.9500\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f8265014160>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = tf.keras.applications.EfficientNetB0(include_top = False)\n",
    "net.trainable = False\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Rescaling(scale=255),\n",
    "    tf.keras.layers.TimeDistributed(net),\n",
    "    tf.keras.layers.Dense(10),\n",
    "    tf.keras.layers.GlobalAveragePooling3D()\n",
    "])\n",
    "\n",
    "model.compile(optimizer = 'adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_ds, \n",
    "          epochs = 10,\n",
    "          validation_data = val_ds,\n",
    "          callbacks = tf.keras.callbacks.EarlyStopping(patience = 2, monitor = 'val_loss'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DdJm7ojgGxtT"
   },
   "source": [
    "要详细了解如何在 TensorFlow 中处理视频数据，请查看以下教程：\n",
    "\n",
    "- [构建用于视频分类的 3D CNN 模型](https://tensorflow.google.cn/tutorials/video/video_classification)\n",
    "- [用于流式动作识别的 MoViNet](https://tensorflow.google.cn/hub/tutorials/movinet)\n",
    "- [使用 MoViNet 进行视频分类的迁移学习](https://tensorflow.google.cn/tutorials/video/transfer_learning_with_movinet)"
   ]
  }
 ],
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