{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5wFF5JFyD2Ki"
   },
   "source": [
    "#### Copyright 2019 The TensorFlow Hub Authors.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:45.870955Z",
     "iopub.status.busy": "2024-01-11T19:07:45.870731Z",
     "iopub.status.idle": "2024-01-11T19:07:45.874616Z",
     "shell.execute_reply": "2024-01-11T19:07:45.874013Z"
    },
    "id": "Uf6NouXxDqGk"
   },
   "outputs": [],
   "source": [
    "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ORy-KvWXGXBo"
   },
   "source": [
    "# TF-Hub CORD-19 Swivel 埋め込みを探索する\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfBg1C5NB3X0"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>     <a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/cord_19_embeddings_keras\">     <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">     TensorFlow.org で表示</a>\n",
    "</td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colabで実行</a>\n",
    "</td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub でソースを表示</a>\n",
    "</td>\n",
    "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/hub/tutorials/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n",
    "  <td>     <a href=\"https://tfhub.dev/tensorflow/cord-19/swivel-128d/3\"><img src=\"https://www.tensorflow.org/images/hub_logo_32px.png\">TF Hub モデルを参照</a>   </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yI6Mh3-P0_Pk"
   },
   "source": [
    "TF-Hub (https://tfhub.dev/tensorflow/cord-19/swivel-128d/3) の CORD-19 Swivel テキスト埋め込みモジュールは、COVID-19 に関連する自然言語テキストを分析する研究者をサポートするために構築されました。これらの埋め込みは、[CORD-19 データセット](https://api.semanticscholar.org/CorpusID:216056360)の論文のタイトル、著者、抄録、本文、および参照タイトルをトレーニングしています。\n",
    "\n",
    "この Colab では、以下について取り上げます。\n",
    "\n",
    "- 埋め込み空間内の意味的に類似した単語の分析\n",
    "- CORD-19 埋め込みを使用した SciCite データセットによる分類器のトレーニング\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gVWOrccw0_Pl"
   },
   "source": [
    "## セットアップ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:45.878271Z",
     "iopub.status.busy": "2024-01-11T19:07:45.878032Z",
     "iopub.status.idle": "2024-01-11T19:07:49.404592Z",
     "shell.execute_reply": "2024-01-11T19:07:49.403832Z"
    },
    "id": "Ym2nXOPuPV__"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-11 19:07:47.276545: 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",
      "2024-01-11 19:07:47.276588: 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",
      "2024-01-11 19:07:47.278343: 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 functools\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_hub as hub\n",
    "\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VgRRf2I7tER"
   },
   "source": [
    "# 埋め込みを分析する\n",
    "\n",
    "まず、異なる単語間の相関行列を計算してプロットし、埋め込みを分析してみましょう。異なる単語の意味をうまく捉えられるように埋め込みが学習できていれば、意味的に似た単語の埋め込みベクトルは近くにあるはずです。COVID-19 関連の用語をいくつか見てみましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:49.409282Z",
     "iopub.status.busy": "2024-01-11T19:07:49.408449Z",
     "iopub.status.idle": "2024-01-11T19:07:55.157349Z",
     "shell.execute_reply": "2024-01-11T19:07:55.156666Z"
    },
    "id": "HNN_9bBKSLHU"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAHmCAYAAACWKUEfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABfnklEQVR4nO3dd1iTZ9sG8DOssGSICKggIg5ABRUH7oEFtVpHrVVUcO9FXbhwvBW1tdVWW1tFkVbco1qsVqmjrjpBrauiiK0sByqoAcnz/SHmMwUiqcEn4/y9x3Mc5M4zrvhauXLdSyIIggAiIiIigpHYARARERFpCyZGRERERIWYGBEREREVYmJEREREVIiJEREREVEhJkZEREREhZgYERERERViYkRERERUiIkRERERUSEmRkRERESFmBgRERGR1jly5Ai6dOmCSpUqQSKRYOfOnW+85tChQ2jQoAGkUik8PT0RExOj9nOZGBEREZHWyc3Nha+vL1asWFGq82/duoXOnTujbdu2SExMxIQJEzBkyBDs27dPredKuIksERERaTOJRIIdO3agW7duJZ4zdepUxMfH49KlS4q2jz/+GNnZ2di7d2+pn8WKEREREb0TMpkMjx8/VjpkMplG7n3ixAkEBgYqtQUFBeHEiRNq3cdEI9GQRj39YqjYIahl/+JcsUNQS4qZbn0f2FqQJnYIavn7+T2xQ1CLg5mN2CGoTQ7dKvRbG0nFDkEtaXnZYoeglr+yzpb5M/Lv3dTIfaKWx2Lu3LlKbZGRkZgzZ85b3zs9PR1OTk5KbU5OTnj8+DGePXsGCwuLUt2HiRERERG9ExEREQgPD1dqk0q1K3FmYkRERESqyQs0chupVFpmiZCzszMyMjKU2jIyMmBjY1PqahHAxIiIiIjeRJCLHcEbBQQEYM+ePUpt+/fvR0BAgFr30a3BFkRERGQQcnJykJiYiMTERAAvp+MnJiYiNTUVwMtuuQEDBijOHzFiBG7evIkpU6bg6tWr+Oabb7B582ZMnDhRreeyYkRERESqyd99xejMmTNo27at4vWrsUmhoaGIiYlBWlqaIkkCgGrVqiE+Ph4TJ07EsmXLUKVKFaxevRpBQUFqPZeJEREREakkiNCV1qZNG6haarG4Va3btGmD8+fPv9Vz2ZVGREREVIgVIyIiIlJNhK40sTAxIiIiItV0YFaaprArjYiIiKgQK0ZERESkmoYWeNQFTIyIiIhINQPqSmNiRERERKoZ0OBrjjF6jUQiwc6dO8UOg4iIiETCitFr0tLSYG9vL3YYREREWkWMBR7FImpilJeXBzMzMzFDUOLs7Kzy/fz8fJiamr6jaIiIiLQEu9JKJpfLsXjxYnh6ekIqlcLNzQ2ffvopAODixYto164dLCws4ODggGHDhiEnJ0dxbVhYGLp164ZPP/0UlSpVQq1atdS67vPPP4eLiwscHBwwevRo5OfnK8754Ycf4O/vj3LlysHZ2Rl9+/ZFZmamIuYqVarg22+/Vfos58+fh5GREW7fvg1AuSstJSUFEokEmzZtQuvWrWFubo7169djzpw58PPzU7rP0qVL4e7urnh96NAhNG7cGFZWVrCzs0Pz5s0VzyAiIiLtpXZiFBERgYULF2LWrFm4fPky4uLi4OTkhNzcXAQFBcHe3h6nT5/Gli1bcODAAYwZM0bp+oSEBFy7dg379+/Hzz//XOrrDh48iOTkZBw8eBDr1q1DTEyM0j4p+fn5mD9/PpKSkrBz506kpKQgLCzs5Yc0MkKfPn0QFxendM/169ejefPmqFq1aomfd9q0aRg/fjyuXLlSqo3oXrx4gW7duqF169a4cOECTpw4gWHDhkEikbzxWiIiIq0kyDVz6AC1utKePHmCZcuWYfny5QgNDQUAVK9eHS1atMCqVavw/PlzxMbGwsrKCgCwfPlydOnSBYsWLYKTkxMAwMrKCqtXr1Z0oZX2Ont7eyxfvhzGxsaoXbs2OnfujISEBAwdOhQAMGjQIEWcHh4e+Oqrr9CoUSPk5OTA2toaISEhWLJkCVJTU+Hm5ga5XI6NGzdi5syZKj/zhAkT0KNHj1L/GT1+/BiPHj3C+++/j+rVqwMAvLy8Sn09ERGR1jGgdYzUqhhduXIFMpkM7du3L/Y9X19fRXIDAM2bN4dcLse1a9cUbXXr1lUaV1Ta63x8fGBsbKx47eLiougqA4CzZ8+iS5cucHNzQ7ly5dC6dWsAQGpqKgDAz88PXl5eiqrR4cOHkZmZiV69eqn8zP7+/qr/UP6lfPnyCAsLQ1BQELp06YJly5YhLS2txPNlMhkeP36sdMheGM5fQCIiIm2iVmJkYWHx1g98PQFSx78HPUskEsgLB4O96o6zsbHB+vXrcfr0aezYsQPAywHer4SEhCgSo7i4OAQHB8PBwUGteI2MjCAIglLb62OdAGDt2rU4ceIEmjVrhk2bNqFmzZo4efJksfePioqCra2t0vF5QqLKmIiIiN4pA+pKUysxqlGjBiwsLJCQkFDkPS8vLyQlJSE3N1fRduzYMRgZGSkGWRfnv173uqtXr+L+/ftYuHAhWrZsidq1aytVk17p27cvLl26hLNnz2Lr1q0ICQkp1f1f5+joiPT0dKXkKDExsch59evXR0REBI4fP446deoUGd/0SkREBB49eqR0TGrvp3ZcREREZUYu18yhA9RKjMzNzTF16lRMmTIFsbGxSE5OxsmTJxEdHY2QkBCYm5sjNDQUly5dwsGDBzF27Fj0799fMU6oOP/1ute5ubnBzMwMX3/9NW7evIldu3Zh/vz5Rc5zd3dHs2bNMHjwYBQUFKBr167qfHwAQJs2bZCVlYXFixcjOTkZK1aswC+//KJ4/9atW4iIiMCJEydw+/Zt/Prrr/jrr79KHGcklUphY2OjdEhNjIs9l4iIiMqW2rPSZs2ahU8++QSzZ8+Gl5cXevfujczMTFhaWmLfvn148OABGjVqhA8//BDt27fH8uXLVd7vv173OkdHR8TExGDLli3w9vbGwoUL8fnnnxd7bkhICJKSktC9e/f/1DXo5eWFb775BitWrICvry9OnTqFSZMmKX2eq1evomfPnqhZsyaGDRuG0aNHY/jw4Wo/i4iISCsYUFeaRPj3gBkS3dMvhoodglr2L85980laJMVMt3bC2VpQ8uB9bfT383tih6AWBzMbsUNQmxy69c+2tZFU7BDUkpaXLXYIavkr62yZP0N2YZ9G7iOt9+Zlb8TGLUGIiIhIJUEwnNnSuvXVmYiIiKgMsWJEREREqunI+CBNYGJEREREqunIVHtNYFcaERERUSFWjIiIiEg1dqURERERFeImskRERESGhxUjIiIiUo1daURERESFOCuNiIiIyPCwYkRERESqsSuNiIiIqJABdaUxMSIiIiLVDCgx4hgjIiIiokKsGGmh/YtzxQ5BLc3r/yN2CGp5nlhF7BDUcib7htghqCW/4IXYIajF1NZU7BDU5mPhInYIapFBtxYH9DSvKHYIWkcQdOv/w7fBxIiIiIhUY1caERERkeFhxYiIiIhU43R9IiIiokLsSiMiIiIyPKwYERERkWrsSiMiIiIqxK40IiIiIsPDihERERGpxq40IiIiokIG1JXGxIiIiIhUM6DEiGOMiIiIiAqxYkRERESqcYwRERERUSF2pemPrKwsjBw5Em5ubpBKpXB2dkZQUBCOHTumdN6JEydgbGyMzp07F7lHSkoKJBKJ4ihfvjxat26N33//Xem8p0+fIiIiAtWrV4e5uTkcHR3RunVr/PTTT2X6GYmIiEgz9L5i1LNnT+Tl5WHdunXw8PBARkYGEhIScP/+faXzoqOjMXbsWERHR+Pu3buoVKlSkXsdOHAAPj4+uHfvHj799FO8//77uH79OpycnAAAI0aMwB9//IGvv/4a3t7euH//Po4fP17kWURERDqFXWn6ITs7G7///jsOHTqE1q1bAwCqVq2Kxo0bK52Xk5ODTZs24cyZM0hPT0dMTAymT59e5H4ODg5wdnaGs7Mzpk+fjo0bN+KPP/5A165dAQC7du3CsmXL0KlTJwCAu7s7GjZsWMafkoiIqIyxK00/WFtbw9raGjt37oRMJivxvM2bN6N27dqoVasW+vXrhzVr1kAQhBLPf/bsGWJjYwEAZmZminZnZ2fs2bMHT5480dyHICIiondGrxMjExMTxMTEYN26dbCzs0Pz5s0xffp0XLhwQem86Oho9OvXDwAQHByMR48e4fDhw0Xu16xZM1hbW8PKygqff/45GjZsiPbt2yve//7773H8+HE4ODigUaNGmDhxYpGxTERERDpHkGvm0AF6nRgBL8cY3b17F7t27UJwcDAOHTqEBg0aICYmBgBw7do1nDp1Cn369AHwMpnq3bs3oqOji9xr06ZNOH/+PLZt2wZPT0/ExMTA1NRU8X6rVq1w8+ZNJCQk4MMPP8Sff/6Jli1bYv78+SXGJ5PJ8PjxY6UjXyjQ7B8CERHR25DLNXPoAL1PjADA3NwcHTp0wKxZs3D8+HGEhYUhMjISwMtq0YsXL1CpUiWYmJjAxMQE3377LbZt24ZHjx4p3cfV1RU1atRA9+7dsWDBAnTv3r1IF52pqSlatmyJqVOn4tdff8W8efMwf/585OXlFRtbVFQUbG1tlY6tuZfL5g+CiIiIVDKIxOjfvL29kZubixcvXiA2NhZLlixBYmKi4khKSkKlSpWwYcOGEu/x4YcfwsTEBN98880bn/XixQs8f/682PcjIiLw6NEjpeNDK++3+nxEREQaZUAVI72elXb//n306tULgwYNQr169VCuXDmcOXMGixcvxgcffICff/4ZDx8+xODBg2Fra6t0bc+ePREdHY0RI0YUe2+JRIJx48Zhzpw5GD58OCwtLdGmTRv06dMH/v7+cHBwwOXLlzF9+nS0bdsWNjY2xd5HKpVCKpUqtZlKjDXzB0BERKQJKiYk6Ru9rhhZW1ujSZMm+PLLL9GqVSvUqVMHs2bNwtChQ7F8+XJER0cjMDCwSFIEvEyMzpw5U2Sg9utCQ0ORn5+P5cuXAwCCgoKwbt06vPfee/Dy8sLYsWMRFBSEzZs3l9lnJCIiKnMGVDGSCKrmpZMofnLuK3YIamle/x+xQ1BLQmIVsUNQS1j2UbFDUEt+wQuxQ1CLh62L2CGozcdCt2KWQbcmlOjar8Vf7vxS5s94tiFSI/ex6DNXI/cpS3rdlUZEREQaoCPVHk3Q6640IiIi0gAR1zFasWIF3N3dYW5ujiZNmuDUqVMqz1+6dClq1aoFCwsLuLq6YuLEiSVOgCoOEyMiIiLSSps2bUJ4eDgiIyNx7tw5+Pr6IigoCJmZmcWeHxcXh2nTpiEyMhJXrlxBdHQ0Nm3aVOw2XyVhYkRERESqiTT4+osvvsDQoUMxcOBAeHt7Y+XKlbC0tMSaNWuKPf/48eNo3rw5+vbtC3d3d7z33nvo06fPG6tMr2NiRERERKoJgmYONeTl5eHs2bMIDAxUtBkZGSEwMBAnTpwo9ppmzZrh7NmzikTo5s2b2LNnj2Jz99Lg4GsiIiJ6J2QyWZEdI4pbzw8A7t27h4KCAjg5OSm1Ozk54erVq8Xev2/fvrh37x5atGgBQRDw4sULjBgxgl1pREREpEEa6korbhusqKgojYV56NAhLFiwAN988w3OnTuH7du3Iz4+XuWepf/GihERERGppqHp+hEREQgPD1dqK65aBAAVKlSAsbExMjIylNozMjLg7Oxc7DWzZs1C//79MWTIEABA3bp1kZubi2HDhmHGjBkwMnpzPYgVIyIiInonpFIpbGxslI6SEiMzMzM0bNgQCQkJija5XI6EhAQEBAQUe83Tp0+LJD/Gxi+32Srtwp2sGBEREZFq/3ENorcVHh6O0NBQ+Pv7o3Hjxli6dClyc3MxcOBAAMCAAQNQuXJlRXdcly5d8MUXX6B+/fpo0qQJbty4gVmzZqFLly6KBOlNmBgRERGRSoJcnG1SevfujaysLMyePRvp6enw8/PD3r17FQOyU1NTlSpEM2fOhEQiwcyZM/HPP//A0dERXbp0waefflrqZ3KvNC3EvdLKFvdKK1vcK63sca+0sqVrvxbfxV5pT1eO18h9LEcs08h9yhLHGBEREREVYlcaERERqSbSGCMxMDEiIiIi1UQaYyQGdqURERERFWLFSAulmOlWvvpcxwYzt6x2V+wQ1OJyvbzYIajl6YvnYoeglvKm1mKHoDY7IzOxQ1BLhvyZ2CGoJTXvgdghaB8NLfCoC5gYERERkWoGlBjpVmmCiIiIqAyxYkRERESq6djaTm+DiRERERGpZkBdaUyMiIiISDVO1yciIiIyPKwYERERkWpc+ZqIiIioELvSiIiIiAwPK0ZERESkksBZaURERESF2JVGREREZHhYMSIiIiLVDGhWmt5WjMLCwiCRSDBixIgi740ePRoSiQRhYWFK5/77CA4OVlzj7u6uaLe0tETdunWxevXqIvdetWoVfH19YW1tDTs7O9SvXx9RUVFl9jmJiIjKnFzQzKED9DYxAgBXV1ds3LgRz549U7Q9f/4ccXFxcHNzUzo3ODgYaWlpSseGDRuUzpk3bx7S0tJw6dIl9OvXD0OHDsUvv/yieH/NmjWYMGECxo0bh8TERBw7dgxTpkxBTk5O2X5QIiIi0gi97kpr0KABkpOTsX37doSEhAAAtm/fDjc3N1SrVk3pXKlUCmdnZ5X3K1eunOKcqVOnYvHixdi/fz86duwIANi1axc++ugjDB48WHGNj4+PJj8SERHRu2dAs9L0umIEAIMGDcLatWsVr9esWYOBAwe+1T3lcjm2bduGhw8fwszMTNHu7OyMkydP4vbt2291fyIiIq3CrjT90a9fPxw9ehS3b9/G7du3cezYMfTr16/IeT///DOsra2VjgULFiidM3XqVFhbW0MqleLDDz+Evb09hgwZong/MjISdnZ2cHd3R61atRAWFobNmzdDbkCZNhER6SFBrplDB+h1VxoAODo6onPnzoiJiYEgCOjcuTMqVKhQ5Ly2bdvi22+/VWorX7680uvJkycjLCwMaWlpmDx5MkaNGgVPT0/F+y4uLjhx4gQuXbqEI0eO4Pjx4wgNDcXq1auxd+9eGBkVzUNlMhlkMplS2wuhACYS47f52ERERPQf6H1iBLzsThszZgwAYMWKFcWeY2VlpZTkFKdChQrw9PSEp6cntmzZgrp168Lf3x/e3t5K59WpUwd16tTBqFGjMGLECLRs2RKHDx9G27Zti9wzKioKc+fOVWoLsqmLjrb11PmIREREZUdHusE0Qe+70oCXM87y8vKQn5+PoKAgjdzT1dUVvXv3RkREhMrzXiVNubm5xb4fERGBR48eKR0dbDhgm4iItIcgl2vk0AUGUTEyNjbGlStXFD8XRyaTIT09XanNxMSk2G63V8aPH486dergzJkz8Pf3x8iRI1GpUiW0a9cOVapUQVpaGv73v//B0dERAQEBxd5DKpVCKpUqP5fdaERERKIwiIoRANjY2MDGxqbE9/fu3QsXFxelo0WLFirv6e3tjffeew+zZ88GAAQGBuLkyZPo1asXatasiZ49e8Lc3BwJCQlwcHDQ6OchIiJ6ZwxoVppEEATdiNSALHMrOmtOmznn69ZfoZbV7oodglpaXM8WOwS1PH3xXOwQ1OJu6SR2CGrzMtOtL1oZ8mdvPkmL3JbdFzsEtfyZ8UeZPyNncneN3Mf6sx0auU9ZMpiKEREREdGbGMQYIyIiInoLOrIGkSYwMSIiIiLVdGR8kCawK42IiIioECtGREREpJJgQBUjJkZERESkGhMjIiIiokI6smq1JnCMEREREVEhVoyIiIhINXalERERERUyoMSIXWlEREREhVgxIiIiIpUMaVtVJkZERESkGrvSiIiIiAwPK0ZERESkmgFVjJgYERERkUrcEoREtbUgTewQ1HIm+4bYIajF5Xp5sUNQy5WjS8UOQS3n2y0TOwS1XJFYih2C2qY8PC12CGqxNjEXOwSiUmNiRERERKqxYkRERERUyHC2SmNiRERERKoZ0hgjTtcnIiIiKsSKEREREalmQBUjJkZERESkmgGNMWJXGhEREVEhVoyIiIhIJUMafM3EiIiIiFRjVxoRERGR4WHFiIiIiFRiVxoRERHRK+xKo9IICwtDt27dxA6DiIiINMRgE6PXk5o2bdpgwoQJosZDRESkrQS5Zg5dwK40IiIiUk1HkhpNMNiK0SthYWE4fPgwli1bBolEAolEgpSUFBQUFGDw4MGoVq0aLCwsUKtWLSxbtqzE+8TGxsLBwQEymUypvVu3bujfv39ZfwwiIqIyY0gVI4NPjJYtW4aAgAAMHToUaWlpSEtLg6urK+RyOapUqYItW7bg8uXLmD17NqZPn47NmzcXe59evXqhoKAAu3btUrRlZmYiPj4egwYNelcfh4iISK+sWLEC7u7uMDc3R5MmTXDq1CmV52dnZ2P06NFwcXGBVCpFzZo1sWfPnlI/z+C70mxtbWFmZgZLS0s4Ozsr2o2NjTF37lzF62rVquHEiRPYvHkzPvrooyL3sbCwQN++fbF27Vr06tULAPDjjz/Czc0Nbdq0KfPPQUREVGZEqvZs2rQJ4eHhWLlyJZo0aYKlS5ciKCgI165dQ8WKFYucn5eXhw4dOqBixYrYunUrKleujNu3b8POzq7UzzT4xEiVFStWYM2aNUhNTcWzZ8+Ql5cHPz+/Es8fOnQoGjVqhH/++QeVK1dGTEwMwsLCIJFISrxGJpMV6X6TC3IYSQy+mEdERFpCrG6wL774AkOHDsXAgQMBACtXrkR8fDzWrFmDadOmFTl/zZo1ePDgAY4fPw5TU1MAgLu7u1rP5G/fEmzcuBGTJk3C4MGD8euvvyIxMREDBw5EXl5eidfUr18fvr6+iI2NxdmzZ/Hnn38iLCxM5XOioqJga2urdNx5kqLZD0NERKQFZDIZHj9+rHT8uzjwSl5eHs6ePYvAwEBFm5GREQIDA3HixIlir9m1axcCAgIwevRoODk5oU6dOliwYAEKCgpKHSMTIwBmZmZF/tCOHTuGZs2aYdSoUahfvz48PT2RnJz8xnsNGTIEMTExWLt2LQIDA+Hq6qry/IiICDx69EjpcC3n/jYfh4iISKM0Nfi6uGJAVFRUsc+8d+8eCgoK4OTkpNTu5OSE9PT0Yq+5efMmtm7dioKCAuzZswezZs3CkiVL8L///a/Un5VdaXhZZvvjjz+QkpICa2trlC9fHjVq1EBsbCz27duHatWq4YcffsDp06dRrVo1lffq27cvJk2ahFWrViE2NvaNz5ZKpZBKpUpt7EYjIiJtoqmutIiICISHhyu1/ft34NuQy+WoWLEivv/+exgbG6Nhw4b4559/8NlnnyEyMrJU9+BvYACTJk2CsbExvL294ejoiNTUVAwfPhw9evRA79690aRJE9y/fx+jRo16471sbW3Rs2dPWFtbc1VsIiKi10ilUtjY2CgdJSVGFSpUgLGxMTIyMpTaMzIylCZLvc7FxQU1a9aEsbGxos3Lywvp6ekqh8K8zmArRjExMYqfa9asWWx/5dq1a7F27VqlttdLfq/f43X//PMPQkJCNJoFExERiUYoeRJRWTEzM0PDhg2RkJCgKDTI5XIkJCRgzJgxxV7TvHlzxMXFQS6Xw8joZe3n+vXrcHFxgZmZWamey4qRBj18+BA7duzAoUOHMHr0aLHDISIi0gixFngMDw/HqlWrsG7dOly5cgUjR45Ebm6uYpbagAEDEBERoTh/5MiRePDgAcaPH4/r168jPj4eCxYsUOt3ssFWjMpC/fr18fDhQyxatAi1atUSOxwiIiKd1rt3b2RlZWH27NlIT0+Hn58f9u7dqxiQnZqaqqgMAYCrqyv27duHiRMnol69eqhcuTLGjx+PqVOnlvqZTIw0KCUlRewQiIiINE6Qv/uutFfGjBlTYtfZoUOHirQFBATg5MmT//l5TIyIiIhIJV3Z50wTmBgRERGRSoIIg6/FwsHXRERERIVYMSIiIiKV2JVGREREVEjMwdfvGrvSiIiIiAqxYkREREQqCYLYEbw7TIyIiIhIJXalERERERkgVoyIiIhIJUOqGDExIiIiIpUMaYwRu9KIiIiICrFiRERERCqxK41E9ffze2KHoJb8ghdih6CWpy+eix2CWs63WyZ2CGrx/b6F2CGo5e6QM2KHoLaHz3LEDkEtno6VxA5BLdn5uWKHoHUMaa80JkZERESkkiFtCcIxRkRERESFWDEiIiIileTsSiMiIiJ6yZDGGLErjYiIiKgQK0ZERESkEqfrExERERXiytdEREREBogVIyIiIlKJXWlEREREhQxpuj670oiIiIgKsWJEREREKnEdIyqVOXPmwM/PT+wwiIiIypQgaObQBXqbGGVlZWHkyJFwc3ODVCqFs7MzgoKCcOzYMY09Y9KkSUhISNDY/YiIiLSRXJBo5NAFetuV1rNnT+Tl5WHdunXw8PBARkYGEhIScP/+fY09w9raGtbW1hq7HxEREYlLLytG2dnZ+P3337Fo0SK0bdsWVatWRePGjREREYGuXbsCACQSCb799lt07NgRFhYW8PDwwNatW5XuM3XqVNSsWROWlpbw8PDArFmzkJ+fr3j/311pYWFh6NatGz7//HO4uLjAwcEBo0ePVrqGiIhI1wiCRCOHLtDLxOhVJWfnzp2QyWQlnjdr1iz07NkTSUlJCAkJwccff4wrV64o3i9XrhxiYmJw+fJlLFu2DKtWrcKXX36p8tkHDx5EcnIyDh48iHXr1iEmJgYxMTGa+mhERETvHMcY6TgTExPExMRg3bp1sLOzQ/PmzTF9+nRcuHBB6bxevXphyJAhqFmzJubPnw9/f398/fXXivdnzpyJZs2awd3dHV26dMGkSZOwefNmlc+2t7fH8uXLUbt2bbz//vvo3LkzxyERERHpCL1MjICXY4zu3r2LXbt2ITg4GIcOHUKDBg2UqjcBAQFK1wQEBChVjDZt2oTmzZvD2dkZ1tbWmDlzJlJTU1U+18fHB8bGxorXLi4uyMzMLPF8mUyGx48fKx2CIFfz0xIREZUdQxp8rbeJEQCYm5ujQ4cOmDVrFo4fP46wsDBERkaW6toTJ04gJCQEnTp1ws8//4zz589jxowZyMvLU3mdqamp0muJRAK5vOREJyoqCra2tkpH9rOSEykiIqJ3jWOM9JS3tzdyc3MVr0+ePKn0/smTJ+Hl5QUAOH78OKpWrYoZM2bA398fNWrUwO3btzUeU0REBB49eqR02FlU1PhziIiI6M30crr+/fv30atXLwwaNAj16tVDuXLlcObMGSxevBgffPCB4rwtW7bA398fLVq0wPr163Hq1ClER0cDAGrUqIHU1FRs3LgRjRo1Qnx8PHbs2KHxWKVSKaRSqVKbRGJQ+SoREWk5XekG0wS9TIysra3RpEkTfPnll0hOTkZ+fj5cXV0xdOhQTJ8+XXHe3LlzsXHjRowaNQouLi7YsGEDvL29AQBdu3bFxIkTMWbMGMhkMnTu3BmzZs3CnDlzRPpURERE4tCRCWUaIREEXZlAp1kSiQQ7duxAt27dxA6liGoOvmKHoJY7T+6JHYJaKljaiB2CWnZaeokdglp8v28hdghq+WXIGbFDUNtHDw6LHYJaGjnWFDsEtWTn5775JC1yJfNUmT/jZKUeGrlP07vbNXKfsqSXFSMiIiLSHHalERERERXSlRllmmCwiZGB9iASERGpzZBW1+P0JyIiIqJCBlsxIiIiotIRwK40IiIiIgCA3IBGn7ArjYiIiKgQK0ZERESkkpxdaUREREQvGdIYI3alERERERVixYiIiIhUMqR1jJgYERERkUrsSiMiIiIyQKwYERERkUrsSiMiIiIqxMSIiIiIqJAhjTFiYqSFHMxsxA5BLaa2pmKHoJbyptZih6CWKxJLsUNQy90hZ8QOQS1BY3RvqKXb0opih6AWF5NyYoeglvt5T8QOgUTExIiIiIhUkhtOwYiJEREREalmSFuC6F4NmYiIiKiMsGJEREREKgliB/AOMTEiIiIilQxpuj670oiIiIgKMTEiIiIileQSiUaO/2LFihVwd3eHubk5mjRpglOnTpXquo0bN0IikaBbt25qPY+JEREREakkaOhQ16ZNmxAeHo7IyEicO3cOvr6+CAoKQmZmpsrrUlJSMGnSJLRs2VLtZzIxIiIiIq30xRdfYOjQoRg4cCC8vb2xcuVKWFpaYs2aNSVeU1BQgJCQEMydOxceHh5qP5OJEREREakk19Chjry8PJw9exaBgYGKNiMjIwQGBuLEiRMlXjdv3jxUrFgRgwcPVvOJL3FWGhEREamkqZWvZTIZZDKZUptUKoVUKi1y7r1791BQUAAnJyeldicnJ1y9erXY+x89ehTR0dFITEz8zzGyYkREREQqySHRyBEVFQVbW1ulIyoqSiMxPnnyBP3798eqVatQoUKF/3wfVoyIiIjonYiIiEB4eLhSW3HVIgCoUKECjI2NkZGRodSekZEBZ2fnIucnJycjJSUFXbp0UbTJ5S878ExMTHDt2jVUr179jTHqXMUoLCwMEomkyBEcHCx2aERERHpJU7PSpFIpbGxslI6SEiMzMzM0bNgQCQkJija5XI6EhAQEBAQUOb927dq4ePEiEhMTFUfXrl3Rtm1bJCYmwtXVtVSfVScrRsHBwVi7dq1SW0l/sG8iCAIKCgpgYqKTfxRERERlTlNjjNQVHh6O0NBQ+Pv7o3Hjxli6dClyc3MxcOBAAMCAAQNQuXJlREVFwdzcHHXq1FG63s7ODgCKtKuicxUj4GUS5OzsrHTY29sjJSUFEolEadBVdnY2JBIJDh06BAA4dOgQJBIJfvnlFzRs2BBSqRRHjx6FTCbDuHHjULFiRZibm6NFixY4ffq04j6vrouPj0e9evVgbm6Opk2b4tKlS0qxHT16FC1btoSFhQVcXV0xbtw45Obmvos/FiIiIr3Su3dvfP7555g9ezb8/PyQmJiIvXv3KgZkp6amIi0tTaPP1MnESBOmTZuGhQsX4sqVK6hXrx6mTJmCbdu2Yd26dTh37hw8PT0RFBSEBw8eKF03efJkLFmyBKdPn4ajoyO6dOmC/Px8AC/7N4ODg9GzZ09cuHABmzZtwtGjRzFmzBgxPiIREZFGiDFd/5UxY8bg9u3bkMlk+OOPP9CkSRPFe4cOHUJMTEyJ18bExGDnzp1qPU8nE6Off/4Z1tbWSseCBQvUuse8efPQoUMHVK9eHVKpFN9++y0+++wzdOzYEd7e3li1ahUsLCwQHR2tdF1kZCQ6dOiAunXrYt26dcjIyMCOHTsAAFFRUQgJCcGECRNQo0YNNGvWDF999RViY2Px/PlzjX1+IiKid0msla/FoJMDa9q2bYtvv/1Wqa18+fJ4/Phxqe/h7++v+Dk5ORn5+flo3ry5os3U1BSNGzfGlStXlK57fcBX+fLlUatWLcU5SUlJuHDhAtavX684RxAEyOVy3Lp1C15eXkXiKG5NB7kgh5FEJ3NWIiIinaaTiZGVlRU8PT2LtOfk5AB4mYy88qqbq7h7aFpOTg6GDx+OcePGFXnPzc2t2GuioqIwd+5cpTYXK1dUKldV4/ERERH9F2INvhaDXpUlHB0dAUBpIFZpVr+sXr06zMzMcOzYMUVbfn4+Tp8+DW9vb6VzT548qfj54cOHuH79uqIS1KBBA1y+fBmenp5FDjMzs2KfHRERgUePHikdztalm1JIRET0Log5xuhd08mKkUwmQ3p6ulKbiYkJKlSogKZNm2LhwoWoVq0aMjMzMXPmzDfez8rKCiNHjsTkyZNRvnx5uLm5YfHixXj69GmRvVbmzZsHBwcHODk5YcaMGahQoQK6desGAJg6dSqaNm2KMWPGYMiQIbCyssLly5exf/9+LF++vNhnF7cUOrvRiIiIxKGTidHevXvh4uKi1FarVi1cvXoVa9asweDBg9GwYUPUqlULixcvxnvvvffGey5cuBByuRz9+/fHkydP4O/vj3379sHe3r7IeePHj8dff/0FPz8/7N69W1ENqlevHg4fPowZM2agZcuWEAQB1atXR+/evTX34YmIiN4xXan2aIJEeH1ADpXo0KFDaNu2LR4+fKhYMKqs+Lu0LNP7a9rjF8/EDkEt5U2txQ5BLcMlVcQOQS22Bbr1T2jQGN2r0NZd+qfYIajFz0q3hgdcenpX7BDUcj3rTJk/Y6VrP43cZ8SdHzVyn7KkkxUjIiIiend06+vO29G9r0pEREREZYQVo1Jq06YN2OtIRESGyJAqRkyMiIiISCVDKguwK42IiIioECtGREREpJIhrXzNxIiIiIhUMqQxRuxKIyIiIirEihERERGpZEgVIyZGREREpBJnpREREREZIFaMiIiISCXOSiMiIiIqxDFGRERERIU4xoiIiIjIALFiRERERCrJDahmxMRIC+naX0AfCxexQ1CLnZGZ2CGoZcrD02KHoJaHz3LEDkEtbksrih2C2i7/OlfsENSyt9NGsUNQS5Na5cQOQesY0hgjdqURERERFWLFiIiIiFTSrX6Mt8PEiIiIiFRiVxoRERGRAWLFiIiIiFTiytdEREREhXRttvTbYFcaERERUSFWjIiIiEglw6kXMTEiIiKiNzCkWWlMjIiIiEgljjEiIiIiMkCsGBEREZFKhlMvYsVI4yQSCXbu3Cl2GERERBoj19ChC5gYERERERViVxoRERGpxMHXOkoul2Px4sXw9PSEVCqFm5sbPv30UwDAxYsX0a5dO1hYWMDBwQHDhg1DTk6O4to2bdpgwoQJSvfr1q0bwsLCFK/T0tLQuXNnWFhYoFq1aoiLi4O7uzuWLl2qdN29e/fQvXt3WFpaokaNGti1a1dZfWQiIqIyJ2jo0AV6lRhFRERg4cKFmDVrFi5fvoy4uDg4OTkhNzcXQUFBsLe3x+nTp7FlyxYcOHAAY8aMUev+AwYMwN27d3Ho0CFs27YN33//PTIzM4ucN3fuXHz00Ue4cOECOnXqhJCQEDx48EBTH5OIiIjKiN50pT158gTLli3D8uXLERoaCgCoXr06WrRogVWrVuH58+eIjY2FlZUVAGD58uXo0qULFi1aBCcnpzfe/+rVqzhw4ABOnz4Nf39/AMDq1atRo0aNIueGhYWhT58+AIAFCxbgq6++wqlTpxAcHKypj0tERPTO6MrAaU3Qm8ToypUrkMlkaN++fbHv+fr6KpIiAGjevDnkcjmuXbtWqsTo2rVrMDExQYMGDRRtnp6esLe3L3JuvXr1FD9bWVnBxsam2MoSAMhkMshkMqU2uSCHkUSvinlERKTDBJ3pCHt7evPb18LC4q2uNzIygiAo/x+fn5//n+5lamqq9FoikUAuLz7fjoqKgq2trdKRkfP3f3ouERERvR29SYxq1KgBCwsLJCQkFHnPy8sLSUlJyM3NVbQdO3YMRkZGqFWrFgDA0dERaWlpivcLCgpw6dIlxetatWrhxYsXOH/+vKLtxo0bePjw4VvFHRERgUePHikdTtZV3uqeREREmsR1jHSQubk5pk6diilTpiA2NhbJyck4efIkoqOjERISAnNzc4SGhuLSpUs4ePAgxo4di/79+yu60dq1a4f4+HjEx8fj6tWrGDlyJLKzsxX3r127NgIDAzFs2DCcOnUK58+fx7Bhw2BhYQGJRPKf45ZKpbCxsVE62I1GRETaRA5BI4cu0JsxRgAwa9YsmJiYYPbs2bh79y5cXFwwYsQIWFpaYt++fRg/fjwaNWoES0tL9OzZE1988YXi2kGDBiEpKQkDBgyAiYkJJk6ciLZt2yrdPzY2FoMHD0arVq3g7OyMqKgo/PnnnzA3N3/XH5WIiOid0Y2URjMkwr8H1lCp/f3333B1dcWBAweKHfT9XzVwaaGxe70LVU2LDkDXZnZGZmKHoJafH15680la5OGznDefpEXcbCqKHYLaLv86V+wQ1LK300axQ1BLE8+0N5+kRVyOHizzZ4x0/0gj9/k2ZbNG7lOW9KpiVNZ+++035OTkoG7dukhLS8OUKVPg7u6OVq1aiR0aERFRmdGVbjBNYGKkhvz8fEyfPh03b95EuXLl0KxZM6xfv77ILDQiIiJ9oisDpzWBiZEagoKCEBQUJHYYREREVEaYGBEREZFKhrTAIxMjIiIiUsmQutK4YA4RERFRIVaMiIiISCV2pREREREVYlcaERERkQFixYiIiIhUkhvQJhlMjIiIiEglw0mLmBgRERHRGxjSliAcY0RERERUiBUjIiIiUsmQpuuzYkREREQqyTV0/BcrVqyAu7s7zM3N0aRJE5w6darEc1etWoWWLVvC3t4e9vb2CAwMVHl+cZgYERERkVbatGkTwsPDERkZiXPnzsHX1xdBQUHIzMws9vxDhw6hT58+OHjwIE6cOAFXV1e89957+Oeff0r9TIkgGNAcPB3RqnJ7sUNQi7WxVOwQ9NqV3NL/B60NnM3Lix2CWlxMyokdgtoGyKzFDkEtnY5PEDsEtWxu8ZXYIail390fy/wZvap+oJH7bLn9k1rnN2nSBI0aNcLy5csBAHK5HK6urhg7diymTZv2xusLCgpgb2+P5cuXY8CAAaV6JscYERERkUqaGmMkk8kgk8mU2qRSKaTSol+w8/LycPbsWURERCjajIyMEBgYiBMnTpTqeU+fPkV+fj7Kly/9FzZ2pREREdE7ERUVBVtbW6UjKiqq2HPv3buHgoICODk5KbU7OTkhPT29VM+bOnUqKlWqhMDAwFLHyIoRERERqaSpvdIiIiIQHh6u1FZctUgTFi5ciI0bN+LQoUMwNzcv9XVMjIiIiEglTQ1HLqnbrDgVKlSAsbExMjIylNozMjLg7Oys8trPP/8cCxcuxIEDB1CvXj21YmRXGhEREWkdMzMzNGzYEAkJCYo2uVyOhIQEBAQElHjd4sWLMX/+fOzduxf+/v5qP5cVIyIiIlJJrC1BwsPDERoaCn9/fzRu3BhLly5Fbm4uBg4cCAAYMGAAKleurBintGjRIsyePRtxcXFwd3dXjEWytraGtXXpZnMyMSIiIiKVNDXGSF29e/dGVlYWZs+ejfT0dPj5+WHv3r2KAdmpqakwMvr/zq9vv/0WeXl5+PDDD5XuExkZiTlz5pTqmUyMiIiISCUxtwQZM2YMxowZU+x7hw4dUnqdkpLy1s/jGCMiIiKiQqwYERERkUpijTESAxMjIiIiUsmQdg9jVxoRERFRIVaMiIiISCWxZqWJwSAqRoIgYNiwYShfvjwkEgkSExPFDomIiEhnCBr6ny4wiIrR3r17ERMTg0OHDsHDwwMVKlQQOyQiIiLSQgaRGCUnJ8PFxQXNmjUTLYa8vDyYmZmJ9nwiIqL/ypBmpel9V1pYWBjGjh2L1NRUSCQSuLu7Qy6XIyoqCtWqVYOFhQV8fX2xdetWAC/3YalSpQq+/fZbpfucP38eRkZGuH37NgAgOzsbQ4YMgaOjI2xsbNCuXTskJSUpzp8zZw78/PywevVqVKtWTa2dfYmIiLSJIAgaOXSB3idGy5Ytw7x581ClShWkpaXh9OnTiIqKQmxsLFauXIk///wTEydORL9+/XD48GEYGRmhT58+iIuLU7rP+vXr0bx5c1StWhUA0KtXL2RmZuKXX37B2bNn0aBBA7Rv3x4PHjxQXHPjxg1s27YN27dv57gmIiIiHaD3XWm2trYoV64cjI2N4ezsDJlMhgULFuDAgQOK3Xk9PDxw9OhRfPfdd2jdujVCQkKwZMkSpKamws3NDXK5HBs3bsTMmTMBAEePHsWpU6eQmZkJqVQKAPj888+xc+dObN26FcOGDQPwsvssNjYWjo6OJcYnk8kgk8mU2uSCHEYSvc9ZiYhIR7ArTY/duHEDT58+RYcOHRS77VpbWyM2NhbJyckAAD8/P3h5eSmqRocPH0ZmZiZ69eoFAEhKSkJOTg4cHByU7nHr1i3FPQCgatWqKpMiAIiKioKtra3ScedJStl8eCIiov+As9L0WE5ODgAgPj4elStXVnrvVfUHAEJCQhAXF4dp06YhLi4OwcHBcHBwUNzDxcWlyOZ1AGBnZ6f42crK6o3xREREIDw8XKmtU+0PSvtxiIiIypxcR8YHaYLBJUbe3t6QSqVITU1F69atSzyvb9++mDlzJs6ePYutW7di5cqVivcaNGiA9PR0mJiYwN3d/a3ikUqlSgkZAHajERERicTgEqNy5cph0qRJmDhxIuRyOVq0aIFHjx7h2LFjsLGxQWhoKADA3d0dzZo1w+DBg1FQUICuXbsq7hEYGIiAgAB069YNixcvRs2aNXH37l3Ex8eje/fu8Pf3F+vjERERaZzh1IsMMDECgPnz58PR0RFRUVG4efMm7Ozs0KBBA0yfPl3pvJCQEIwaNQoDBgyAhYWFol0ikWDPnj2YMWMGBg4ciKysLDg7O6NVq1ZwcnJ61x+HiIioTBnS4GuJoCsLCxiQVpXbix2CWqyNpW8+if6zK7n/iB2CWpzNy4sdglpcTMqJHYLaBsisxQ5BLZ2OTxA7BLVsbvGV2CGopd/dH8v8Gc0rt9PIfY7985tG7lOWDLJiRERERKVnSBUjJkZERESkkiF1LnH6ExEREVEhVoyIiIhIJXalERERERXSlVWrNYGJEREREanEMUZEREREBogVIyIiIlKJY4yIiIiICrErjYiIiMgAsWJEREREKrErjYiIiKiQIU3XZ1caERERUSFWjIiIiEgluQENvmZiRERERCoZUlcaEyMtlJaXLXYIavE0ryh2CGpJzXsgdgh6LTs/V+wQ1HI/74nYIaitSa1yYoegls0tvhI7BLX0vjBP7BBIREyMiIiISCV2pREREREVYlcaERERUSFDqhhxuj4RERFRIVaMiIiISCV2pREREREVYlcaERERkQFixYiIiIhUYlcaERERUSFBkIsdwjvDrjQiIiKiQqwYERERkUpydqURERERvSRwVhoRERGR4dHbxOjQoUOQSCTIzs4WOxQiIiKdJoegkUMX6E1i1KZNG0yYMEHsMBTc3d2xdOlSscMgIiJ6a4IgaOTQBRxj9Jq8vDyYmZmJHQYREZFW4crXOiYsLAyHDx/GsmXLIJFIIJFIkJKSAgA4e/Ys/P39YWlpiWbNmuHatWuK6+bMmQM/Pz+sXr0a1apVg7m5OQAgNTUVH3zwAaytrWFjY4OPPvoIGRkZiuuSk5PxwQcfwMnJCdbW1mjUqBEOHDigeL9Nmza4ffs2Jk6cqIiHiIiItJ9eJEbLli1DQEAAhg4dirS0NKSlpcHV1RUAMGPGDCxZsgRnzpyBiYkJBg0apHTtjRs3sG3bNmzfvh2JiYmQy+X44IMP8ODBAxw+fBj79+/HzZs30bt3b8U1OTk56NSpExISEnD+/HkEBwejS5cuSE1NBQBs374dVapUwbx58xTxEBER6SpBQ//TBXrRlWZrawszMzNYWlrC2dkZAHD16lUAwKefforWrVsDAKZNm4bOnTvj+fPniupQXl4eYmNj4ejoCADYv38/Ll68iFu3bimSq9jYWPj4+OD06dNo1KgRfH194evrq3j+/PnzsWPHDuzatQtjxoxB+fLlYWxsjHLlyiniKYlMJoNMJlNqEwQ5JBK9yFmJiEgP6Mr4IE3Q+9++9erVU/zs4uICAMjMzFS0Va1aVZEUAcCVK1fg6uqqSIoAwNvbG3Z2drhy5QqAlxWjSZMmwcvLC3Z2drC2tsaVK1cUFSN1REVFwdbWVul48DRd7fsQERHR29P7xMjU1FTx86uxPnL5/+/5YmVlpfY9J02ahB07dmDBggX4/fffkZiYiLp16yIvL0/te0VERODRo0dKR3lL1VUmIiKid8mQpuvrRVcaAJiZmaGgoOCt7+Pl5YU7d+7gzp07iqrR5cuXkZ2dDW9vbwDAsWPHEBYWhu7duwN4WUF6Ndhb3XikUimkUqlSG7vRiIhIm7ArTQe5u7vjjz/+QEpKCu7du6dUFVJHYGAg6tati5CQEJw7dw6nTp3CgAED0Lp1a/j7+wMAatSooRisnZSUhL59+xZ5nru7O44cOYJ//vkH9+7de+vPR0RERGVPbxKjSZMmwdjYGN7e3nB0dPxP432Al91tP/30E+zt7dGqVSsEBgbCw8MDmzZtUpzzxRdfwN7eHs2aNUOXLl0QFBSEBg0aKN1n3rx5SElJQfXq1ZXGMBEREekauSBo5NAFEsGQ6mM6ooZjQ7FDUIuneUWxQ1BLat4DsUNQy9MXz8UOQS3mxtI3n6RFCoT/Vl0W0+Fa5cQOQS0JNyuLHYJael+YJ3YIajGt4FHmz7C39tTIfR7m3NDIfcqS3lSMiIiIiN6W3gy+JiIiorKhKzPKNIGJEREREalkSKNu2JVGREREKok5+HrFihVwd3eHubk5mjRpglOnTqk8f8uWLahduzbMzc1Rt25d7NmzR63nMTEiIiIirbRp0yaEh4cjMjIS586dg6+vL4KCgpR2sHjd8ePH0adPHwwePBjnz59Ht27d0K1bN1y6dKnUz+SsNC3EWWlli7PSyhZnpZU9zkorW5yVVpSVpbtG7pP7NEWt85s0aYJGjRph+fLlAF7uXOHq6oqxY8di2rRpRc7v3bs3cnNz8fPPPyvamjZtCj8/P6xcubJUz2TFiIiIiFTSVFeaTCbD48ePlY5/b6T+Sl5eHs6ePYvAwEBFm5GREQIDA3HixIlirzlx4oTS+QAQFBRU4vnFYWJERERE70RxG6dHRUUVe+69e/dQUFAAJycnpXYnJyekpxe/2Xp6erpa5xeHs9KIiIhIJU2NuomIiEB4eLhS27/3CxUbEyMiIiJSSdDQOkbFbZxekgoVKsDY2BgZGRlK7RkZGXB2di72GmdnZ7XOLw670oiIiEjrmJmZoWHDhkhISFC0yeVyJCQkICAgoNhrAgIClM4HgP3795d4fnFYMSIiIiKVxJrAHh4ejtDQUPj7+6Nx48ZYunQpcnNzMXDgQADAgAEDULlyZcU4pfHjx6N169ZYsmQJOnfujI0bN+LMmTP4/vvvS/1MJkZERESkkliJUe/evZGVlYXZs2cjPT0dfn5+2Lt3r2KAdWpqKoyM/r/zq1mzZoiLi8PMmTMxffp01KhRAzt37kSdOnVK/UyuY6SFuI5R2eI6RmWL6xiVPa5jVLa4jlExzzDTzP+H+Xn/aOQ+ZYkVIyIiIlLJoCooAhmE58+fC5GRkcLz58/FDqVUGG/ZYrxlS9fiFQTdi5nxUllhV5qBePz4MWxtbfHo0SPY2NiIHc4bMd6yxXjLlq7FC+hezIyXygqn6xMREREVYmJEREREVIiJEREREVEhJkYGQiqVIjIyUuv2pCkJ4y1bjLds6Vq8gO7FzHiprHDwNREREVEhVoyIiIiICjExIiIiIirExIiIiIioEBMjIiIiokJMjIiIiIgKcRNZPXbnzh1IJBJUqVIFAHDq1CnExcXB29sbw4YNEzm6N3vx4gWeP38Oa2trsUMh0kvZ2dk4deoUMjMzIZfLld4bMGCASFEVlZ+fDwsLCyQmJqJOnTpih1MqxsbGSEtLQ8WKFZXa79+/j4oVK6KgoECkyOhNmBjpsb59+2LYsGHo378/0tPT0aFDB/j4+GD9+vVIT0/H7NmzxQ4RALB7927cv38fYWFhirZPP/0U8+fPx4sXL9CuXTts2rQJ9vb24gVZSocPH0Zubi4CAgK0Mt7Q0FAMHjwYrVq1EjuUNyooKEBMTAwSEhKK/cX922+/iRTZm+Xl5eHWrVuoXr06TEy085/Z3bt3IyQkBDk5ObCxsYFEIlG8J5FItCoxMjU1hZubm04lEyWthCOTyWBmZvaOoyF1aOd/saQRly5dQuPGjQEAmzdvRp06dXDs2DH8+uuvGDFihNYkRl988QU+/PBDxevjx49j9uzZmDdvHry8vDBjxgzMnz8fX3zxhYhRKlu0aBFycnIwf/58AC//EezYsSN+/fVXAEDFihWRkJAAHx8fMcMs4tGjRwgMDETVqlUxcOBAhIaGonLlymKHVazx48cjJiYGnTt3Rp06dZR+cWurp0+fYuzYsVi3bh0A4Pr16/Dw8MDYsWNRuXJlTJs2TeQI/98nn3yCQYMGYcGCBbC0tBQ7nDeaMWMGpk+fjh9++AHly5cXO5wSffXVVwBeJperV69WqngXFBTgyJEjqF27tljhUWkIpLesrKyEW7duCYIgCF26dBEWLlwoCIIg3L59WzA3NxcxMmWOjo7CuXPnFK8nTpwoBAUFKV7Hx8cLnp6eYoRWovr16wsbN25UvN68ebNgYWEhHD16VLh//77QuXNnoVevXiJGWLLMzExhyZIlQr169QQTExMhODhY2LJli5CXlyd2aEocHByE+Ph4scNQy7hx44SGDRsKv//+u2BlZSUkJycLgiAIO3fuFPz8/ESOTpmlpaUiPl3g5+cnWFtbC1KpVKhZs6ZQv359pUNbuLu7C+7u7oJEIhFcXV0Vr93d3YWaNWsK7733nnDy5EmxwyQVWDHSYz4+Pli5ciU6d+6M/fv3K6obd+/ehYODg8jR/b8nT54oxXP06FH06tVL8drHxwd3794VI7QS3bp1C/Xq1VO83rNnDz788EM0b94cADBz5kylz6BNHB0dER4ejvDwcJw7dw5r165F//79YW1tjX79+mHUqFGoUaOG2GHCzMwMnp6eYoehlp07d2LTpk1o2rSpUoXLx8cHycnJIkZWVFBQEM6cOQMPDw+xQymVbt26iR1Cqdy6dQsA0LZtW2zfvl0ru9RJNSZGemzRokXo3r07PvvsM4SGhsLX1xcAsGvXLkUXmzaoXLkyrly5Ajc3N+Tk5CApKQlffvml4v379+9rXan/xYsXSnsenThxAhMmTFC8rlSpEu7duydCZKWXlpaG/fv3Y//+/TA2NkanTp1w8eJFeHt7Y/HixZg4caKo8X3yySdYtmwZli9frhPdaACQlZVVZLAtAOTm5mrFZ9i1a5fi586dO2Py5Mm4fPky6tatC1NTU6Vzu3bt+q7DUykyMlLsENRy8OBBsUOg/4iJkR5r06YN7t27h8ePHyt9axk2bJhWJRq9evXChAkTMH36dOzZswfOzs5o2rSp4v0zZ86gVq1aIkZYVPXq1XHkyBF4eHggNTUV169fVxrQ/Pfff2tVVe6V/Px87Nq1C2vXrsWvv/6KevXqYcKECejbty9sbGwAADt27MCgQYNET4yOHj2KgwcP4pdffoGPj0+RX9zbt28XKbKS+fv7Iz4+HmPHjgUARTK0evVqBAQEiBkagOKrLvPmzSvSJpFItHKgc3Z2NrZu3Yrk5GRMnjwZ5cuXx7lz5+Dk5KSVY+X+/vtv7Nq1C6mpqcjLy1N6T5vGTJIyJkZ6ztjYuEgp193dXZxgSjB79mz8888/GDduHJydnfHjjz/C2NhY8f6GDRvQpUsXESMsavTo0RgzZgx+//13nDx5EgEBAfD29la8/9tvv6F+/foiRlg8FxcXyOVy9OnTB6dOnYKfn1+Rc9q2bQs7O7t3Htu/2dnZoXv37mKHoZYFCxagY8eOuHz5Ml68eIFly5bh8uXLOH78OA4fPix2eEVm9umSCxcuIDAwELa2tkhJScHQoUNRvnx5bN++HampqYiNjRU7RCUJCQno2rUrPDw8cPXqVdSpUwcpKSkQBAENGjQQOzxSQSIIJcwpJJ1XrVo1leX7mzdvvsNo9M+aNWuwe/duODs7IzIyEs7Ozor3Ro0ahcDAQPTo0UPECIv64Ycf0KtXL5ibm4sdit5KTk7GwoULkZSUhJycHDRo0ABTp05F3bp1xQ5NpwUGBqJBgwZYvHgxypUrh6SkJHh4eOD48ePo27cvUlJSxA5RSePGjdGxY0fMnTtXEW/FihUREhKC4OBgjBw5UuwQqQRMjPTYsmXLlF7n5+fj/Pnz2Lt3LyZPnqxVU4dVef78OZYvX45JkyaJHQqR3ng1rfzfJBIJzM3N4enpiVatWilVb8Vka2uLc+fOoXr16kqJ0e3bt1GrVi08f/5c7BCVlCtXDomJiahevTrs7e1x9OhR+Pj4ICkpCR988IHWJXL0/9iVpsfGjx9fbPuKFStw5syZdxyNallZWfjjjz9gZmaG9u3bw9jYGPn5+fjmm28QFRWFFy9e6FRidO7cOcyePRs///yz2KGoVbUSe9xOgwYNkJCQAHt7e9SvX19lxfPcuXPvMLLSKygowI4dO3DlyhUAgLe3Nz744AOtW+jxyy+/RFZWFp4+farobn/48CEsLS1hbW2NzMxMeHh44ODBg3B1dRU5WkAqleLx48dF2q9fvw5HR0cRIlLNyspKMa7IxcUFycnJinXNtH1ihqHTrv9S6Z3o2LEjIiIisHbtWrFDAfBykO3777+Px48fQyKRwN/fH2vXrkW3bt1gYmKCOXPmIDQ0VOwwi9i3bx/2798PMzMzDBkyRDGWYNq0adi9ezeCgoLEDhHAy2/auuKDDz5QzPbTlenZr/vzzz/RtWtXpKenKyYMLFq0CI6Ojti9e7dWbWexYMECfP/991i9ejWqV68OALhx4waGDx+OYcOGoXnz5vj4448xceJEbN26VeRoX86SmzdvHjZv3gzgZWUrNTUVU6dORc+ePUWOrqimTZvi6NGj8PLyQqdOnfDJJ5/g4sWL2L59u9LkEtJCoq6iRKJYtGiRULVqVbHDUGjdurXQp08f4eLFi8KkSZMEiUQi1KxZU9iyZYvYoZVo9erVgkQiERwcHAQjIyPB0dFR+OGHHwQ7Ozth+PDhwuXLl8UOkUTQtGlToUuXLsKDBw8UbQ8ePBC6du0qBAQEiBhZUR4eHsL58+eLtJ87d06oVq2aIAiCcOzYMcHZ2fkdR1a87OxsITAwULCzsxOMjY0FV1dXwdTUVGjVqpWQk5MjdnhFJCcnC0lJSYIgCEJOTo4wfPhwoW7dukKPHj2ElJQUkaMjVTjGSI/9uytCEASkp6cjKysL33zzjdZsJOvg4IDff/8d3t7eePbsGaytrbF9+3Z88MEHYodWonr16qF///6YPHkytm3bhl69eqFp06bYvHmzYtNeMjwWFhY4c+ZMka1gLl26hEaNGuHZs2ciRVaUpaUljhw5An9/f6X206dPo3Xr1nj69ClSUlJQp04d5OTkiBRlUUePHsWFCxcUA9sDAwPFDon0DLvS9Ni/uyKMjIzg6OiINm3aaNVePQ8fPkSFChUAvPzFYmlpqVVdDsVJTk5WrGzdo0cPmJiY4LPPPtOJpGjr1q3YvHlzsWuraNO4nYKCAnz55ZclxvrgwQORIitZzZo1kZGRUSQxyszM1LpVvNu2bYvhw4dj9erViqUlzp8/j5EjR6Jdu3YAgIsXL6JatWpihqlw584duLq6okWLFmjRooXY4ZTa2bNnFePNfHx8tHIZD/oXkStWVEby8/OFdevWCenp6WKH8kYSiUQ4ePCgkJSUJCQlJQlWVlZCfHy84vWrQ5tIJBIhIyND8dra2lon9p1atmyZYG1tLYwZM0YwMzMThg8fLgQGBgq2trbC9OnTxQ5PyaxZswQXFxfh888/F8zNzYX58+cLgwcPFhwcHIRly5aJHV6x4uPjBR8fH2HLli3CnTt3hDt37ghbtmwR6tatK8THxwuPHj1SHGJLS0sTAgMDBYlEIpiZmQlmZmaCkZGR0KFDB8W/G7/99puwb98+kSN9ycjISGjVqpXw/fffK3VVaquMjAyhbdu2gkQiEezt7QV7e3tBIpEI7dq1EzIzM8UOj1RgV5oes7S0xJUrV1C1alWxQ1HJyMgIEokExf1VfNWubSvxGhkZ4X//+59i5+ypU6di8uTJisrXK+PGjRMjvBLVrl0bkZGR6NOnj9KU59mzZ+PBgwdYvny52CEqVK9eHV999RU6d+6sNPX5q6++wsmTJxEXFyd2iEUYGRkpfn7Vjf3q7/Xrr7Xp7/PVq1dx/fp1AECtWrW0bpX5V86fP4+4uDhs3LgRWVlZCA4ORr9+/dClSxel7Xm0Re/evXHz5k3ExsbCy8sLAHD58mWEhobC09MTGzZsEDlCKgkTIz3Wpk0bTJgwQetn99y+fbtU52lTgufu7v7Gva8kEonWLaL5erJcsWJF7N+/H76+vvjrr7/QtGlT3L9/X+wQFaysrBR76Lm4uCA+Ph4NGjTAzZs3Ub9+fTx69EjsEItQZ3Xr1q1bl2Ek+ksQBBw6dAhxcXHYtm0b5HI5evTogTVr1ogdmhJbW1scOHAAjRo1Umo/deoU3nvvPWRnZ4sTGL0RxxjpsVGjRuGTTz7B33//jYYNG8LKykrp/dd3hxdTaRKeS5cuvYNISk9XF2dzdnbGgwcPULVqVbi5ueHkyZPw9fXFrVu3iq3YialKlSpIS0uDm5sbqlevjl9//RUNGjTA6dOntbJCAOhWsjNo0CCV72tbovGKRCJB27Zt0bZtW4wcORKDBw/GunXrtC5euVxeZH8/ADA1NdXprVkMARMjPfbxxx8DUO7O0dauqeI8efIEGzZswOrVq3H27Fmtj1cXtGvXDrt27UL9+vUxcOBAxRo1Z86c0brtS7p3746EhAQ0adIEY8eORb9+/RAdHY3U1FTRN7hVJTs7G9HR0UoDbgcNGqR160k9fPhQ6XV+fj4uXbqE7OxsxeBrbfT3338jLi4OcXFxuHTpEgICArBixQqxwyqiXbt2GD9+PDZs2IBKlSoBAP755x9MnDgR7du3Fzk6UoVdaXrsTV1U2tQ19bojR44gOjoa27ZtQ6VKldCjRw/07NmzSElaTJ06dcKGDRsUv+wWLlyIESNGKDZfvX//Plq2bInLly+LGGVRcrkccrlcsQrzxo0bcfz4cdSoUQPDhw+HmZmZyBGW7MSJEzhx4gRq1KihdZsKv3LmzBkEBQXBwsICjRs3BvBy+vuzZ88UFS9tJpfLMXLkSFSvXh1TpkwROxwl3333HeLi4nDs2DHUrl0bISEh6Nu3r9b+O3bnzh107doVf/75p2Ll8Dt37qBOnTrYtWuXTsxgNVRMjEgrpKenIyYmBtHR0Xj8+DE++ugjrFy5EklJSUq71msLY2NjpKWloWLFigAAGxsbJCYmwsPDAwCQkZGBSpUqaV2VKzU1Fa6urkXGRwmCgDt37sDNzU2kyPRDy5Yt4enpiVWrVimSzxcvXmDIkCG4efMmjhw5InKEb3bt2jW0adMGaWlpYoeixNXVFX369EFISAh8fX3FDqdUBEHAgQMHcPXqVQCAl5cX113SAexK0zO7du1Cx44dYWpqil27dqk8t2vXru8oKtW6dOmCI0eOoHPnzli6dCmCg4NhbGyMlStXih1aif79fUJXvl9Uq1ZNKaF75cGDB6hWrZrWJXLXrl3D119/reiW8vLywtixY7V25tSZM2eUkiIAMDExwZQpU4ospKitkpOT8eLFC7HDKCI1NfWNEx60RX5+PiwsLJCYmIgOHTqgQ4cOYodEamBipGe6deuG9PR0VKxYUeVsNG0aY/TLL79g3LhxGDlyJGrUqCF2OHrt1fiyf8vJyYG5ubkIEZVs27Zt+Pjjj+Hv74+AgAAAwMmTJ1GnTh1s3LhRK/fHsrGxQWpqapEFVO/cuYNy5cqJFFXxwsPDlV4LgoC0tDTEx8dr5d6EEokEv//+O7777jskJydj69atqFy5Mn744QdUq1ZNqxZ9NDU1hZubm9b8G0vqYWKkZ16f7aArMx+OHj2K6OhoNGzYEF5eXujfv79i4Li2kkgkRRIMbf42++qXoEQiwaxZs2Bpaal4r6CgAH/88Qf8/PxEiq54U6ZMQUREBObNm6fUHhkZiSlTpmhlYtS7d28MHjwYn3/+OZo1awYAOHbsGCZPnow+ffqIHJ2y8+fPK71+tTL+kiVL3jhjTQzbtm1D//79ERISgvPnz0MmkwEAHj16hAULFmDPnj0iR6hsxowZmD59On744QeUL19e7HBIDRxjpMdeLaGvK3Jzc7Fp0yasWbMGp06dQkFBAb744gsMGjRI675tGxkZoWPHjopp47t370a7du0USyLIZDLs3btXa74xtm3bFsDLdXYCAgKUBlmbmZnB3d0dkyZN0qqKnaWlJS5cuFBkK42//voLvr6+ePr0qUiRlSwvLw+TJ0/GypUrFd1RpqamGDlyJBYuXKi1ywzogvr162PixIkYMGCA0uKk58+fR8eOHZGeni52iErq16+PGzduID8/H1WrVi2yXIo2bb9DypgY6TFjY2O0aNEC/fr1w4cffgh7e3uxQyq1a9euITo6Gj/88AOys7PRoUOHN46ZepfCwsJKVSFau3btO4im9AYOHIivvvpK6xLN4nTq1Am9evXCwIEDldrXrl2LjRs3Yt++fSJFVryCggIcO3YMdevWhVQqRXJyMoCXK3i/XqHTNllZWbh27RqAlytfOzo6ihxR8SwtLXH58mW4u7srJUY3b96Et7c3nj9/LnaISubOnavy/cjIyHcUCamLiZEe07Ul9ItTUFCAn3/+GdHR0VqVGOma0q5RtH379jKOpPRWrlyJ2bNn46OPPkLTpk0BvBxjtGXLFsydO1exNgygPRMJzM3NceXKFa3ZeFWV3NxcjB07FrGxsYpud2NjYwwYMABff/211iVzHh4e+P777xEYGKiUGMXGxmLhwoVatzQG6S4mRgZA25fQP3HiBO7fv4/3339f0RYbG4vIyEjk5uaiW7du+Prrr7UqmSvNGAyJRILo6Oh3EM2b/bvqUhJtqnC9vu+YKto0kcDf3x+LFi3SiQX8hg8fjgMHDmD58uVo3rw5gJfj/caNG4cOHTrg22+/FTlCZVFRUfjxxx+xZs0adOjQAXv27MHt27cxceJEzJo1C2PHjhU7xGLl5eUhMzOzyJhPLo2hvZgYGZhz585h8ODBuHDhgtb8MunYsSPatGmDqVOnAgAuXryIBg0aICwsDF5eXvjss88wfPhwzJkzR9xAX2NkZISqVauifv36Kqfq79ix4x1GRWLbu3cvIiIiMH/+/GK34bGxsREpsqIqVKiArVu3ok2bNkrtBw8exEcffYSsrCxxAnvNhQsXUKdOHUWS/OmnnyIqKkoxvkwqlWLSpEmYP3++mGEW6/r16xg8eDCOHz+u1K4rOw8YMiZGBqC4JfRDQkIwYsQIsUMDALi4uGD37t2KdV5mzJiBw4cP4+jRowCALVu2IDIyUqtK5aNHj8aGDRtQtWpVDBw4EP369ePMEw3RxQriK69XuV4fg6aNvwwtLS1x9uxZxc7vr/z5559o3LgxcnNzRYrs/72+kKqHhwdOnz6NcuXK4caNG8jJyYG3tzesra3FDrNYzZs3h4mJCaZNmwYXF5ciYxJ1ZZFKQ8TESI/pyhL65ubm+OuvvxQz6Fq0aIGOHTtixowZAF5u2Fq3bl08efJEzDCLkMlk2L59O9asWYPjx4+jc+fOGDx4MN577z2tnrqv7XSxgvjK4cOHVb6vTZvMtm/fHg4ODoiNjVWsYfXs2TOEhobiwYMHOHDggMgRAg4ODtizZw+aNGkCIyMjZGRkaO3g8H+zsrLC2bNni6xpRdqPiZEe05Ul9KtWrYoffvgBrVq1Ql5eHuzs7LB7927FOI2LFy+idevWePDggciRluz27duIiYlBbGwsXrx4gT///FNrv8lqO12sIOqiixcvIjg4GDKZTPHvQ1JSEqRSKX799Vf4+PiIHCEwbNgwxMbGwsXFBampqahSpQqMjY2LPffmzZvvODrVGjVqhC+//FKrFp6k0uECj3pMV5bQ79SpE6ZNm4ZFixZh586dsLS0RMuWLRXvX7hwAdWrVxcxwjczMjKCRCKBIAha1V2iix4+fAgnJyfF68OHD6Njx46K140aNcKdO3fECO2N3rQXWqtWrd5RJG9Wt25d/PXXX1i/fr1iL69XX6QsLCxEju6l77//Hj169MCNGzcwbtw4DB06VKuXmnj8+LHi50WLFmHKlClYsGAB6tatC1NTU6VztWm8GSljYqTHXiVFT58+RWpqKvLy8pTer1evnhhhFTF//nz06NEDrVu3hrW1NdatW6e0AOGaNWvw3nvviRhh8V7vSjt69Cjef/99LF++HMHBwaWeUUVFOTk54datW3B1dUVeXh7OnTuntCbMkydPivyS0Rb/HsgMKI810qakOSoqCk5OThg6dKhS+5o1a5CVlaXoyhRbcHAwAODs2bMYP368VidGdnZ2RcaW/XuGojaONyNlTIz0WFZWFsLCwrB3795i39eW/zArVKiAI0eO4NGjR7C2ti5SKt+yZYvWdUuNGjUKGzduhKurKwYNGoQNGzagQoUKYoelF3S5gvjw4UOl1/n5+Th//jxmzZqFTz/9VKSoivdqDOK/+fj44OOPP9aaxOgVbVpKoiQHDx5U/JySkgJXV9ci/57J5XKkpqa+69BIDRxjpMdCQkJw+/ZtLF26FG3atMGOHTuQkZGB//3vf1iyZAk6d+4sdog6y8jICG5ubqhfv77K7kptWjBRV9y7dw89evTA0aNHFRXE7t27K95v3749mjZtqnWJhiqHDx9GeHg4zp49K3YoCiUtRqmtK0nrmtdn1L3u/v37qFixotZ8MaWiWDHSY7/99ht++ukn+Pv7K9bd6dChA2xsbBAVFcXE6C0MGDBAJ8Zv6SJdrCC+iZOTk2LbDW3h6uqKY8eOFUmMjh07prSqOP03r7rM/i0nJ0cxC5C0ExMjPZabm6v4tmJvb4+srCzUrFkTdevW5QaGbykmJkbsEPSera1tse3avF7UhQsXlF4LgoC0tDQsXLgQfn5+4gRVgqFDh2LChAnIz89Hu3btAAAJCQmYMmUKPvnkE5Gj013h4eEAXo4tmzVrltLWKgUFBfjjjz+07u8CKWNipMdq1aqFa9euwd3dHb6+vvjuu+/g7u6OlStXwsXFRezwiPSOn5+fYnbi65o2bao1W/C8MnnyZNy/fx+jRo1STMwwNzfH1KlTERERIXJ0uuv8+fMAXibFFy9eVJpIYmZmBl9fX0yaNEms8KgUOMZIj/3444948eIFwsLCcPbsWQQHB+PBgwcwMzNDTEwMevfuLXaIRHrl9u3bSq+NjIzg6Oio1V0nOTk5uHLlCiwsLFCjRg2tXFFcFw0cOBDLli3jtHwdxMTIgDx9+hRXr16Fm5sbZ1ARaVCnTp2wYcMGRfffwoULMWLECNjZ2QF4OeC2ZcuWXJSSSAcwMSIiekv/noFkY2ODxMREeHh4AAAyMjJQqVIlzkQi0gEcY6THCgoKEBMTg4SEBGRmZkIulyu9/9tvv4kUGZF++ff3S37fJNJdTIz02Pjx4xETE4POnTujTp06nF5ORET0BkyM9NjGjRuxefNmdOrUSexQiPSaRCIp8sWDX0SIdBMTIz1mZmYGT09PscMg0nuCICAsLEwxo+v58+cYMWIErKysALzcV4+IdAMHX+uxJUuW4ObNm1i+fDm/vRKVoYEDB5bqPF3Y74vI0DEx0mPdu3fHwYMHUb58efj4+BTZkZz7eBERESljV5oes7OzU9p8k4iIiFRjxYiIiIioECtGBiArK0uxs3etWrXg6OgockRERETayUjsAKjs5ObmYtCgQXBxcUGrVq3QqlUrVKpUCYMHD8bTp0/FDo+IiEjrMDHSY+Hh4Th8+DB2796N7OxsZGdn46effsLhw4fxySefiB0eERGR1uEYIz1WoUIFbN26FW3atFFqP3jwID766CNkZWWJExgREZGWYsVIjz19+hROTk5F2itWrMiuNCIiomKwYqTH2rdvDwcHB8TGxsLc3BwA8OzZM4SGhuLBgwc4cOCAyBESERFpFyZGeuzixYsIDg6GTCaDr68vACApKQnm5ubYt28ffHx8RI6QiIhIuzAx0nNPnz7F+vXrcfXqVQCAl5cXQkJCYGFhIXJkRERE2oeJkZ7Kz89H7dq18fPPP8PLy0vscIiIiHQCB1/rKVNTUzx//lzsMIiIiHQKEyM9Nnr0aCxatAgvXrwQOxQiIiKdwK40Pda9e3ckJCTA2toadevWhZWVldL727dvFykyIiIi7cS90vSYnZ0devbsKXYYREREOoMVIyIiIqJCrBgZgKysLFy7dg0AUKtWLTg6OoocERERkXbi4Gs9lpubi0GDBsHFxQWtWrVCq1atUKlSJQwePJhbghARERWDiZEeCw8Px+HDh7F7925kZ2cjOzsbP/30Ew4fPoxPPvlE7PCIiIi0DscY6bEKFSpg69ataNOmjVL7wYMH8dFHHyErK0ucwIiIiLQUK0Z67OnTp3BycirSXrFiRXalERERFYMVIz3Wvn17ODg4IDY2Fubm5gCAZ8+eITQ0FA8ePMCBAwdEjpCIiEi7MDHSYxcvXkRwcDBkMhl8fX0BAElJSZBKpfj111/h4+MjcoRERETahYmRnnv69CnWr1+Pq1evAgC8vLwQEhICCwsLkSMjIiLSPkyM9FhUVBScnJwwaNAgpfY1a9YgKysLU6dOFSkyIiIi7cTB13rsu+++Q+3atYu0+/j4YOXKlSJEREREpN2YGOmx9PR0uLi4FGl3dHREWlqaCBERERFpNyZGeszV1RXHjh0r0n7s2DFUqlRJhIiIiIi0G/dK02NDhw7FhAkTkJ+fj3bt2gEAEhISMGXKFK58TUREVAwOvtZjgiBg2rRp+Oqrr5CXlwcAMDc3x9SpUzF79myRoyMiItI+TIwMQE5ODq5cuQILCwvUqFEDUqlU7JCIiIi0EhMjIiIiokIcfE1ERERUiIkRERERUSEmRkRERESFmBgRERERFWJiRERERFSIiRERERFRISZGRERERIWYGBEREREV+j8gtOhSkZ8IBAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the inner product between two embedding vectors as the similarity measure\n",
    "def plot_correlation(labels, features):\n",
    "  corr = np.inner(features, features)\n",
    "  corr /= np.max(corr)\n",
    "  sns.heatmap(corr, xticklabels=labels, yticklabels=labels)\n",
    "\n",
    "# Generate embeddings for some terms\n",
    "queries = [\n",
    "  # Related viruses\n",
    "  'coronavirus', 'SARS', 'MERS',\n",
    "  # Regions\n",
    "  'Italy', 'Spain', 'Europe',\n",
    "  # Symptoms\n",
    "  'cough', 'fever', 'throat'\n",
    "]\n",
    "\n",
    "module = hub.load('https://tfhub.dev/tensorflow/cord-19/swivel-128d/3')\n",
    "embeddings = module(queries)\n",
    "\n",
    "plot_correlation(queries, embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bg-PGqtm8B7K"
   },
   "source": [
    "埋め込みが異なる用語の意味をうまく捉えていることが分かります。それぞれの単語は所属するクラスタの他の単語に類似していますが（「コロナウイルス」は「SARS」や「MERS」と高い関連性がある）、ほかのクラスタの単語とは異なります（「SARS」と「スペイン」の類似度はゼロに近い）。\n",
    "\n",
    "では、これらの埋め込みを使用して特定のタスクを解決する方法を見てみましょう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idJ1jFmH7xMa"
   },
   "source": [
    "## SciCite: 引用の意図の分類\n",
    "\n",
    "このセクションでは、テキスト分類など下流のタスクに埋め込みを使う方法を示します。学術論文の引用の意図の分類には、TensorFlow Dataset の [SciCite データセット](https://www.tensorflow.org/datasets/catalog/scicite)を使用します。学術論文からの引用がある文章がある場合に、その引用の主な意図が背景情報、方法の使用、または結果の比較のうち、どれであるかを分類します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:55.161259Z",
     "iopub.status.busy": "2024-01-11T19:07:55.160998Z",
     "iopub.status.idle": "2024-01-11T19:07:56.611733Z",
     "shell.execute_reply": "2024-01-11T19:07:56.611002Z"
    },
    "id": "Ghc-CzT8DDaZ"
   },
   "outputs": [],
   "source": [
    "builder = tfds.builder(name='scicite')\n",
    "builder.download_and_prepare()\n",
    "train_data, validation_data, test_data = builder.as_dataset(\n",
    "    split=('train', 'validation', 'test'),\n",
    "    as_supervised=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:56.615457Z",
     "iopub.status.busy": "2024-01-11T19:07:56.615193Z",
     "iopub.status.idle": "2024-01-11T19:07:56.977663Z",
     "shell.execute_reply": "2024-01-11T19:07:56.976934Z"
    },
    "id": "CVjyBD0ZPh4Z"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The finding that BMI is closely related to TBF...</td>\n",
       "      <td>result</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The average magnitude of the NBR increases wit...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It has been reported that NF-κB activation can...</td>\n",
       "      <td>result</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>, 2008; Quraan and Cheyne, 2008; Quraan and Ch...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5B), but, interestingly, they shared conserved...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Some investigators have noted an association o...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>In our previous study, it is documented that b...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>These subjects have intact cognitive function ...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Another study reported improved knee function ...</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C. Data Analysis Transcription Speech samples ...</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              string       label\n",
       "0  The finding that BMI is closely related to TBF...      result\n",
       "1  The average magnitude of the NBR increases wit...  background\n",
       "2  It has been reported that NF-κB activation can...      result\n",
       "3  , 2008; Quraan and Cheyne, 2008; Quraan and Ch...  background\n",
       "4  5B), but, interestingly, they shared conserved...  background\n",
       "5  Some investigators have noted an association o...  background\n",
       "6  In our previous study, it is documented that b...  background\n",
       "7  These subjects have intact cognitive function ...  background\n",
       "8  Another study reported improved knee function ...  background\n",
       "9  C. Data Analysis Transcription Speech samples ...      method"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Let's take a look at a few labeled examples from the training set\n",
    "NUM_EXAMPLES =   10#@param {type:\"integer\"}\n",
    "\n",
    "TEXT_FEATURE_NAME = builder.info.supervised_keys[0]\n",
    "LABEL_NAME = builder.info.supervised_keys[1]\n",
    "\n",
    "def label2str(numeric_label):\n",
    "  m = builder.info.features[LABEL_NAME].names\n",
    "  return m[numeric_label]\n",
    "\n",
    "data = next(iter(train_data.batch(NUM_EXAMPLES)))\n",
    "\n",
    "\n",
    "pd.DataFrame({\n",
    "    TEXT_FEATURE_NAME: [ex.numpy().decode('utf8') for ex in data[0]],\n",
    "    LABEL_NAME: [label2str(x) for x in data[1]]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "65s9UpYJ_1ct"
   },
   "source": [
    "## 引用の意図分類器をトレーニングする\n",
    "\n",
    "分類器のトレーニングには、[SciCite データセット](https://www.tensorflow.org/datasets/catalog/scicite)に対して Keras を使用します。上に分類レイヤーを持ち、CORD-19 埋め込みを使用するモデルを構築してみましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:56.981213Z",
     "iopub.status.busy": "2024-01-11T19:07:56.980937Z",
     "iopub.status.idle": "2024-01-11T19:07:57.773843Z",
     "shell.execute_reply": "2024-01-11T19:07:57.773148Z"
    },
    "id": "yZUclu8xBYlj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Layer (type)                Output Shape              Param #   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " keras_layer (KerasLayer)    (None, 128)               17301632  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dense (Dense)               (None, 3)                 387       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 17302019 (132.00 MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable params: 387 (1.51 KB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-trainable params: 17301632 (132.00 MB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#@title Hyperparameters { run: \"auto\" }\n",
    "\n",
    "EMBEDDING = 'https://tfhub.dev/tensorflow/cord-19/swivel-128d/3'  #@param {type: \"string\"}\n",
    "TRAINABLE_MODULE = False  #@param {type: \"boolean\"}\n",
    "\n",
    "hub_layer = hub.KerasLayer(EMBEDDING, input_shape=[], \n",
    "                           dtype=tf.string, trainable=TRAINABLE_MODULE)\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "model.add(hub_layer)\n",
    "model.add(tf.keras.layers.Dense(3))\n",
    "model.summary()\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "weZKWK-pLBll"
   },
   "source": [
    "## モデルをトレーニングして評価する\n",
    "\n",
    "モデルをトレーニングして評価を行い、SciCite タスクでのパフォーマンスを見てみましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:07:57.777328Z",
     "iopub.status.busy": "2024-01-11T19:07:57.777063Z",
     "iopub.status.idle": "2024-01-11T19:08:51.891377Z",
     "shell.execute_reply": "2024-01-11T19:08:51.890606Z"
    },
    "id": "cO1FWkZW2WS9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 4:42 - loss: 0.9825 - accuracy: 0.5625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 15/257 [>.............................] - ETA: 0s - loss: 1.0672 - accuracy: 0.4938  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1705000078.803553   91317 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/257 [==>...........................] - ETA: 0s - loss: 1.0500 - accuracy: 0.5171"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/257 [====>.........................] - ETA: 0s - loss: 1.0299 - accuracy: 0.5352"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 1.0144 - accuracy: 0.5392"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.9970 - accuracy: 0.5455"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.9899 - accuracy: 0.5451"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.9745 - accuracy: 0.5515"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/257 [=============>................] - ETA: 0s - loss: 0.9593 - accuracy: 0.5586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/257 [===============>..............] - ETA: 0s - loss: 0.9493 - accuracy: 0.5629"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/257 [=================>............] - ETA: 0s - loss: 0.9415 - accuracy: 0.5692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/257 [===================>..........] - ETA: 0s - loss: 0.9313 - accuracy: 0.5763"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/257 [=====================>........] - ETA: 0s - loss: 0.9226 - accuracy: 0.5807"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/257 [=======================>......] - ETA: 0s - loss: 0.9082 - accuracy: 0.5900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/257 [=========================>....] - ETA: 0s - loss: 0.8996 - 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/257 [==========================>...] - ETA: 0s - loss: 0.8920 - accuracy: 0.5992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/257 [============================>.] - ETA: 0s - loss: 0.8828 - accuracy: 0.6042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 3s 5ms/step - loss: 0.8821 - accuracy: 0.6047 - val_loss: 0.7713 - val_accuracy: 0.6779\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.6100 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.7221 - accuracy: 0.6953  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.7239 - accuracy: 0.6951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.7175 - accuracy: 0.6992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.7097 - accuracy: 0.7078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.7119 - accuracy: 0.7057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.7088 - accuracy: 0.7095"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/257 [===========>..................] - ETA: 0s - loss: 0.7054 - accuracy: 0.7139"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/257 [=============>................] - ETA: 0s - loss: 0.7053 - accuracy: 0.7145"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "141/257 [===============>..............] - ETA: 0s - loss: 0.7013 - accuracy: 0.7172"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/257 [=================>............] - ETA: 0s - loss: 0.6955 - accuracy: 0.7199"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172/257 [===================>..........] - ETA: 0s - loss: 0.6985 - accuracy: 0.7171"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/257 [====================>.........] - ETA: 0s - loss: 0.6967 - accuracy: 0.7192"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/257 [======================>.......] - ETA: 0s - loss: 0.6936 - accuracy: 0.7206"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/257 [========================>.....] - ETA: 0s - loss: 0.6931 - accuracy: 0.7215"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/257 [==========================>...] - ETA: 0s - loss: 0.6923 - accuracy: 0.7216"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/257 [===========================>..] - ETA: 0s - loss: 0.6900 - accuracy: 0.7235"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.6886 - accuracy: 0.7248 - val_loss: 0.6688 - val_accuracy: 0.7391\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:32 - loss: 0.7164 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.6414 - accuracy: 0.7539  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.6509 - accuracy: 0.7490"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.6475 - accuracy: 0.7474"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.6375 - accuracy: 0.7544"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.6390 - accuracy: 0.7461"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.6320 - accuracy: 0.7497"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.6323 - accuracy: 0.7492"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.6302 - accuracy: 0.7507"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.6281 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.6279 - accuracy: 0.7543"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.6273 - accuracy: 0.7564"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/257 [=====================>........] - ETA: 0s - loss: 0.6285 - accuracy: 0.7557"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/257 [=======================>......] - ETA: 0s - loss: 0.6253 - accuracy: 0.7574"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "226/257 [=========================>....] - ETA: 0s - loss: 0.6219 - accuracy: 0.7584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.6187 - accuracy: 0.7599"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 4ms/step - loss: 0.6186 - accuracy: 0.7598 - val_loss: 0.6251 - val_accuracy: 0.7434\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:21 - loss: 0.6011 - accuracy: 0.6875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5739 - accuracy: 0.7871  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5667 - accuracy: 0.7920"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5886 - accuracy: 0.7773"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5930 - accuracy: 0.7700"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5961 - accuracy: 0.7707"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5939 - accuracy: 0.7706"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5949 - accuracy: 0.7707"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5932 - accuracy: 0.7691"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.5943 - accuracy: 0.7683"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/257 [=================>............] - ETA: 0s - loss: 0.5968 - accuracy: 0.7673"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/257 [===================>..........] - ETA: 0s - loss: 0.5916 - accuracy: 0.7708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/257 [=====================>........] - ETA: 0s - loss: 0.5923 - accuracy: 0.7709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5928 - accuracy: 0.7697"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5916 - accuracy: 0.7706"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5877 - accuracy: 0.7728"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5853 - accuracy: 0.7732"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5853 - accuracy: 0.7732 - val_loss: 0.6061 - val_accuracy: 0.7489\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:34 - loss: 0.3739 - accuracy: 0.9375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5699 - accuracy: 0.7923  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/257 [==>...........................] - ETA: 0s - loss: 0.5616 - accuracy: 0.7978"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/257 [====>.........................] - ETA: 0s - loss: 0.5580 - accuracy: 0.7950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 66/257 [======>.......................] - ETA: 0s - loss: 0.5633 - accuracy: 0.7893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/257 [========>.....................] - ETA: 0s - loss: 0.5702 - accuracy: 0.7831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/257 [==========>...................] - ETA: 0s - loss: 0.5690 - accuracy: 0.7828"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/257 [============>.................] - ETA: 0s - loss: 0.5654 - accuracy: 0.7837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/257 [==============>...............] - ETA: 0s - loss: 0.5662 - accuracy: 0.7822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/257 [================>.............] - ETA: 0s - loss: 0.5662 - accuracy: 0.7831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/257 [==================>...........] - ETA: 0s - loss: 0.5705 - accuracy: 0.7809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/257 [====================>.........] - ETA: 0s - loss: 0.5681 - accuracy: 0.7809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/257 [======================>.......] - ETA: 0s - loss: 0.5680 - accuracy: 0.7811"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/257 [========================>.....] - ETA: 0s - loss: 0.5670 - accuracy: 0.7808"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/257 [==========================>...] - ETA: 0s - loss: 0.5669 - accuracy: 0.7810"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/257 [============================>.] - ETA: 0s - loss: 0.5670 - accuracy: 0.7810"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5660 - accuracy: 0.7814 - val_loss: 0.5878 - val_accuracy: 0.7620\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:23 - loss: 0.4820 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5791 - accuracy: 0.7886  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5944 - accuracy: 0.7746"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/257 [====>.........................] - ETA: 0s - loss: 0.5676 - accuracy: 0.7788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 66/257 [======>.......................] - ETA: 0s - loss: 0.5633 - accuracy: 0.7779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/257 [========>.....................] - ETA: 0s - loss: 0.5659 - accuracy: 0.7763"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/257 [==========>...................] - ETA: 0s - loss: 0.5657 - accuracy: 0.7777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/257 [============>.................] - ETA: 0s - loss: 0.5635 - accuracy: 0.7802"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/257 [==============>...............] - ETA: 0s - loss: 0.5646 - accuracy: 0.7815"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "147/257 [================>.............] - ETA: 0s - loss: 0.5636 - accuracy: 0.7821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/257 [==================>...........] - ETA: 0s - loss: 0.5627 - accuracy: 0.7814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/257 [====================>.........] - ETA: 0s - loss: 0.5583 - accuracy: 0.7839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.5543 - accuracy: 0.7868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/257 [=======================>......] - ETA: 0s - loss: 0.5551 - accuracy: 0.7862"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.5535 - accuracy: 0.7867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/257 [===========================>..] - ETA: 0s - loss: 0.5522 - accuracy: 0.7865"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5534 - accuracy: 0.7863 - val_loss: 0.5813 - val_accuracy: 0.7664\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.5793 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5175 - accuracy: 0.8033  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5456 - accuracy: 0.7945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.5465 - accuracy: 0.7921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.5466 - accuracy: 0.7889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5434 - accuracy: 0.7894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5457 - accuracy: 0.7909"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5393 - accuracy: 0.7934"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5478 - accuracy: 0.7888"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.5441 - accuracy: 0.7905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/257 [=================>............] - ETA: 0s - loss: 0.5435 - accuracy: 0.7909"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/257 [===================>..........] - ETA: 0s - loss: 0.5415 - accuracy: 0.7923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/257 [=====================>........] - ETA: 0s - loss: 0.5407 - accuracy: 0.7912"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/257 [=======================>......] - ETA: 0s - loss: 0.5450 - accuracy: 0.7891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.5462 - accuracy: 0.7879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/257 [===========================>..] - ETA: 0s - loss: 0.5460 - accuracy: 0.7865"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5449 - accuracy: 0.7869 - val_loss: 0.5716 - val_accuracy: 0.7762\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.5585 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5440 - accuracy: 0.8015  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5442 - accuracy: 0.7983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.5311 - accuracy: 0.7991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.5324 - accuracy: 0.7947"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5346 - accuracy: 0.7959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5352 - accuracy: 0.7964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5382 - accuracy: 0.7934"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5347 - accuracy: 0.7931"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.5415 - accuracy: 0.7907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/257 [=================>............] - ETA: 0s - loss: 0.5447 - accuracy: 0.7894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/257 [===================>..........] - ETA: 0s - loss: 0.5466 - accuracy: 0.7887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/257 [=====================>........] - ETA: 0s - loss: 0.5463 - accuracy: 0.7877"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5443 - accuracy: 0.7872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5428 - accuracy: 0.7879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5408 - accuracy: 0.7893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5383 - accuracy: 0.7898 - val_loss: 0.5693 - val_accuracy: 0.7740\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.4687 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.4990 - accuracy: 0.8015  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5168 - accuracy: 0.7983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.5290 - accuracy: 0.7915"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5269 - accuracy: 0.7876"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.5293 - accuracy: 0.7884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/257 [=========>....................] - ETA: 0s - loss: 0.5307 - accuracy: 0.7869"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/257 [===========>..................] - ETA: 0s - loss: 0.5336 - accuracy: 0.7864"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/257 [=============>................] - ETA: 0s - loss: 0.5335 - accuracy: 0.7879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/257 [===============>..............] - ETA: 0s - loss: 0.5312 - accuracy: 0.7901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/257 [=================>............] - ETA: 0s - loss: 0.5317 - accuracy: 0.7898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.5328 - accuracy: 0.7895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.5333 - accuracy: 0.7893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/257 [=======================>......] - ETA: 0s - loss: 0.5353 - accuracy: 0.7900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5381 - accuracy: 0.7876"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/257 [==========================>...] - ETA: 0s - loss: 0.5332 - accuracy: 0.7895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/257 [============================>.] - ETA: 0s - loss: 0.5325 - accuracy: 0.7896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 4ms/step - loss: 0.5327 - accuracy: 0.7896 - val_loss: 0.5627 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.4062 - accuracy: 0.9375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4941 - accuracy: 0.8262  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5297 - accuracy: 0.7939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5284 - accuracy: 0.7936"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5288 - accuracy: 0.7935"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5325 - accuracy: 0.7922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5332 - accuracy: 0.7926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5266 - accuracy: 0.7924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5244 - accuracy: 0.7932"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5282 - accuracy: 0.7925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5310 - accuracy: 0.7912"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5300 - accuracy: 0.7914"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5313 - accuracy: 0.7923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.5289 - accuracy: 0.7931"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/257 [=========================>....] - ETA: 0s - loss: 0.5286 - accuracy: 0.7924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/257 [===========================>..] - ETA: 0s - loss: 0.5305 - accuracy: 0.7918"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5292 - accuracy: 0.7938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5291 - accuracy: 0.7939 - val_loss: 0.5602 - val_accuracy: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:33 - loss: 0.4207 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.5012 - accuracy: 0.8088  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.4972 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.5156 - accuracy: 0.8074"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.5167 - accuracy: 0.8048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5303 - accuracy: 0.7975"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5225 - accuracy: 0.8003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5228 - accuracy: 0.8003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5279 - accuracy: 0.7977"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.5265 - accuracy: 0.7978"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/257 [=================>............] - ETA: 0s - loss: 0.5280 - accuracy: 0.7944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/257 [===================>..........] - ETA: 0s - loss: 0.5255 - accuracy: 0.7942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/257 [=====================>........] - ETA: 0s - loss: 0.5276 - accuracy: 0.7933"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/257 [=======================>......] - ETA: 0s - loss: 0.5235 - accuracy: 0.7965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.5249 - accuracy: 0.7943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/257 [===========================>..] - ETA: 0s - loss: 0.5243 - accuracy: 0.7948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5254 - accuracy: 0.7940 - val_loss: 0.5575 - val_accuracy: 0.7838\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:24 - loss: 0.4698 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5189 - accuracy: 0.7871  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5090 - accuracy: 0.7930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/257 [====>.........................] - ETA: 0s - loss: 0.5176 - accuracy: 0.7912"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/257 [======>.......................] - ETA: 0s - loss: 0.5225 - accuracy: 0.7939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 78/257 [========>.....................] - ETA: 0s - loss: 0.5330 - accuracy: 0.7893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/257 [=========>....................] - ETA: 0s - loss: 0.5334 - accuracy: 0.7856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "108/257 [===========>..................] - ETA: 0s - loss: 0.5216 - accuracy: 0.7917"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/257 [=============>................] - ETA: 0s - loss: 0.5173 - accuracy: 0.7970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "138/257 [===============>..............] - ETA: 0s - loss: 0.5157 - accuracy: 0.7957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "153/257 [================>.............] - ETA: 0s - loss: 0.5187 - accuracy: 0.7941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/257 [==================>...........] - ETA: 0s - loss: 0.5198 - accuracy: 0.7947"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/257 [====================>.........] - ETA: 0s - loss: 0.5172 - accuracy: 0.7950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/257 [======================>.......] - ETA: 0s - loss: 0.5161 - accuracy: 0.7962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/257 [=======================>......] - ETA: 0s - loss: 0.5180 - accuracy: 0.7954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.5194 - accuracy: 0.7949"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/257 [===========================>..] - ETA: 0s - loss: 0.5199 - accuracy: 0.7955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5229 - accuracy: 0.7944 - val_loss: 0.5563 - val_accuracy: 0.7795\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.7159 - accuracy: 0.5938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5052 - accuracy: 0.7871  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5412 - accuracy: 0.7764"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5217 - accuracy: 0.7878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5224 - accuracy: 0.7876"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5195 - accuracy: 0.7895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5261 - accuracy: 0.7878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5286 - accuracy: 0.7905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5305 - accuracy: 0.7910"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5291 - accuracy: 0.7906"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5233 - accuracy: 0.7943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5178 - accuracy: 0.7985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5195 - accuracy: 0.7974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/257 [=======================>......] - ETA: 0s - loss: 0.5203 - accuracy: 0.7965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5232 - accuracy: 0.7957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/257 [==========================>...] - ETA: 0s - loss: 0.5222 - accuracy: 0.7965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/257 [============================>.] - ETA: 0s - loss: 0.5200 - accuracy: 0.7962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5198 - accuracy: 0.7959 - val_loss: 0.5545 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:27 - loss: 0.5335 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5520 - accuracy: 0.7812  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5297 - accuracy: 0.7930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5258 - accuracy: 0.7923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5053 - accuracy: 0.8037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5116 - accuracy: 0.7996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5086 - accuracy: 0.8021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5049 - accuracy: 0.7997"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5126 - accuracy: 0.7944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5138 - accuracy: 0.7932"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5096 - accuracy: 0.7971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5091 - accuracy: 0.7997"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/257 [=====================>........] - ETA: 0s - loss: 0.5123 - accuracy: 0.7995"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5119 - accuracy: 0.7998"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5140 - accuracy: 0.7976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5166 - accuracy: 0.7962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5179 - accuracy: 0.7952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5179 - accuracy: 0.7952 - val_loss: 0.5511 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.6235 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5596 - accuracy: 0.7793  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5324 - accuracy: 0.7861"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5254 - accuracy: 0.7910"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5236 - accuracy: 0.7939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5228 - accuracy: 0.7926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5082 - accuracy: 0.8005"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5145 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5143 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5146 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5126 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5112 - accuracy: 0.7987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5151 - accuracy: 0.7987"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.5143 - accuracy: 0.7996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/257 [=========================>....] - ETA: 0s - loss: 0.5141 - accuracy: 0.7998"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/257 [===========================>..] - ETA: 0s - loss: 0.5138 - accuracy: 0.7993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5161 - accuracy: 0.7979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5160 - accuracy: 0.7979 - val_loss: 0.5552 - val_accuracy: 0.7773\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:55 - loss: 0.6078 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5207 - accuracy: 0.7832  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5254 - accuracy: 0.7773"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5118 - accuracy: 0.7884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5071 - accuracy: 0.7954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5092 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5022 - accuracy: 0.8018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5094 - accuracy: 0.7970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/257 [=============>................] - ETA: 0s - loss: 0.5124 - accuracy: 0.7958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/257 [===============>..............] - ETA: 0s - loss: 0.5163 - accuracy: 0.7974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/257 [=================>............] - ETA: 0s - loss: 0.5155 - accuracy: 0.7960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.5129 - accuracy: 0.7971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.5151 - accuracy: 0.7974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/257 [=======================>......] - ETA: 0s - loss: 0.5151 - accuracy: 0.7975"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5152 - accuracy: 0.7976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/257 [==========================>...] - ETA: 0s - loss: 0.5149 - accuracy: 0.7973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/257 [============================>.] - ETA: 0s - loss: 0.5149 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5144 - accuracy: 0.7972 - val_loss: 0.5504 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.4915 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4895 - accuracy: 0.8164  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5194 - accuracy: 0.7939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5139 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5101 - accuracy: 0.8047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5126 - accuracy: 0.8066"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5047 - accuracy: 0.8079"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.4966 - accuracy: 0.8119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5009 - accuracy: 0.8096"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/257 [================>.............] - ETA: 0s - loss: 0.4966 - accuracy: 0.8119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/257 [=================>............] - ETA: 0s - loss: 0.5007 - accuracy: 0.8081"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/257 [===================>..........] - ETA: 0s - loss: 0.5036 - accuracy: 0.8058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/257 [=====================>........] - ETA: 0s - loss: 0.5019 - accuracy: 0.8059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/257 [=======================>......] - ETA: 0s - loss: 0.5075 - accuracy: 0.8004"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.5086 - accuracy: 0.7991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/257 [===========================>..] - ETA: 0s - loss: 0.5091 - accuracy: 0.7993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5130 - accuracy: 0.7974 - val_loss: 0.5511 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:22 - loss: 0.5142 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5188 - accuracy: 0.7910  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5139 - accuracy: 0.7979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5176 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.5060 - accuracy: 0.8026"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.5076 - accuracy: 0.7979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5158 - accuracy: 0.7951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5184 - accuracy: 0.7925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/257 [=============>................] - ETA: 0s - loss: 0.5155 - accuracy: 0.7938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/257 [===============>..............] - ETA: 0s - loss: 0.5157 - accuracy: 0.7968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/257 [=================>............] - ETA: 0s - loss: 0.5112 - accuracy: 0.7989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/257 [===================>..........] - ETA: 0s - loss: 0.5065 - accuracy: 0.8009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/257 [=====================>........] - ETA: 0s - loss: 0.5062 - accuracy: 0.7997"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/257 [=======================>......] - ETA: 0s - loss: 0.5054 - accuracy: 0.7999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/257 [========================>.....] - ETA: 0s - loss: 0.5075 - accuracy: 0.7991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/257 [==========================>...] - ETA: 0s - loss: 0.5063 - accuracy: 0.7993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/257 [============================>.] - ETA: 0s - loss: 0.5095 - accuracy: 0.7971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 5ms/step - loss: 0.5111 - accuracy: 0.7967 - val_loss: 0.5487 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.4189 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5476 - accuracy: 0.8066  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5330 - accuracy: 0.7988"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5336 - accuracy: 0.7943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5332 - accuracy: 0.7930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5280 - accuracy: 0.7953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.5254 - accuracy: 0.7934"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5250 - accuracy: 0.7919"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/257 [=============>................] - ETA: 0s - loss: 0.5245 - accuracy: 0.7948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/257 [===============>..............] - ETA: 0s - loss: 0.5193 - accuracy: 0.7968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/257 [=================>............] - ETA: 0s - loss: 0.5241 - accuracy: 0.7944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.5196 - accuracy: 0.7974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.5143 - accuracy: 0.8000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/257 [=======================>......] - ETA: 0s - loss: 0.5123 - accuracy: 0.7985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5129 - accuracy: 0.7972"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/257 [==========================>...] - ETA: 0s - loss: 0.5102 - accuracy: 0.7982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/257 [============================>.] - ETA: 0s - loss: 0.5117 - accuracy: 0.7974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5099 - accuracy: 0.7979 - val_loss: 0.5501 - val_accuracy: 0.7806\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:24 - loss: 0.6587 - accuracy: 0.6875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5342 - accuracy: 0.7676  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5137 - accuracy: 0.7852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5078 - accuracy: 0.7910"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5227 - accuracy: 0.7861"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5258 - accuracy: 0.7836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5190 - accuracy: 0.7871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5198 - accuracy: 0.7849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5231 - accuracy: 0.7839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5230 - accuracy: 0.7860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5223 - accuracy: 0.7889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5173 - accuracy: 0.7908"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5160 - accuracy: 0.7915"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.5143 - accuracy: 0.7935"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.5128 - accuracy: 0.7961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/257 [===========================>..] - ETA: 0s - loss: 0.5133 - accuracy: 0.7962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/257 [============================>.] - ETA: 0s - loss: 0.5091 - accuracy: 0.7977"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5090 - accuracy: 0.7978 - val_loss: 0.5483 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.3576 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5064 - accuracy: 0.8027  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5033 - accuracy: 0.7969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5007 - accuracy: 0.7995"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.5135 - accuracy: 0.7957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5120 - accuracy: 0.7971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5021 - accuracy: 0.8025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5042 - accuracy: 0.8020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/257 [==============>...............] - ETA: 0s - loss: 0.5064 - accuracy: 0.7981"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/257 [================>.............] - ETA: 0s - loss: 0.5022 - accuracy: 0.7999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/257 [==================>...........] - ETA: 0s - loss: 0.5087 - accuracy: 0.7958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/257 [===================>..........] - ETA: 0s - loss: 0.5085 - accuracy: 0.7980"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.5086 - accuracy: 0.8002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/257 [=======================>......] - ETA: 0s - loss: 0.5079 - accuracy: 0.7998"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.5069 - accuracy: 0.7998"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/257 [===========================>..] - ETA: 0s - loss: 0.5073 - accuracy: 0.7993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5075 - accuracy: 0.7989 - val_loss: 0.5478 - val_accuracy: 0.7784\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:23 - loss: 0.4837 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4992 - accuracy: 0.8008  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4822 - accuracy: 0.8027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.4809 - accuracy: 0.8092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.4884 - accuracy: 0.8072"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.4972 - accuracy: 0.8017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.4977 - accuracy: 0.8022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.4913 - accuracy: 0.8056"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.4936 - accuracy: 0.8018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.4942 - accuracy: 0.8024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/257 [=================>............] - ETA: 0s - loss: 0.4939 - accuracy: 0.8034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/257 [===================>..........] - ETA: 0s - loss: 0.4947 - accuracy: 0.8031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.4988 - accuracy: 0.8016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/257 [=======================>......] - ETA: 0s - loss: 0.4996 - accuracy: 0.8010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5002 - accuracy: 0.8015"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/257 [==========================>...] - ETA: 0s - loss: 0.5041 - accuracy: 0.8000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/257 [============================>.] - ETA: 0s - loss: 0.5048 - accuracy: 0.7999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5068 - accuracy: 0.7984 - val_loss: 0.5449 - val_accuracy: 0.7849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.4579 - accuracy: 0.7500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5111 - accuracy: 0.8008  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5021 - accuracy: 0.8024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/257 [====>.........................] - ETA: 0s - loss: 0.4905 - accuracy: 0.8032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/257 [======>.......................] - ETA: 0s - loss: 0.4829 - accuracy: 0.8085"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/257 [=======>......................] - ETA: 0s - loss: 0.4851 - accuracy: 0.8121"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/257 [=========>....................] - ETA: 0s - loss: 0.4937 - accuracy: 0.8105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/257 [===========>..................] - ETA: 0s - loss: 0.4958 - accuracy: 0.8071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/257 [=============>................] - ETA: 0s - loss: 0.5044 - accuracy: 0.8039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/257 [===============>..............] - ETA: 0s - loss: 0.5052 - accuracy: 0.8036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "156/257 [=================>............] - ETA: 0s - loss: 0.5070 - accuracy: 0.8021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "171/257 [==================>...........] - ETA: 0s - loss: 0.5075 - accuracy: 0.8019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "186/257 [====================>.........] - ETA: 0s - loss: 0.5101 - accuracy: 0.8006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/257 [======================>.......] - ETA: 0s - loss: 0.5080 - accuracy: 0.8015"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "216/257 [========================>.....] - ETA: 0s - loss: 0.5088 - accuracy: 0.8014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/257 [==========================>...] - ETA: 0s - loss: 0.5062 - accuracy: 0.8020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/257 [===========================>..] - ETA: 0s - loss: 0.5074 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5058 - accuracy: 0.8008 - val_loss: 0.5451 - val_accuracy: 0.7849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.4537 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4817 - accuracy: 0.8398  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5033 - accuracy: 0.8044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/257 [====>.........................] - ETA: 0s - loss: 0.4956 - accuracy: 0.8032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/257 [======>.......................] - ETA: 0s - loss: 0.5057 - accuracy: 0.7979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/257 [=======>......................] - ETA: 0s - loss: 0.5013 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/257 [=========>....................] - ETA: 0s - loss: 0.5049 - accuracy: 0.8003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/257 [===========>..................] - ETA: 0s - loss: 0.4973 - accuracy: 0.8040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/257 [=============>................] - ETA: 0s - loss: 0.5038 - accuracy: 0.7999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "138/257 [===============>..............] - ETA: 0s - loss: 0.5010 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/257 [================>.............] - ETA: 0s - loss: 0.5012 - accuracy: 0.7993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/257 [==================>...........] - ETA: 0s - loss: 0.5034 - accuracy: 0.7996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "186/257 [====================>.........] - ETA: 0s - loss: 0.5025 - accuracy: 0.8009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/257 [======================>.......] - ETA: 0s - loss: 0.5018 - accuracy: 0.8012"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/257 [========================>.....] - ETA: 0s - loss: 0.5028 - accuracy: 0.8002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "234/257 [==========================>...] - ETA: 0s - loss: 0.5039 - accuracy: 0.8013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/257 [============================>.] - ETA: 0s - loss: 0.5050 - accuracy: 0.8013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5052 - accuracy: 0.8011 - val_loss: 0.5466 - val_accuracy: 0.7838\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.5563 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4616 - accuracy: 0.8125  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5019 - accuracy: 0.8057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.4985 - accuracy: 0.8027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.4937 - accuracy: 0.8051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.4973 - accuracy: 0.8030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.4937 - accuracy: 0.8049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5001 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/257 [=============>................] - ETA: 0s - loss: 0.4983 - accuracy: 0.8022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/257 [===============>..............] - ETA: 0s - loss: 0.4990 - accuracy: 0.8020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/257 [=================>............] - ETA: 0s - loss: 0.5034 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/257 [===================>..........] - ETA: 0s - loss: 0.5005 - accuracy: 0.8014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/257 [=====================>........] - ETA: 0s - loss: 0.5010 - accuracy: 0.8015"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/257 [=======================>......] - ETA: 0s - loss: 0.5061 - accuracy: 0.7986"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/257 [=========================>....] - ETA: 0s - loss: 0.5032 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/257 [==========================>...] - ETA: 0s - loss: 0.5040 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/257 [============================>.] - ETA: 0s - loss: 0.5046 - accuracy: 0.7996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5048 - accuracy: 0.7996 - val_loss: 0.5469 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:26 - loss: 0.4814 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5243 - accuracy: 0.7891  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5163 - accuracy: 0.7907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/257 [====>.........................] - ETA: 0s - loss: 0.5314 - accuracy: 0.7875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 66/257 [======>.......................] - ETA: 0s - loss: 0.5233 - accuracy: 0.7945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/257 [========>.....................] - ETA: 0s - loss: 0.5086 - accuracy: 0.7976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/257 [==========>...................] - ETA: 0s - loss: 0.4999 - accuracy: 0.8010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "114/257 [============>.................] - ETA: 0s - loss: 0.4999 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/257 [==============>...............] - ETA: 0s - loss: 0.5024 - accuracy: 0.8002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/257 [================>.............] - ETA: 0s - loss: 0.5059 - accuracy: 0.7979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/257 [=================>............] - ETA: 0s - loss: 0.5062 - accuracy: 0.7990"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/257 [===================>..........] - ETA: 0s - loss: 0.5066 - accuracy: 0.7992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/257 [=====================>........] - ETA: 0s - loss: 0.5062 - accuracy: 0.7996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/257 [=======================>......] - ETA: 0s - loss: 0.5054 - accuracy: 0.7994"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "226/257 [=========================>....] - ETA: 0s - loss: 0.5062 - accuracy: 0.7985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/257 [===========================>..] - ETA: 0s - loss: 0.5035 - accuracy: 0.7991"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5035 - accuracy: 0.8002 - val_loss: 0.5479 - val_accuracy: 0.7838\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:21 - loss: 0.4622 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5227 - accuracy: 0.7871  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4943 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5027 - accuracy: 0.7956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5131 - accuracy: 0.7944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5217 - accuracy: 0.7891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5244 - accuracy: 0.7891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5205 - accuracy: 0.7921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5210 - accuracy: 0.7922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5171 - accuracy: 0.7941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5106 - accuracy: 0.7977"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5082 - accuracy: 0.7983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5022 - accuracy: 0.8013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.5019 - accuracy: 0.8017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/257 [=========================>....] - ETA: 0s - loss: 0.5039 - accuracy: 0.8002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5041 - accuracy: 0.7998"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5038 - accuracy: 0.8001 - val_loss: 0.5446 - val_accuracy: 0.7817\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.4546 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4758 - accuracy: 0.8164  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.4937 - accuracy: 0.7979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5176 - accuracy: 0.7949"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5169 - accuracy: 0.7974"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5182 - accuracy: 0.7897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.5115 - accuracy: 0.7938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5096 - accuracy: 0.7956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5018 - accuracy: 0.8018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/257 [================>.............] - ETA: 0s - loss: 0.4973 - accuracy: 0.8039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/257 [==================>...........] - ETA: 0s - loss: 0.4998 - accuracy: 0.8039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/257 [===================>..........] - ETA: 0s - loss: 0.5028 - accuracy: 0.8036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.5004 - accuracy: 0.8036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/257 [=======================>......] - ETA: 0s - loss: 0.5029 - accuracy: 0.8019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/257 [=========================>....] - ETA: 0s - loss: 0.5029 - accuracy: 0.8014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/257 [===========================>..] - ETA: 0s - loss: 0.5007 - accuracy: 0.8027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5027 - accuracy: 0.8018 - val_loss: 0.5441 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:30 - loss: 0.6670 - accuracy: 0.7812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4907 - accuracy: 0.8105  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5028 - accuracy: 0.8054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/257 [====>.........................] - ETA: 0s - loss: 0.5063 - accuracy: 0.8045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.5028 - accuracy: 0.8006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.4910 - accuracy: 0.8046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/257 [==========>...................] - ETA: 0s - loss: 0.4934 - accuracy: 0.8046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/257 [===========>..................] - ETA: 0s - loss: 0.5001 - accuracy: 0.8010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.4945 - accuracy: 0.8047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.4899 - accuracy: 0.8062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/257 [=================>............] - ETA: 0s - loss: 0.4921 - accuracy: 0.8046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/257 [===================>..........] - ETA: 0s - loss: 0.4944 - accuracy: 0.8035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/257 [=====================>........] - ETA: 0s - loss: 0.4942 - accuracy: 0.8021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/257 [=======================>......] - ETA: 0s - loss: 0.4971 - accuracy: 0.8005"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/257 [=========================>....] - ETA: 0s - loss: 0.4999 - accuracy: 0.8004"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/257 [===========================>..] - ETA: 0s - loss: 0.5021 - accuracy: 0.7996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5021 - accuracy: 0.8000 - val_loss: 0.5442 - val_accuracy: 0.7849\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 3:12 - loss: 0.5393 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4945 - accuracy: 0.7988  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5098 - accuracy: 0.8021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.4939 - accuracy: 0.8087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.4958 - accuracy: 0.8091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.5027 - accuracy: 0.8032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.4995 - accuracy: 0.8080"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5041 - accuracy: 0.8050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5005 - accuracy: 0.8077"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.4995 - accuracy: 0.8071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/257 [=================>............] - ETA: 0s - loss: 0.4953 - accuracy: 0.8065"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/257 [===================>..........] - ETA: 0s - loss: 0.4935 - accuracy: 0.8066"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/257 [=====================>........] - ETA: 0s - loss: 0.4948 - accuracy: 0.8044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/257 [=======================>......] - ETA: 0s - loss: 0.4980 - accuracy: 0.8025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/257 [=========================>....] - ETA: 0s - loss: 0.4996 - accuracy: 0.8012"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/257 [===========================>..] - ETA: 0s - loss: 0.5000 - accuracy: 0.8019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 4ms/step - loss: 0.5014 - accuracy: 0.8019 - val_loss: 0.5438 - val_accuracy: 0.7882\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.4019 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.4864 - accuracy: 0.8070  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.4926 - accuracy: 0.7973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.4879 - accuracy: 0.8014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 63/257 [======>.......................] - ETA: 0s - loss: 0.5033 - accuracy: 0.7976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/257 [========>.....................] - ETA: 0s - loss: 0.5013 - accuracy: 0.8010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/257 [=========>....................] - ETA: 0s - loss: 0.4985 - accuracy: 0.8002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/257 [===========>..................] - ETA: 0s - loss: 0.4966 - accuracy: 0.8014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/257 [=============>................] - ETA: 0s - loss: 0.4897 - accuracy: 0.8058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/257 [===============>..............] - ETA: 0s - loss: 0.4883 - accuracy: 0.8066"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/257 [=================>............] - ETA: 0s - loss: 0.4930 - accuracy: 0.8034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/257 [===================>..........] - ETA: 0s - loss: 0.4928 - accuracy: 0.8044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/257 [=====================>........] - ETA: 0s - loss: 0.4962 - accuracy: 0.8036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/257 [=======================>......] - ETA: 0s - loss: 0.4966 - accuracy: 0.8040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/257 [========================>.....] - ETA: 0s - loss: 0.5019 - accuracy: 0.8017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/257 [==========================>...] - ETA: 0s - loss: 0.5043 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/257 [============================>.] - ETA: 0s - loss: 0.5001 - accuracy: 0.8025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5009 - accuracy: 0.8024 - val_loss: 0.5438 - val_accuracy: 0.7871\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:28 - loss: 0.6535 - accuracy: 0.8125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.5128 - accuracy: 0.7910  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/257 [==>...........................] - ETA: 0s - loss: 0.5084 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 48/257 [====>.........................] - ETA: 0s - loss: 0.5392 - accuracy: 0.7871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/257 [======>.......................] - ETA: 0s - loss: 0.5265 - accuracy: 0.7949"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/257 [========>.....................] - ETA: 0s - loss: 0.5170 - accuracy: 0.7973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/257 [==========>...................] - ETA: 0s - loss: 0.5156 - accuracy: 0.7933"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/257 [============>.................] - ETA: 0s - loss: 0.5145 - accuracy: 0.7938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/257 [=============>................] - ETA: 0s - loss: 0.5140 - accuracy: 0.7930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/257 [===============>..............] - ETA: 0s - loss: 0.5189 - accuracy: 0.7917"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/257 [=================>............] - ETA: 0s - loss: 0.5113 - accuracy: 0.7959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/257 [===================>..........] - ETA: 0s - loss: 0.5090 - accuracy: 0.7967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/257 [=====================>........] - ETA: 0s - loss: 0.5023 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/257 [=======================>......] - ETA: 0s - loss: 0.5012 - accuracy: 0.8003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/257 [=========================>....] - ETA: 0s - loss: 0.5005 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.5019 - accuracy: 0.7999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.5001 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5001 - accuracy: 0.8008 - val_loss: 0.5439 - val_accuracy: 0.7904\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:31 - loss: 0.3819 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.4964 - accuracy: 0.7978  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/257 [==>...........................] - ETA: 0s - loss: 0.5018 - accuracy: 0.7964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 45/257 [====>.........................] - ETA: 0s - loss: 0.5128 - accuracy: 0.7896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 60/257 [======>.......................] - ETA: 0s - loss: 0.5078 - accuracy: 0.7927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 76/257 [=======>......................] - ETA: 0s - loss: 0.5136 - accuracy: 0.7944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/257 [=========>....................] - ETA: 0s - loss: 0.5114 - accuracy: 0.7962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "108/257 [===========>..................] - ETA: 0s - loss: 0.5097 - accuracy: 0.8006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/257 [=============>................] - ETA: 0s - loss: 0.5105 - accuracy: 0.7971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/257 [===============>..............] - ETA: 0s - loss: 0.5105 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/257 [================>.............] - ETA: 0s - loss: 0.5101 - accuracy: 0.7981"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/257 [==================>...........] - ETA: 0s - loss: 0.5075 - accuracy: 0.7994"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/257 [====================>.........] - ETA: 0s - loss: 0.5058 - accuracy: 0.8007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/257 [======================>.......] - ETA: 0s - loss: 0.5067 - accuracy: 0.8016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "216/257 [========================>.....] - ETA: 0s - loss: 0.5054 - accuracy: 0.8016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/257 [==========================>...] - ETA: 0s - loss: 0.5036 - accuracy: 0.8013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/257 [===========================>..] - ETA: 0s - loss: 0.5016 - accuracy: 0.8026"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 2s 5ms/step - loss: 0.5006 - accuracy: 0.8034 - val_loss: 0.5456 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:32 - loss: 0.4996 - accuracy: 0.7188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/257 [>.............................] - ETA: 0s - loss: 0.4795 - accuracy: 0.8070  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/257 [==>...........................] - ETA: 0s - loss: 0.4829 - accuracy: 0.8107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/257 [====>.........................] - ETA: 0s - loss: 0.4905 - accuracy: 0.8069"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 66/257 [======>.......................] - ETA: 0s - loss: 0.4898 - accuracy: 0.8054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/257 [========>.....................] - ETA: 0s - loss: 0.4914 - accuracy: 0.8041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/257 [==========>...................] - ETA: 0s - loss: 0.4986 - accuracy: 0.8020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "114/257 [============>.................] - ETA: 0s - loss: 0.4962 - accuracy: 0.8037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/257 [==============>...............] - ETA: 0s - loss: 0.4970 - accuracy: 0.8029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/257 [================>.............] - ETA: 0s - loss: 0.5013 - accuracy: 0.8012"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/257 [==================>...........] - ETA: 0s - loss: 0.4962 - accuracy: 0.8039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/257 [====================>.........] - ETA: 0s - loss: 0.5013 - accuracy: 0.8031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/257 [=====================>........] - ETA: 0s - loss: 0.5060 - accuracy: 0.8001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/257 [=======================>......] - ETA: 0s - loss: 0.5061 - accuracy: 0.7992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "231/257 [=========================>....] - ETA: 0s - loss: 0.5018 - accuracy: 0.8025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/257 [===========================>..] - ETA: 0s - loss: 0.5021 - accuracy: 0.8011"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.5001 - accuracy: 0.8027 - val_loss: 0.5437 - val_accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/35\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/257 [..............................] - ETA: 1:29 - loss: 0.5460 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16/257 [>.............................] - ETA: 0s - loss: 0.4900 - accuracy: 0.8027  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 33/257 [==>...........................] - ETA: 0s - loss: 0.5030 - accuracy: 0.7973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/257 [====>.........................] - ETA: 0s - loss: 0.5068 - accuracy: 0.7972"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/257 [======>.......................] - ETA: 0s - loss: 0.5007 - accuracy: 0.8000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/257 [========>.....................] - ETA: 0s - loss: 0.4983 - accuracy: 0.8025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/257 [==========>...................] - ETA: 0s - loss: 0.4970 - accuracy: 0.8025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/257 [============>.................] - ETA: 0s - loss: 0.5045 - accuracy: 0.8017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/257 [==============>...............] - ETA: 0s - loss: 0.5041 - accuracy: 0.8014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/257 [===============>..............] - ETA: 0s - loss: 0.5007 - accuracy: 0.8032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/257 [=================>............] - ETA: 0s - loss: 0.5007 - accuracy: 0.8028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/257 [===================>..........] - ETA: 0s - loss: 0.4986 - accuracy: 0.8028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/257 [=====================>........] - ETA: 0s - loss: 0.4972 - accuracy: 0.8047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/257 [=======================>......] - ETA: 0s - loss: 0.4934 - accuracy: 0.8061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/257 [=========================>....] - ETA: 0s - loss: 0.4950 - accuracy: 0.8050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/257 [===========================>..] - ETA: 0s - loss: 0.4978 - accuracy: 0.8033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - ETA: 0s - loss: 0.4993 - accuracy: 0.8027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/257 [==============================] - 1s 4ms/step - loss: 0.4993 - accuracy: 0.8027 - val_loss: 0.5436 - val_accuracy: 0.7904\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 35#@param {type: \"integer\"}\n",
    "BATCH_SIZE = 32#@param {type: \"integer\"}\n",
    "\n",
    "history = model.fit(train_data.shuffle(10000).batch(BATCH_SIZE),\n",
    "                    epochs=EPOCHS,\n",
    "                    validation_data=validation_data.batch(BATCH_SIZE),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:08:51.895591Z",
     "iopub.status.busy": "2024-01-11T19:08:51.894935Z",
     "iopub.status.idle": "2024-01-11T19:08:51.900332Z",
     "shell.execute_reply": "2024-01-11T19:08:51.899582Z"
    },
    "id": "2sKE7kEyLJQZ"
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "def display_training_curves(training, validation, title, subplot):\n",
    "  if subplot%10==1: # set up the subplots on the first call\n",
    "    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
    "    plt.tight_layout()\n",
    "  ax = plt.subplot(subplot)\n",
    "  ax.set_facecolor('#F8F8F8')\n",
    "  ax.plot(training)\n",
    "  ax.plot(validation)\n",
    "  ax.set_title('model '+ title)\n",
    "  ax.set_ylabel(title)\n",
    "  ax.set_xlabel('epoch')\n",
    "  ax.legend(['train', 'valid.'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:08:51.903582Z",
     "iopub.status.busy": "2024-01-11T19:08:51.903000Z",
     "iopub.status.idle": "2024-01-11T19:08:52.451601Z",
     "shell.execute_reply": "2024-01-11T19:08:52.450867Z"
    },
    "id": "nnQfxevhLKld"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_training_curves(history.history['accuracy'], history.history['val_accuracy'], 'accuracy', 211)\n",
    "display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 212)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BjvtOw72Lpyw"
   },
   "source": [
    "## モデルを評価する\n",
    "\n",
    "モデルがどのように実行するか見てみましょう。2 つの値が返されます。損失（誤差、値が低いほど良）と正確度です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:08:52.456384Z",
     "iopub.status.busy": "2024-01-11T19:08:52.455801Z",
     "iopub.status.idle": "2024-01-11T19:08:52.829365Z",
     "shell.execute_reply": "2024-01-11T19:08:52.828632Z"
    },
    "id": "y0ExC8D0LX8m"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 - 0s - loss: 0.5344 - accuracy: 0.7907 - 359ms/epoch - 90ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.534\n",
      "accuracy: 0.791\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(test_data.batch(512), verbose=2)\n",
    "\n",
    "for name, value in zip(model.metrics_names, results):\n",
    "  print('%s: %.3f' % (name, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWp5OWeTL2EW"
   },
   "source": [
    "損失はすぐに減少しますが、特に精度は急速に上がることが分かります。予測と真のラベルがどのように関係しているかを確認するために、いくつかの例をプロットしてみましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-11T19:08:52.833187Z",
     "iopub.status.busy": "2024-01-11T19:08:52.832525Z",
     "iopub.status.idle": "2024-01-11T19:08:53.280709Z",
     "shell.execute_reply": "2024-01-11T19:08:53.279705Z"
    },
    "id": "VzHzAOaaOVC0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 182ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>string</th>\n",
       "      <th>label</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The diffraction grating, LED, and split detect...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Our ideas are based on a previous paper [4] de...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Our finding is consistent with the literature ...</td>\n",
       "      <td>result</td>\n",
       "      <td>result</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Test scores from each of the cognitive domains...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The optimization algorithm was set to maximize...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>To quantify the extent of substitution saturat...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Examples of gesture control are based on the e...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The identification of these features has been ...</td>\n",
       "      <td>method</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Postulated mechanisms for observed effects of ...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>The right inferior phrenic artery is the most ...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[8] presented an approach for estimating the t...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Similar structures were observed in M10 cells ...</td>\n",
       "      <td>result</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cytotoxic effects of cobalt chloride were repo...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>However, prolonged incubation of latex enzyme ...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>…and travels great distances, resulting in a s...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>The images fused using region selection; MSD, ...</td>\n",
       "      <td>method</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>These findings were expected, as EMG activity ...</td>\n",
       "      <td>result</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The model has been extended to both 2D and 3D ...</td>\n",
       "      <td>method</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Therefore, many authors claim comprehensive nu...</td>\n",
       "      <td>background</td>\n",
       "      <td>method</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Similar to Ab40, IAPP-GI populates an aggregat...</td>\n",
       "      <td>background</td>\n",
       "      <td>background</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               string       label  prediction\n",
       "0   The diffraction grating, LED, and split detect...  background      method\n",
       "1   Our ideas are based on a previous paper [4] de...  background      method\n",
       "2   Our finding is consistent with the literature ...      result      result\n",
       "3   Test scores from each of the cognitive domains...      method      method\n",
       "4   The optimization algorithm was set to maximize...      method      method\n",
       "5   To quantify the extent of substitution saturat...      method      method\n",
       "6   Examples of gesture control are based on the e...      method      method\n",
       "7   The identification of these features has been ...      method  background\n",
       "8   Postulated mechanisms for observed effects of ...  background  background\n",
       "9   The right inferior phrenic artery is the most ...  background  background\n",
       "10  [8] presented an approach for estimating the t...  background      method\n",
       "11  Similar structures were observed in M10 cells ...      result      method\n",
       "12  Cytotoxic effects of cobalt chloride were repo...  background  background\n",
       "13  However, prolonged incubation of latex enzyme ...  background  background\n",
       "14  …and travels great distances, resulting in a s...  background  background\n",
       "15  The images fused using region selection; MSD, ...      method      method\n",
       "16  These findings were expected, as EMG activity ...      result  background\n",
       "17  The model has been extended to both 2D and 3D ...      method  background\n",
       "18  Therefore, many authors claim comprehensive nu...  background      method\n",
       "19  Similar to Ab40, IAPP-GI populates an aggregat...  background  background"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_dataset = next(iter(test_data.batch(20)))\n",
    "\n",
    "prediction_texts = [ex.numpy().decode('utf8') for ex in prediction_dataset[0]]\n",
    "prediction_labels = [label2str(x) for x in prediction_dataset[1]]\n",
    "\n",
    "predictions = [\n",
    "    label2str(x) for x in np.argmax(model.predict(prediction_texts), axis=-1)]\n",
    "\n",
    "\n",
    "pd.DataFrame({\n",
    "    TEXT_FEATURE_NAME: prediction_texts,\n",
    "    LABEL_NAME: prediction_labels,\n",
    "    'prediction': predictions\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OSGcrkE069_Q"
   },
   "source": [
    "このランダムサンプルでは、ほとんどの場合、モデルが正しいラベルを予測しており、科学的な文をうまく埋め込むことができていることが分かります。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oLE0kCfO5CIA"
   },
   "source": [
    "# 次のステップ\n",
    "\n",
    "TF-Hub の CORD-19 Swivel 埋め込みについて少し説明しました。COVID-19 関連の学術的なテキストから科学的洞察の取得に貢献できる、CORD-19 Kaggle コンペへの参加をお勧めします。\n",
    "\n",
    "- [CORD-19 Kaggle Challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) に参加しましょう。\n",
    "- 詳細については [COVID-19 Open Research Dataset (CORD-19)](https://api.semanticscholar.org/CorpusID:216056360) をご覧ください。\n",
    "- TF-Hub 埋め込みに関する詳細のドキュメントは https://tfhub.dev/tensorflow/cord-19/swivel-128d/3 をご覧ください。\n",
    "- [TensorFlow Embedding Projector](http://projector.tensorflow.org/?config=https://storage.googleapis.com/tfhub-examples/tensorflow/cord-19/swivel-128d/3/tensorboard/projector_config.json) を利用して CORD-19 埋め込み空間を見てみましょう。"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "cord_19_embeddings_keras.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}