{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Ic4_occAAiAT"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "form",
"execution": {
"iopub.execute_input": "2022-12-14T20:19:13.201210Z",
"iopub.status.busy": "2022-12-14T20:19:13.200698Z",
"iopub.status.idle": "2022-12-14T20:19:13.204982Z",
"shell.execute_reply": "2022-12-14T20:19:13.204338Z"
},
"id": "ioaprt5q5US7"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "form",
"execution": {
"iopub.execute_input": "2022-12-14T20:19:13.208434Z",
"iopub.status.busy": "2022-12-14T20:19:13.207840Z",
"iopub.status.idle": "2022-12-14T20:19:13.211584Z",
"shell.execute_reply": "2022-12-14T20:19:13.210890Z"
},
"id": "yCl0eTNH5RS3"
},
"outputs": [],
"source": [
"#@title MIT License\n",
"#\n",
"# Copyright (c) 2017 François Chollet\n",
"#\n",
"# Permission is hereby granted, free of charge, to any person obtaining a\n",
"# copy of this software and associated documentation files (the \"Software\"),\n",
"# to deal in the Software without restriction, including without limitation\n",
"# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
"# and/or sell copies of the Software, and to permit persons to whom the\n",
"# Software is furnished to do so, subject to the following conditions:\n",
"#\n",
"# The above copyright notice and this permission notice shall be included in\n",
"# all copies or substantial portions of the Software.\n",
"#\n",
"# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
"# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
"# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
"# DEALINGS IN THE SOFTWARE."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ItXfxkxvosLH"
},
"source": [
"# 영화 리뷰를 사용한 텍스트 분류"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hKY4XMc9o8iB"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Eg62Pmz3o83v"
},
"source": [
"이 튜토리얼은 디스크에 저장된 일반 텍스트 파일에서 시작하는 텍스트 분류를 보여줍니다. IMDB 데이터세트에 대한 감정 분석을 수행하도록 이진 분류기를 훈련합니다. 노트북의 마지막에는 스택 오버플로에서 프로그래밍 질문에 대한 태그를 예측하도록 다중 클래스 분류기를 훈련하는 연습을 시도해볼 수 있습니다.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:13.215259Z",
"iopub.status.busy": "2022-12-14T20:19:13.214737Z",
"iopub.status.idle": "2022-12-14T20:19:15.717042Z",
"shell.execute_reply": "2022-12-14T20:19:15.716324Z"
},
"id": "8RZOuS9LWQvv"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-12-14 20:19:14.627348: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
"2022-12-14 20:19:14.627450: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
"2022-12-14 20:19:14.627459: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import os\n",
"import re\n",
"import shutil\n",
"import string\n",
"import tensorflow as tf\n",
"\n",
"from tensorflow.keras import layers\n",
"from tensorflow.keras import losses\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:15.721483Z",
"iopub.status.busy": "2022-12-14T20:19:15.720787Z",
"iopub.status.idle": "2022-12-14T20:19:15.724858Z",
"shell.execute_reply": "2022-12-14T20:19:15.724266Z"
},
"id": "6-tTFS04dChr"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.11.0\n"
]
}
],
"source": [
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NBTI1bi8qdFV"
},
"source": [
"## 감정 분석\n",
"\n",
"이 노트북은 리뷰 텍스트를 사용하여 영화 리뷰를 *긍정적* 또는 *부정적*으로 분류합니다. 중요하고 널리 적용 가능한 머신러닝 문제인 *이진* 분류의 예입니다.\n",
"\n",
"[IMDB 데이터세트](https://ai.stanford.edu/~amaas/data/sentiment/)에는 [인터넷 영화 데이터베이스](https://www.imdb.com/)에서 가져온 50,000개의 영화 리뷰 텍스트가 포함되어 있습니다. 훈련용 리뷰 25,000개와 테스트용 리뷰 25,000개로 나뉩니다. 훈련 및 테스트 세트는 *균형을 이룹니다*. 즉, 동일한 수의 긍정적인 리뷰와 부정적인 리뷰가 포함되어 있습니다.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iAsKG535pHep"
},
"source": [
"### 데이터세트 다운로드 및 탐색하기\n",
"\n",
"데이터 세트를 다운로드하여 추출한 다음 디렉터리 구조를 살펴보겠습니다."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:15.728406Z",
"iopub.status.busy": "2022-12-14T20:19:15.727925Z",
"iopub.status.idle": "2022-12-14T20:19:31.440444Z",
"shell.execute_reply": "2022-12-14T20:19:31.439568Z"
},
"id": "k7ZYnuajVlFN"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 8192/84125825 [..............................] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 57344/84125825 [..............................] - ETA: 2:19"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 106496/84125825 [..............................] - ETA: 2:29"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 204800/84125825 [..............................] - ETA: 1:56"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 352256/84125825 [..............................] - ETA: 1:30"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 516096/84125825 [..............................] - ETA: 1:16"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 663552/84125825 [..............................] - ETA: 1:05"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 860160/84125825 [..............................] - ETA: 59s "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 1073152/84125825 [..............................] - ETA: 51s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 1351680/84125825 [..............................] - ETA: 46s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 1695744/84125825 [..............................] - ETA: 39s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 2088960/84125825 [..............................] - ETA: 35s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 2547712/84125825 [..............................] - ETA: 30s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 3039232/84125825 [>.............................] - ETA: 26s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 3514368/84125825 [>.............................] - ETA: 25s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 4177920/84125825 [>.............................] - ETA: 21s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 4939776/84125825 [>.............................] - ETA: 19s"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 5890048/84125825 [=>............................] - ETA: 16s"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 6823936/84125825 [=>............................] - ETA: 15s"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7856128/84125825 [=>............................] - ETA: 13s"
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},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 9068544/84125825 [==>...........................] - ETA: 11s"
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},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"10493952/84125825 [==>...........................] - ETA: 10s"
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},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"12148736/84125825 [===>..........................] - ETA: 9s "
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},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"13885440/84125825 [===>..........................] - ETA: 7s"
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},
{
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"15843328/84125825 [====>.........................] - ETA: 7s"
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},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"18046976/84125825 [=====>........................] - ETA: 6s"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"20529152/84125825 [======>.......................] - ETA: 5s"
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},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"23142400/84125825 [=======>......................] - ETA: 4s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"25772032/84125825 [========>.....................] - ETA: 4s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"28762112/84125825 [=========>....................] - ETA: 3s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"31744000/84125825 [==========>...................] - ETA: 3s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"34709504/84125825 [===========>..................] - ETA: 2s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"37855232/84125825 [============>.................] - ETA: 2s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"41033728/84125825 [=============>................] - ETA: 2s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"44310528/84125825 [==============>...............] - ETA: 1s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"47554560/84125825 [===============>..............] - ETA: 1s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"50700288/84125825 [=================>............] - ETA: 1s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"53993472/84125825 [==================>...........] - ETA: 1s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"57171968/84125825 [===================>..........] - ETA: 1s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"60334080/84125825 [====================>.........] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"63610880/84125825 [=====================>........] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"66789376/84125825 [======================>.......] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"69935104/84125825 [=======================>......] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"73080832/84125825 [=========================>....] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"76357632/84125825 [==========================>...] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"79552512/84125825 [===========================>..] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"82731008/84125825 [============================>.] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"84125825/84125825 [==============================] - 3s 0us/step\n"
]
}
],
"source": [
"url = \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\n",
"\n",
"dataset = tf.keras.utils.get_file(\"aclImdb_v1\", url,\n",
" untar=True, cache_dir='.',\n",
" cache_subdir='')\n",
"\n",
"dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:31.445175Z",
"iopub.status.busy": "2022-12-14T20:19:31.444568Z",
"iopub.status.idle": "2022-12-14T20:19:31.452104Z",
"shell.execute_reply": "2022-12-14T20:19:31.451457Z"
},
"id": "355CfOvsV1pl"
},
"outputs": [
{
"data": {
"text/plain": [
"['imdbEr.txt', 'imdb.vocab', 'train', 'test', 'README']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir(dataset_dir)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:31.455859Z",
"iopub.status.busy": "2022-12-14T20:19:31.455285Z",
"iopub.status.idle": "2022-12-14T20:19:31.460295Z",
"shell.execute_reply": "2022-12-14T20:19:31.459670Z"
},
"id": "7ASND15oXpF1"
},
"outputs": [
{
"data": {
"text/plain": [
"['urls_unsup.txt',\n",
" 'urls_neg.txt',\n",
" 'labeledBow.feat',\n",
" 'urls_pos.txt',\n",
" 'unsup',\n",
" 'unsupBow.feat',\n",
" 'pos',\n",
" 'neg']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dir = os.path.join(dataset_dir, 'train')\n",
"os.listdir(train_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ysMNMI1CWDFD"
},
"source": [
"`aclImdb/train/pos` 및 `aclImdb/train/neg` 디렉토리에는 각각 단일 영화를 리뷰한 많은 텍스트 파일이 포함되어 있습니다. 그 중 하나를 살펴보겠습니다."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:31.464517Z",
"iopub.status.busy": "2022-12-14T20:19:31.463860Z",
"iopub.status.idle": "2022-12-14T20:19:31.468680Z",
"shell.execute_reply": "2022-12-14T20:19:31.467922Z"
},
"id": "R7g8hFvzWLIZ"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rachel Griffiths writes and directs this award winning short film. A heartwarming story about coping with grief and cherishing the memory of those we've loved and lost. Although, only 15 minutes long, Griffiths manages to capture so much emotion and truth onto film in the short space of time. Bud Tingwell gives a touching performance as Will, a widower struggling to cope with his wife's death. Will is confronted by the harsh reality of loneliness and helplessness as he proceeds to take care of Ruth's pet cow, Tulip. The film displays the grief and responsibility one feels for those they have loved and lost. Good cinematography, great direction, and superbly acted. It will bring tears to all those who have lost a loved one, and survived.\n"
]
}
],
"source": [
"sample_file = os.path.join(train_dir, 'pos/1181_9.txt')\n",
"with open(sample_file) as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mk20TEm6ZRFP"
},
"source": [
"### 데이터세트 로드하기\n",
"\n",
"다음으로, 디스크에서 데이터를 로드하고 훈련에 적합한 형식으로 준비합니다. 이를 위해 다음과 같은 디렉토리 구조를 예상하는 유용한 [text_dataset_from_directory](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text_dataset_from_directory) 유틸리티를 사용합니다.\n",
"\n",
"```\n",
"main_directory/\n",
"...class_a/\n",
"......a_text_1.txt\n",
"......a_text_2.txt\n",
"...class_b/\n",
"......b_text_1.txt\n",
"......b_text_2.txt\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nQauv38Lnok3"
},
"source": [
"이진 분류를 위한 데이터세트를 준비하려면 디스크에 `class_a` 및 `class_b`에 해당하는 두 개의 폴더가 필요합니다. 이것들은 `aclImdb/train/pos` 및 `aclImdb/train/neg`에서 찾을 수 있는 긍정적 영화 리뷰와 부정적 영화 리뷰입니다. IMDB 데이터세트에는 추가 폴더가 포함되어 있으므로 이 유틸리티를 사용하기 전에 제거합니다."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:31.472540Z",
"iopub.status.busy": "2022-12-14T20:19:31.472049Z",
"iopub.status.idle": "2022-12-14T20:19:32.452195Z",
"shell.execute_reply": "2022-12-14T20:19:32.451427Z"
},
"id": "VhejsClzaWfl"
},
"outputs": [],
"source": [
"remove_dir = os.path.join(train_dir, 'unsup')\n",
"shutil.rmtree(remove_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "95kkUdRoaeMw"
},
"source": [
"다음으로 `text_dataset_from_directory` 유틸리티를 사용하여 레이블이 지정된 `tf.data.Dataset`를 만듭니다. [tf.data](https://www.tensorflow.org/guide/data)는 데이터 작업을 위한 강력한 도구 모음입니다.\n",
"\n",
"머신러닝 실험을 실행할 때 데이터세트를 [train](https://developers.google.com/machine-learning/glossary#training_set), [validation](https://developers.google.com/machine-learning/glossary#validation_set) 및 [test](https://developers.google.com/machine-learning/glossary#test-set)의 세 부분으로 나누는 것이 가장 좋습니다.\n",
"\n",
"IMDB 데이터세트는 이미 훈련과 테스트로 나누어져 있지만 검증 세트가 부족합니다. 아래 `validation_split` 인수를 사용하여 훈련 데이터를 80:20으로 분할하여 검증 세트를 생성해 보겠습니다."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:32.456545Z",
"iopub.status.busy": "2022-12-14T20:19:32.456262Z",
"iopub.status.idle": "2022-12-14T20:19:37.425329Z",
"shell.execute_reply": "2022-12-14T20:19:37.424557Z"
},
"id": "nOrK-MTYaw3C"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 25000 files belonging to 2 classes.\n",
"Using 20000 files for training.\n"
]
}
],
"source": [
"batch_size = 32\n",
"seed = 42\n",
"\n",
"raw_train_ds = tf.keras.utils.text_dataset_from_directory(\n",
" 'aclImdb/train', \n",
" batch_size=batch_size, \n",
" validation_split=0.2, \n",
" subset='training', \n",
" seed=seed)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Y33oxOUpYkh"
},
"source": [
"위에서 볼 수 있듯이 training 폴더에는 25,000개의 예제가 있으며 그 중 80%(또는 20,000개)를 훈련에 사용할 것입니다. 잠시 후에 알 수 있겠지만 데이터세트를 `model.fit`에 직접 전달하여 모델을 훈련할 수 있습니다. `tf.data`를 처음 사용하는 경우 데이터세트를 반복하고 다음과 같이 몇 가지 예를 출력할 수도 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:37.429536Z",
"iopub.status.busy": "2022-12-14T20:19:37.428997Z",
"iopub.status.idle": "2022-12-14T20:19:37.458934Z",
"shell.execute_reply": "2022-12-14T20:19:37.458081Z"
},
"id": "51wNaPPApk1K"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Review b'\"Pandemonium\" is a horror movie spoof that comes off more stupid than funny. Believe me when I tell you, I love comedies. Especially comedy spoofs. \"Airplane\", \"The Naked Gun\" trilogy, \"Blazing Saddles\", \"High Anxiety\", and \"Spaceballs\" are some of my favorite comedies that spoof a particular genre. \"Pandemonium\" is not up there with those films. Most of the scenes in this movie had me sitting there in stunned silence because the movie wasn\\'t all that funny. There are a few laughs in the film, but when you watch a comedy, you expect to laugh a lot more than a few times and that\\'s all this film has going for it. Geez, \"Scream\" had more laughs than this film and that was more of a horror film. How bizarre is that?, )\n"
]
}
],
"source": [
"# retrieve a batch (of 32 reviews and labels) from the dataset\n",
"text_batch, label_batch = next(iter(raw_train_ds))\n",
"first_review, first_label = text_batch[0], label_batch[0]\n",
"print(\"Review\", first_review)\n",
"print(\"Label\", raw_train_ds.class_names[first_label])\n",
"print(\"Vectorized review\", vectorize_text(first_review, first_label))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6u5EX0hxyNZT"
},
"source": [
"위에서 볼 수 있듯이 각 토큰은 정수로 대체되었습니다. 레이어에서 `.get_vocabulary()`를 호출하여 각 정수에 해당하는 토큰(문자열)을 조회할 수 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.574388Z",
"iopub.status.busy": "2022-12-14T20:19:42.573766Z",
"iopub.status.idle": "2022-12-14T20:19:42.621457Z",
"shell.execute_reply": "2022-12-14T20:19:42.620773Z"
},
"id": "kRq9hTQzhVhW"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1287 ---> silent\n",
" 313 ---> night\n",
"Vocabulary size: 10000\n"
]
}
],
"source": [
"print(\"1287 ---> \",vectorize_layer.get_vocabulary()[1287])\n",
"print(\" 313 ---> \",vectorize_layer.get_vocabulary()[313])\n",
"print('Vocabulary size: {}'.format(len(vectorize_layer.get_vocabulary())))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XD2H6utRydGv"
},
"source": [
"모델을 훈련할 준비가 거의 되었습니다. 최종 전처리 단계로 이전에 생성한 TextVectorization 레이어를 훈련, 검증 및 테스트 데이터세트에 적용합니다."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.625295Z",
"iopub.status.busy": "2022-12-14T20:19:42.624581Z",
"iopub.status.idle": "2022-12-14T20:19:42.764864Z",
"shell.execute_reply": "2022-12-14T20:19:42.764065Z"
},
"id": "2zhmpeViI1iG"
},
"outputs": [],
"source": [
"train_ds = raw_train_ds.map(vectorize_text)\n",
"val_ds = raw_val_ds.map(vectorize_text)\n",
"test_ds = raw_test_ds.map(vectorize_text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YsVQyPMizjuO"
},
"source": [
"### 성능을 높이도록 데이터세트 구성하기\n",
"\n",
"다음은 I/O가 차단되지 않도록 데이터를 로드할 때 사용해야 하는 두 가지 중요한 메서드입니다.\n",
"\n",
"`.cache()`는 데이터가 디스크에서 로드된 후 메모리에 데이터를 보관합니다. 이렇게 하면 모델을 훈련하는 동안 데이터세트로 인해 병목 현상이 발생하지 않습니다. 데이터세트가 너무 커서 메모리에 맞지 않는 경우, 이 메서드를 사용하여 성능이 뛰어난 온 디스크 캐시를 생성할 수도 있습니다. 많은 작은 파일보다 읽기가 더 효율적입니다.\n",
"\n",
"`.prefetch()`는 훈련 중에 데이터 전처리 및 모델 실행과 겹칩니다.\n",
"\n",
"[데이터 성능 가이드](https://www.tensorflow.org/guide/data_performance)에서 두 가지 메서드와 데이터를 디스크에 캐싱하는 방법에 관해 자세히 알아볼 수 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.769459Z",
"iopub.status.busy": "2022-12-14T20:19:42.768773Z",
"iopub.status.idle": "2022-12-14T20:19:42.777705Z",
"shell.execute_reply": "2022-12-14T20:19:42.777073Z"
},
"id": "wMcs_H7izm5m"
},
"outputs": [],
"source": [
"AUTOTUNE = tf.data.AUTOTUNE\n",
"\n",
"train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
"val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
"test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LLC02j2g-llC"
},
"source": [
"### 모델 생성\n",
"\n",
"이제 신경망을 만들 차례입니다."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.781503Z",
"iopub.status.busy": "2022-12-14T20:19:42.780980Z",
"iopub.status.idle": "2022-12-14T20:19:42.784337Z",
"shell.execute_reply": "2022-12-14T20:19:42.783705Z"
},
"id": "dkQP6in8yUBR"
},
"outputs": [],
"source": [
"embedding_dim = 16"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.787859Z",
"iopub.status.busy": "2022-12-14T20:19:42.787346Z",
"iopub.status.idle": "2022-12-14T20:19:42.850162Z",
"shell.execute_reply": "2022-12-14T20:19:42.849457Z"
},
"id": "xpKOoWgu-llD"
},
"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": [
" embedding (Embedding) (None, None, 16) 160016 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" dropout (Dropout) (None, None, 16) 0 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" global_average_pooling1d (G (None, 16) 0 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" lobalAveragePooling1D) \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" dropout_1 (Dropout) (None, 16) 0 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" dense (Dense) (None, 1) 17 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"=================================================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total params: 160,033\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trainable params: 160,033\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Non-trainable params: 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n"
]
}
],
"source": [
"model = tf.keras.Sequential([\n",
" layers.Embedding(max_features + 1, embedding_dim),\n",
" layers.Dropout(0.2),\n",
" layers.GlobalAveragePooling1D(),\n",
" layers.Dropout(0.2),\n",
" layers.Dense(1)])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6PbKQ6mucuKL"
},
"source": [
"층을 순서대로 쌓아 분류기(classifier)를 만듭니다:\n",
"\n",
"1. 첫 번째 레이어는 `Embedding` 레이어입니다. 이 레이어는 정수로 인코딩된 리뷰를 입력 받고 각 단어 인덱스에 해당하는 임베딩 벡터를 찾습니다. 이러한 벡터는 모델이 훈련되면서 학습됩니다. 이들 벡터는 출력 배열에 차원을 추가합니다. 최종 차원은 `(batch, sequence, embedding)`이 됩니다. 임베딩에 대해 보다 자세히 알아보려면 [단어 임베딩](https://www.tensorflow.org/text/guide/word_embeddings) 튜토리얼을 확인하세요.\n",
"2. 그다음 `GlobalAveragePooling1D` 층은 `sequence` 차원에 대해 평균을 계산하여 각 샘플에 대해 고정된 길이의 출력 벡터를 반환합니다. 이는 길이가 다른 입력을 다루는 가장 간단한 방법입니다.\n",
"3. 이 고정 길이의 출력 벡터는 16개의 은닉 유닛을 가진 완전 연결(fully-connected) 층(`Dense`)을 거칩니다.\n",
"4. 마지막 층은 하나의 출력 노드(node)를 가진 완전 연결 층입니다. `sigmoid` 활성화 함수를 사용하여 0과 1 사이의 실수를 출력합니다. 이 값은 확률 또는 신뢰도를 나타냅니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L4EqVWg4-llM"
},
"source": [
"### 손실 함수와 옵티마이저\n",
"\n",
"모델이 훈련하려면 손실 함수(loss function)과 옵티마이저(optimizer)가 필요합니다. 이 예제는 이진 분류 문제이고 모델이 확률을 출력하므로(출력층의 유닛이 하나이고 `sigmoid` 활성화 함수를 사용합니다), `binary_crossentropy` 손실 함수를 사용하겠습니다.\n",
"\n",
"이제 최적화 기와 손실 함수를 사용하도록 모델을 구성합니다."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.859610Z",
"iopub.status.busy": "2022-12-14T20:19:42.858866Z",
"iopub.status.idle": "2022-12-14T20:19:42.875293Z",
"shell.execute_reply": "2022-12-14T20:19:42.874587Z"
},
"id": "Mr0GP-cQ-llN"
},
"outputs": [],
"source": [
"model.compile(loss=losses.BinaryCrossentropy(from_logits=True),\n",
" optimizer='adam',\n",
" metrics=tf.metrics.BinaryAccuracy(threshold=0.0))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "35jv_fzP-llU"
},
"source": [
"### 모델 훈련하기\n",
"\n",
"`dataset` 개체를 fit 메서드에 전달하여 모델을 훈련합니다."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:19:42.879122Z",
"iopub.status.busy": "2022-12-14T20:19:42.878584Z",
"iopub.status.idle": "2022-12-14T20:20:49.144887Z",
"shell.execute_reply": "2022-12-14T20:20:49.144132Z"
},
"id": "tXSGrjWZ-llW"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 14:52 - loss: 0.6952 - binary_accuracy: 0.1875"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 2/625 [..............................] - ETA: 1:47 - loss: 0.6942 - binary_accuracy: 0.3438 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 3/625 [..............................] - ETA: 1:44 - loss: 0.6937 - binary_accuracy: 0.4271"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 4/625 [..............................] - ETA: 1:45 - loss: 0.6933 - binary_accuracy: 0.4688"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 5/625 [..............................] - ETA: 1:44 - loss: 0.6931 - binary_accuracy: 0.4938"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 6/625 [..............................] - ETA: 1:44 - loss: 0.6930 - binary_accuracy: 0.5052"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7/625 [..............................] - ETA: 1:44 - loss: 0.6930 - binary_accuracy: 0.5089"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 8/625 [..............................] - ETA: 1:43 - loss: 0.6930 - binary_accuracy: 0.5234"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 9/625 [..............................] - ETA: 1:43 - loss: 0.6930 - binary_accuracy: 0.5174"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 10/625 [..............................] - ETA: 1:43 - loss: 0.6932 - binary_accuracy: 0.5125"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 11/625 [..............................] - ETA: 1:42 - loss: 0.6929 - binary_accuracy: 0.5284"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 12/625 [..............................] - ETA: 1:42 - loss: 0.6929 - binary_accuracy: 0.5339"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 13/625 [..............................] - ETA: 1:41 - loss: 0.6928 - binary_accuracy: 0.5409"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 14/625 [..............................] - ETA: 1:41 - loss: 0.6928 - binary_accuracy: 0.5335"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 15/625 [..............................] - ETA: 1:41 - loss: 0.6929 - binary_accuracy: 0.5292"
]
},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 16/625 [..............................] - ETA: 1:40 - loss: 0.6928 - binary_accuracy: 0.5273"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 17/625 [..............................] - ETA: 1:40 - loss: 0.6929 - binary_accuracy: 0.5276"
]
},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/625 [..............................] - ETA: 1:40 - loss: 0.6929 - binary_accuracy: 0.5278"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 19/625 [..............................] - ETA: 1:39 - loss: 0.6929 - binary_accuracy: 0.5230"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 21/625 [>.............................] - ETA: 1:34 - loss: 0.6929 - binary_accuracy: 0.5193"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 22/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5199"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 23/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5231"
]
},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 24/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5208"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 25/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5200"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 26/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5204"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 27/625 [>.............................] - ETA: 1:34 - loss: 0.6929 - binary_accuracy: 0.5116"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 28/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5112"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 29/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5129"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 30/625 [>.............................] - ETA: 1:34 - loss: 0.6927 - binary_accuracy: 0.5146"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 31/625 [>.............................] - ETA: 1:34 - loss: 0.6928 - binary_accuracy: 0.5121"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 32/625 [>.............................] - ETA: 1:34 - loss: 0.6927 - binary_accuracy: 0.5117"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 33/625 [>.............................] - ETA: 1:34 - loss: 0.6927 - binary_accuracy: 0.5133"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 35/625 [>.............................] - ETA: 1:31 - loss: 0.6927 - binary_accuracy: 0.5107"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 36/625 [>.............................] - ETA: 1:31 - loss: 0.6927 - binary_accuracy: 0.5113"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 37/625 [>.............................] - ETA: 1:31 - loss: 0.6927 - binary_accuracy: 0.5101"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 38/625 [>.............................] - ETA: 1:31 - loss: 0.6927 - binary_accuracy: 0.5115"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 39/625 [>.............................] - ETA: 1:31 - loss: 0.6927 - binary_accuracy: 0.5120"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 40/625 [>.............................] - ETA: 1:31 - loss: 0.6927 - binary_accuracy: 0.5141"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 41/625 [>.............................] - ETA: 1:31 - loss: 0.6926 - binary_accuracy: 0.5183"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 42/625 [=>............................] - ETA: 1:31 - loss: 0.6926 - binary_accuracy: 0.5186"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 43/625 [=>............................] - ETA: 1:31 - loss: 0.6926 - binary_accuracy: 0.5182"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 44/625 [=>............................] - ETA: 1:31 - loss: 0.6925 - binary_accuracy: 0.5206"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 46/625 [=>............................] - ETA: 1:29 - loss: 0.6925 - binary_accuracy: 0.5224"
]
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 47/625 [=>............................] - ETA: 1:29 - loss: 0.6924 - binary_accuracy: 0.5246"
]
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 48/625 [=>............................] - ETA: 1:28 - loss: 0.6924 - binary_accuracy: 0.5260"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 49/625 [=>............................] - ETA: 1:28 - loss: 0.6923 - binary_accuracy: 0.5287"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 50/625 [=>............................] - ETA: 1:28 - loss: 0.6923 - binary_accuracy: 0.5275"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 51/625 [=>............................] - ETA: 1:28 - loss: 0.6923 - binary_accuracy: 0.5276"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 52/625 [=>............................] - ETA: 1:28 - loss: 0.6923 - binary_accuracy: 0.5282"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 54/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5324"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 55/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5318"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 56/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5307"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 57/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5291"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 58/625 [=>............................] - ETA: 1:26 - loss: 0.6921 - binary_accuracy: 0.5296"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 59/625 [=>............................] - ETA: 1:26 - loss: 0.6922 - binary_accuracy: 0.5291"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 60/625 [=>............................] - ETA: 1:26 - loss: 0.6921 - binary_accuracy: 0.5307"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 61/625 [=>............................] - ETA: 1:26 - loss: 0.6921 - binary_accuracy: 0.5302"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 62/625 [=>............................] - ETA: 1:26 - loss: 0.6920 - binary_accuracy: 0.5328"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 63/625 [==>...........................] - ETA: 1:26 - loss: 0.6920 - binary_accuracy: 0.5327"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 64/625 [==>...........................] - ETA: 1:26 - loss: 0.6920 - binary_accuracy: 0.5317"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 65/625 [==>...........................] - ETA: 1:26 - loss: 0.6920 - binary_accuracy: 0.5322"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 68/625 [==>...........................] - ETA: 1:23 - loss: 0.6920 - binary_accuracy: 0.5294"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 70/625 [==>...........................] - ETA: 1:22 - loss: 0.6920 - binary_accuracy: 0.5286"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 71/625 [==>...........................] - ETA: 1:22 - loss: 0.6920 - binary_accuracy: 0.5286"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 73/625 [==>...........................] - ETA: 1:20 - loss: 0.6920 - binary_accuracy: 0.5274"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 74/625 [==>...........................] - ETA: 1:20 - loss: 0.6919 - binary_accuracy: 0.5283"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 76/625 [==>...........................] - ETA: 1:19 - loss: 0.6918 - binary_accuracy: 0.5304"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 77/625 [==>...........................] - ETA: 1:19 - loss: 0.6917 - binary_accuracy: 0.5296"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 78/625 [==>...........................] - ETA: 1:19 - loss: 0.6918 - binary_accuracy: 0.5272"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 79/625 [==>...........................] - ETA: 1:19 - loss: 0.6917 - binary_accuracy: 0.5285"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 80/625 [==>...........................] - ETA: 1:19 - loss: 0.6916 - binary_accuracy: 0.5289"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 83/625 [==>...........................] - ETA: 1:17 - loss: 0.6917 - binary_accuracy: 0.5267"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 84/625 [===>..........................] - ETA: 1:17 - loss: 0.6916 - binary_accuracy: 0.5272"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 85/625 [===>..........................] - ETA: 1:17 - loss: 0.6917 - binary_accuracy: 0.5265"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 86/625 [===>..........................] - ETA: 1:17 - loss: 0.6916 - binary_accuracy: 0.5273"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 88/625 [===>..........................] - ETA: 1:16 - loss: 0.6916 - binary_accuracy: 0.5266"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 89/625 [===>..........................] - ETA: 1:16 - loss: 0.6917 - binary_accuracy: 0.5242"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 90/625 [===>..........................] - ETA: 1:16 - loss: 0.6917 - binary_accuracy: 0.5236"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 91/625 [===>..........................] - ETA: 1:15 - loss: 0.6916 - binary_accuracy: 0.5234"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 93/625 [===>..........................] - ETA: 1:15 - loss: 0.6917 - binary_accuracy: 0.5215"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 96/625 [===>..........................] - ETA: 1:13 - loss: 0.6917 - binary_accuracy: 0.5202"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 98/625 [===>..........................] - ETA: 1:12 - loss: 0.6916 - binary_accuracy: 0.5204"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"101/625 [===>..........................] - ETA: 1:10 - loss: 0.6917 - binary_accuracy: 0.5170"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"102/625 [===>..........................] - ETA: 1:10 - loss: 0.6917 - binary_accuracy: 0.5162"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"104/625 [===>..........................] - ETA: 1:09 - loss: 0.6916 - binary_accuracy: 0.5168"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"105/625 [====>.........................] - ETA: 1:09 - loss: 0.6916 - binary_accuracy: 0.5167"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"107/625 [====>.........................] - ETA: 1:09 - loss: 0.6916 - binary_accuracy: 0.5158"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"108/625 [====>.........................] - ETA: 1:09 - loss: 0.6915 - binary_accuracy: 0.5174"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"109/625 [====>.........................] - ETA: 1:09 - loss: 0.6915 - binary_accuracy: 0.5181"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"110/625 [====>.........................] - ETA: 1:09 - loss: 0.6915 - binary_accuracy: 0.5188"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"111/625 [====>.........................] - ETA: 1:09 - loss: 0.6915 - binary_accuracy: 0.5191"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"112/625 [====>.........................] - ETA: 1:09 - loss: 0.6915 - binary_accuracy: 0.5195"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"113/625 [====>.........................] - ETA: 1:09 - loss: 0.6915 - binary_accuracy: 0.5191"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"115/625 [====>.........................] - ETA: 1:08 - loss: 0.6914 - binary_accuracy: 0.5207"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"117/625 [====>.........................] - ETA: 1:07 - loss: 0.6914 - binary_accuracy: 0.5214"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"119/625 [====>.........................] - ETA: 1:07 - loss: 0.6914 - binary_accuracy: 0.5239"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"120/625 [====>.........................] - ETA: 1:07 - loss: 0.6914 - binary_accuracy: 0.5247"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"121/625 [====>.........................] - ETA: 1:07 - loss: 0.6914 - binary_accuracy: 0.5261"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"122/625 [====>.........................] - ETA: 1:07 - loss: 0.6913 - binary_accuracy: 0.5282"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"124/625 [====>.........................] - ETA: 1:06 - loss: 0.6913 - binary_accuracy: 0.5315"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"125/625 [=====>........................] - ETA: 1:06 - loss: 0.6912 - binary_accuracy: 0.5328"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"127/625 [=====>........................] - ETA: 1:05 - loss: 0.6912 - binary_accuracy: 0.5349"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"130/625 [=====>........................] - ETA: 1:04 - loss: 0.6911 - binary_accuracy: 0.5380"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"131/625 [=====>........................] - ETA: 1:04 - loss: 0.6911 - binary_accuracy: 0.5401"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"134/625 [=====>........................] - ETA: 1:03 - loss: 0.6910 - binary_accuracy: 0.5445"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"136/625 [=====>........................] - ETA: 1:02 - loss: 0.6910 - binary_accuracy: 0.5464"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"137/625 [=====>........................] - ETA: 1:02 - loss: 0.6910 - binary_accuracy: 0.5484"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"145/625 [=====>........................] - ETA: 1:00 - loss: 0.6908 - binary_accuracy: 0.5582"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"146/625 [======>.......................] - ETA: 1:00 - loss: 0.6908 - binary_accuracy: 0.5591"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"147/625 [======>.......................] - ETA: 1:00 - loss: 0.6908 - binary_accuracy: 0.5593"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"148/625 [======>.......................] - ETA: 1:00 - loss: 0.6907 - binary_accuracy: 0.5604"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"149/625 [======>.......................] - ETA: 1:00 - loss: 0.6907 - binary_accuracy: 0.5606"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"151/625 [======>.......................] - ETA: 59s - loss: 0.6907 - binary_accuracy: 0.5621 "
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"152/625 [======>.......................] - ETA: 59s - loss: 0.6907 - binary_accuracy: 0.5635"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"162/625 [======>.......................] - ETA: 56s - loss: 0.6904 - binary_accuracy: 0.5718"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"164/625 [======>.......................] - ETA: 55s - loss: 0.6904 - binary_accuracy: 0.5726"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"165/625 [======>.......................] - ETA: 55s - loss: 0.6903 - binary_accuracy: 0.5729"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"166/625 [======>.......................] - ETA: 55s - loss: 0.6903 - binary_accuracy: 0.5745"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"172/625 [=======>......................] - ETA: 55s - loss: 0.6901 - binary_accuracy: 0.5796"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"192/625 [========>.....................] - ETA: 51s - loss: 0.6896 - binary_accuracy: 0.5812"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"195/625 [========>.....................] - ETA: 51s - loss: 0.6895 - binary_accuracy: 0.5804"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"196/625 [========>.....................] - ETA: 51s - loss: 0.6895 - binary_accuracy: 0.5805"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"197/625 [========>.....................] - ETA: 51s - loss: 0.6896 - binary_accuracy: 0.5795"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"202/625 [========>.....................] - ETA: 50s - loss: 0.6894 - binary_accuracy: 0.5792"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"206/625 [========>.....................] - ETA: 49s - loss: 0.6894 - binary_accuracy: 0.5784"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"207/625 [========>.....................] - ETA: 49s - loss: 0.6893 - binary_accuracy: 0.5785"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"211/625 [=========>....................] - ETA: 48s - loss: 0.6892 - binary_accuracy: 0.5803"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"213/625 [=========>....................] - ETA: 48s - loss: 0.6892 - binary_accuracy: 0.5810"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"217/625 [=========>....................] - ETA: 47s - loss: 0.6890 - binary_accuracy: 0.5853"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"218/625 [=========>....................] - ETA: 47s - loss: 0.6889 - binary_accuracy: 0.5857"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"219/625 [=========>....................] - ETA: 47s - loss: 0.6889 - binary_accuracy: 0.5863"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"220/625 [=========>....................] - ETA: 47s - loss: 0.6889 - binary_accuracy: 0.5868"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"221/625 [=========>....................] - ETA: 47s - loss: 0.6888 - binary_accuracy: 0.5874"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"224/625 [=========>....................] - ETA: 46s - loss: 0.6887 - binary_accuracy: 0.5890"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"225/625 [=========>....................] - ETA: 46s - loss: 0.6887 - binary_accuracy: 0.5903"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"228/625 [=========>....................] - ETA: 46s - loss: 0.6886 - binary_accuracy: 0.5917"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"233/625 [==========>...................] - ETA: 45s - loss: 0.6884 - binary_accuracy: 0.5955"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"235/625 [==========>...................] - ETA: 44s - loss: 0.6883 - binary_accuracy: 0.5965"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"237/625 [==========>...................] - ETA: 44s - loss: 0.6883 - binary_accuracy: 0.5980"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"238/625 [==========>...................] - ETA: 44s - loss: 0.6882 - binary_accuracy: 0.5989"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"240/625 [==========>...................] - ETA: 44s - loss: 0.6881 - binary_accuracy: 0.5999"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"241/625 [==========>...................] - ETA: 44s - loss: 0.6881 - binary_accuracy: 0.6004"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"244/625 [==========>...................] - ETA: 43s - loss: 0.6879 - binary_accuracy: 0.6022"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"245/625 [==========>...................] - ETA: 43s - loss: 0.6879 - binary_accuracy: 0.6028"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"247/625 [==========>...................] - ETA: 43s - loss: 0.6878 - binary_accuracy: 0.6034"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"249/625 [==========>...................] - ETA: 42s - loss: 0.6878 - binary_accuracy: 0.6048"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"251/625 [===========>..................] - ETA: 42s - loss: 0.6877 - binary_accuracy: 0.6060"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"253/625 [===========>..................] - ETA: 42s - loss: 0.6876 - binary_accuracy: 0.6066"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"254/625 [===========>..................] - ETA: 42s - loss: 0.6876 - binary_accuracy: 0.6070"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"256/625 [===========>..................] - ETA: 41s - loss: 0.6875 - binary_accuracy: 0.6078"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"259/625 [===========>..................] - ETA: 41s - loss: 0.6874 - binary_accuracy: 0.6094"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"261/625 [===========>..................] - ETA: 41s - loss: 0.6873 - binary_accuracy: 0.6103"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"262/625 [===========>..................] - ETA: 41s - loss: 0.6872 - binary_accuracy: 0.6110"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"264/625 [===========>..................] - ETA: 41s - loss: 0.6871 - binary_accuracy: 0.6114"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"266/625 [===========>..................] - ETA: 40s - loss: 0.6870 - binary_accuracy: 0.6118"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"267/625 [===========>..................] - ETA: 40s - loss: 0.6870 - binary_accuracy: 0.6120"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"269/625 [===========>..................] - ETA: 40s - loss: 0.6869 - binary_accuracy: 0.6127"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"270/625 [===========>..................] - ETA: 40s - loss: 0.6869 - binary_accuracy: 0.6125"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"277/625 [============>.................] - ETA: 39s - loss: 0.6866 - binary_accuracy: 0.6148"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"281/625 [============>.................] - ETA: 38s - loss: 0.6864 - binary_accuracy: 0.6165"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"291/625 [============>.................] - ETA: 36s - loss: 0.6861 - binary_accuracy: 0.6200"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"293/625 [=============>................] - ETA: 36s - loss: 0.6860 - binary_accuracy: 0.6206"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"301/625 [=============>................] - ETA: 35s - loss: 0.6857 - binary_accuracy: 0.6231"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"302/625 [=============>................] - ETA: 34s - loss: 0.6856 - binary_accuracy: 0.6238"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"306/625 [=============>................] - ETA: 34s - loss: 0.6854 - binary_accuracy: 0.6246"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"307/625 [=============>................] - ETA: 34s - loss: 0.6854 - binary_accuracy: 0.6252"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"308/625 [=============>................] - ETA: 34s - loss: 0.6853 - binary_accuracy: 0.6257"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"445/625 [====================>.........] - ETA: 15s - loss: 0.6776 - binary_accuracy: 0.6623"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"454/625 [====================>.........] - ETA: 14s - loss: 0.6770 - binary_accuracy: 0.6643"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"456/625 [====================>.........] - ETA: 14s - loss: 0.6769 - binary_accuracy: 0.6650"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"464/625 [=====================>........] - ETA: 13s - loss: 0.6764 - binary_accuracy: 0.6659"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"465/625 [=====================>........] - ETA: 13s - loss: 0.6763 - binary_accuracy: 0.6664"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"470/625 [=====================>........] - ETA: 13s - loss: 0.6761 - binary_accuracy: 0.6669"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"488/625 [======================>.......] - ETA: 11s - loss: 0.6748 - binary_accuracy: 0.6698"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"490/625 [======================>.......] - ETA: 11s - loss: 0.6746 - binary_accuracy: 0.6703"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"497/625 [======================>.......] - ETA: 10s - loss: 0.6741 - binary_accuracy: 0.6716"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"499/625 [======================>.......] - ETA: 10s - loss: 0.6740 - binary_accuracy: 0.6720"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"509/625 [=======================>......] - ETA: 9s - loss: 0.6732 - binary_accuracy: 0.6741 "
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"513/625 [=======================>......] - ETA: 9s - loss: 0.6730 - binary_accuracy: 0.6744"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"521/625 [========================>.....] - ETA: 8s - loss: 0.6723 - binary_accuracy: 0.6763"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"524/625 [========================>.....] - ETA: 8s - loss: 0.6722 - binary_accuracy: 0.6762"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"526/625 [========================>.....] - ETA: 7s - loss: 0.6720 - binary_accuracy: 0.6768"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"530/625 [========================>.....] - ETA: 7s - loss: 0.6717 - binary_accuracy: 0.6774"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"533/625 [========================>.....] - ETA: 7s - loss: 0.6715 - binary_accuracy: 0.6778"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"535/625 [========================>.....] - ETA: 7s - loss: 0.6714 - binary_accuracy: 0.6778"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"538/625 [========================>.....] - ETA: 6s - loss: 0.6713 - binary_accuracy: 0.6782"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"541/625 [========================>.....] - ETA: 6s - loss: 0.6710 - binary_accuracy: 0.6789"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"544/625 [=========================>....] - ETA: 6s - loss: 0.6708 - binary_accuracy: 0.6794"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"546/625 [=========================>....] - ETA: 6s - loss: 0.6707 - binary_accuracy: 0.6795"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"547/625 [=========================>....] - ETA: 6s - loss: 0.6705 - binary_accuracy: 0.6800"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"560/625 [=========================>....] - ETA: 5s - loss: 0.6696 - binary_accuracy: 0.6814"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"564/625 [==========================>...] - ETA: 4s - loss: 0.6694 - binary_accuracy: 0.6815"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"572/625 [==========================>...] - ETA: 4s - loss: 0.6687 - binary_accuracy: 0.6833"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"577/625 [==========================>...] - ETA: 3s - loss: 0.6684 - binary_accuracy: 0.6837"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"582/625 [==========================>...] - ETA: 3s - loss: 0.6680 - binary_accuracy: 0.6843"
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"591/625 [===========================>..] - ETA: 2s - loss: 0.6673 - binary_accuracy: 0.6855"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"600/625 [===========================>..] - ETA: 1s - loss: 0.6665 - binary_accuracy: 0.6872"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"602/625 [===========================>..] - ETA: 1s - loss: 0.6662 - binary_accuracy: 0.6877"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"608/625 [============================>.] - ETA: 1s - loss: 0.6658 - binary_accuracy: 0.6886"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"609/625 [============================>.] - ETA: 1s - loss: 0.6657 - binary_accuracy: 0.6887"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"615/625 [============================>.] - ETA: 0s - loss: 0.6652 - binary_accuracy: 0.6895"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"619/625 [============================>.] - ETA: 0s - loss: 0.6649 - binary_accuracy: 0.6899"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 47s 74ms/step - loss: 0.6645 - binary_accuracy: 0.6906 - val_loss: 0.6151 - val_binary_accuracy: 0.7676\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 2/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.5807 - binary_accuracy: 0.8438"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/625 [..............................] - ETA: 1s - loss: 0.6109 - binary_accuracy: 0.7656"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 35/625 [>.............................] - ETA: 1s - loss: 0.6090 - binary_accuracy: 0.7634"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 53/625 [=>............................] - ETA: 1s - loss: 0.6047 - binary_accuracy: 0.7665"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 71/625 [==>...........................] - ETA: 1s - loss: 0.6053 - binary_accuracy: 0.7676"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 89/625 [===>..........................] - ETA: 1s - loss: 0.6017 - binary_accuracy: 0.7700"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"107/625 [====>.........................] - ETA: 1s - loss: 0.5994 - binary_accuracy: 0.7728"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"125/625 [=====>........................] - ETA: 1s - loss: 0.5981 - binary_accuracy: 0.7722"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"142/625 [=====>........................] - ETA: 1s - loss: 0.5968 - binary_accuracy: 0.7733"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"160/625 [======>.......................] - ETA: 1s - loss: 0.5960 - binary_accuracy: 0.7719"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"178/625 [=======>......................] - ETA: 1s - loss: 0.5937 - binary_accuracy: 0.7742"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"196/625 [========>.....................] - ETA: 1s - loss: 0.5928 - binary_accuracy: 0.7738"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"214/625 [=========>....................] - ETA: 1s - loss: 0.5908 - binary_accuracy: 0.7761"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"232/625 [==========>...................] - ETA: 1s - loss: 0.5881 - binary_accuracy: 0.7786"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"250/625 [===========>..................] - ETA: 1s - loss: 0.5856 - binary_accuracy: 0.7796"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"268/625 [===========>..................] - ETA: 1s - loss: 0.5832 - binary_accuracy: 0.7816"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"285/625 [============>.................] - ETA: 0s - loss: 0.5816 - binary_accuracy: 0.7829"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"302/625 [=============>................] - ETA: 0s - loss: 0.5806 - binary_accuracy: 0.7824"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"319/625 [==============>...............] - ETA: 0s - loss: 0.5794 - binary_accuracy: 0.7815"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"353/625 [===============>..............] - ETA: 0s - loss: 0.5764 - binary_accuracy: 0.7840"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"370/625 [================>.............] - ETA: 0s - loss: 0.5744 - binary_accuracy: 0.7854"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"386/625 [=================>............] - ETA: 0s - loss: 0.5724 - binary_accuracy: 0.7868"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"402/625 [==================>...........] - ETA: 0s - loss: 0.5703 - binary_accuracy: 0.7886"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"419/625 [===================>..........] - ETA: 0s - loss: 0.5682 - binary_accuracy: 0.7899"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"436/625 [===================>..........] - ETA: 0s - loss: 0.5665 - binary_accuracy: 0.7906"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"453/625 [====================>.........] - ETA: 0s - loss: 0.5646 - binary_accuracy: 0.7919"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"470/625 [=====================>........] - ETA: 0s - loss: 0.5632 - binary_accuracy: 0.7933"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"487/625 [======================>.......] - ETA: 0s - loss: 0.5616 - binary_accuracy: 0.7943"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"504/625 [=======================>......] - ETA: 0s - loss: 0.5597 - binary_accuracy: 0.7959"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"521/625 [========================>.....] - ETA: 0s - loss: 0.5578 - binary_accuracy: 0.7970"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"538/625 [========================>.....] - ETA: 0s - loss: 0.5566 - binary_accuracy: 0.7968"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"555/625 [=========================>....] - ETA: 0s - loss: 0.5550 - binary_accuracy: 0.7979"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"572/625 [==========================>...] - ETA: 0s - loss: 0.5533 - binary_accuracy: 0.7986"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"589/625 [===========================>..] - ETA: 0s - loss: 0.5522 - binary_accuracy: 0.7988"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"606/625 [============================>.] - ETA: 0s - loss: 0.5505 - binary_accuracy: 0.7996"
]
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"623/625 [============================>.] - ETA: 0s - loss: 0.5492 - binary_accuracy: 0.8000"
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.5489 - binary_accuracy: 0.8001 - val_loss: 0.4983 - val_binary_accuracy: 0.8222\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 3/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.4445 - binary_accuracy: 0.9062"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/625 [..............................] - ETA: 1s - loss: 0.4852 - binary_accuracy: 0.8316"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 36/625 [>.............................] - ETA: 1s - loss: 0.4868 - binary_accuracy: 0.8255"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 53/625 [=>............................] - ETA: 1s - loss: 0.4831 - binary_accuracy: 0.8284"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 69/625 [==>...........................] - ETA: 1s - loss: 0.4844 - binary_accuracy: 0.8293"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 85/625 [===>..........................] - ETA: 1s - loss: 0.4812 - binary_accuracy: 0.8309"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"101/625 [===>..........................] - ETA: 1s - loss: 0.4811 - binary_accuracy: 0.8308"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"118/625 [====>.........................] - ETA: 1s - loss: 0.4801 - binary_accuracy: 0.8316"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"135/625 [=====>........................] - ETA: 1s - loss: 0.4782 - binary_accuracy: 0.8308"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"153/625 [======>.......................] - ETA: 1s - loss: 0.4788 - binary_accuracy: 0.8297"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"171/625 [=======>......................] - ETA: 1s - loss: 0.4772 - binary_accuracy: 0.8308"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"189/625 [========>.....................] - ETA: 1s - loss: 0.4757 - binary_accuracy: 0.8312"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"207/625 [========>.....................] - ETA: 1s - loss: 0.4754 - binary_accuracy: 0.8312"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"225/625 [=========>....................] - ETA: 1s - loss: 0.4732 - binary_accuracy: 0.8319"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"242/625 [==========>...................] - ETA: 1s - loss: 0.4715 - binary_accuracy: 0.8325"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"259/625 [===========>..................] - ETA: 1s - loss: 0.4693 - binary_accuracy: 0.8339"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"276/625 [============>.................] - ETA: 1s - loss: 0.4670 - binary_accuracy: 0.8361"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"293/625 [=============>................] - ETA: 1s - loss: 0.4680 - binary_accuracy: 0.8340"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"412/625 [==================>...........] - ETA: 0s - loss: 0.4585 - binary_accuracy: 0.8397"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"480/625 [======================>.......] - ETA: 0s - loss: 0.4540 - binary_accuracy: 0.8417"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"497/625 [======================>.......] - ETA: 0s - loss: 0.4521 - binary_accuracy: 0.8431"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"514/625 [=======================>......] - ETA: 0s - loss: 0.4506 - binary_accuracy: 0.8441"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"531/625 [========================>.....] - ETA: 0s - loss: 0.4497 - binary_accuracy: 0.8443"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"549/625 [=========================>....] - ETA: 0s - loss: 0.4486 - binary_accuracy: 0.8450"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"566/625 [==========================>...] - ETA: 0s - loss: 0.4478 - binary_accuracy: 0.8454"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"583/625 [==========================>...] - ETA: 0s - loss: 0.4472 - binary_accuracy: 0.8453"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"601/625 [===========================>..] - ETA: 0s - loss: 0.4461 - binary_accuracy: 0.8458"
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"619/625 [============================>.] - ETA: 0s - loss: 0.4457 - binary_accuracy: 0.8455"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.4452 - binary_accuracy: 0.8454 - val_loss: 0.4201 - val_binary_accuracy: 0.8474\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 4/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.3707 - binary_accuracy: 0.9062"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 19/625 [..............................] - ETA: 1s - loss: 0.4023 - binary_accuracy: 0.8651"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 36/625 [>.............................] - ETA: 1s - loss: 0.4012 - binary_accuracy: 0.8628"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 53/625 [=>............................] - ETA: 1s - loss: 0.4001 - binary_accuracy: 0.8620"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 70/625 [==>...........................] - ETA: 1s - loss: 0.4027 - binary_accuracy: 0.8634"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 88/625 [===>..........................] - ETA: 1s - loss: 0.3991 - binary_accuracy: 0.8640"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"139/625 [=====>........................] - ETA: 1s - loss: 0.3969 - binary_accuracy: 0.8620"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"157/625 [======>.......................] - ETA: 1s - loss: 0.3997 - binary_accuracy: 0.8597"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"175/625 [=======>......................] - ETA: 1s - loss: 0.3973 - binary_accuracy: 0.8612"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"192/625 [========>.....................] - ETA: 1s - loss: 0.3968 - binary_accuracy: 0.8607"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"399/625 [==================>...........] - ETA: 0s - loss: 0.3870 - binary_accuracy: 0.8625"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"416/625 [==================>...........] - ETA: 0s - loss: 0.3857 - binary_accuracy: 0.8634"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"450/625 [====================>.........] - ETA: 0s - loss: 0.3841 - binary_accuracy: 0.8639"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"468/625 [=====================>........] - ETA: 0s - loss: 0.3828 - binary_accuracy: 0.8650"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"486/625 [======================>.......] - ETA: 0s - loss: 0.3827 - binary_accuracy: 0.8654"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"503/625 [=======================>......] - ETA: 0s - loss: 0.3812 - binary_accuracy: 0.8663"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"521/625 [========================>.....] - ETA: 0s - loss: 0.3800 - binary_accuracy: 0.8663"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"538/625 [========================>.....] - ETA: 0s - loss: 0.3799 - binary_accuracy: 0.8663"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"556/625 [=========================>....] - ETA: 0s - loss: 0.3794 - binary_accuracy: 0.8668"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"573/625 [==========================>...] - ETA: 0s - loss: 0.3787 - binary_accuracy: 0.8670"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"590/625 [===========================>..] - ETA: 0s - loss: 0.3792 - binary_accuracy: 0.8663"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"608/625 [============================>.] - ETA: 0s - loss: 0.3790 - binary_accuracy: 0.8663"
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - ETA: 0s - loss: 0.3784 - binary_accuracy: 0.8662"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.3784 - binary_accuracy: 0.8662 - val_loss: 0.3737 - val_binary_accuracy: 0.8602\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 5/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.3303 - binary_accuracy: 0.9062"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 17/625 [..............................] - ETA: 1s - loss: 0.3472 - binary_accuracy: 0.8934"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 34/625 [>.............................] - ETA: 1s - loss: 0.3469 - binary_accuracy: 0.8787"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 51/625 [=>............................] - ETA: 1s - loss: 0.3483 - binary_accuracy: 0.8738"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 68/625 [==>...........................] - ETA: 1s - loss: 0.3477 - binary_accuracy: 0.8759"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 85/625 [===>..........................] - ETA: 1s - loss: 0.3467 - binary_accuracy: 0.8735"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"240/625 [==========>...................] - ETA: 1s - loss: 0.3472 - binary_accuracy: 0.8746"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"327/625 [==============>...............] - ETA: 0s - loss: 0.3457 - binary_accuracy: 0.8744"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"378/625 [=================>............] - ETA: 0s - loss: 0.3431 - binary_accuracy: 0.8746"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"412/625 [==================>...........] - ETA: 0s - loss: 0.3407 - binary_accuracy: 0.8764"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"429/625 [===================>..........] - ETA: 0s - loss: 0.3393 - binary_accuracy: 0.8772"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"446/625 [====================>.........] - ETA: 0s - loss: 0.3382 - binary_accuracy: 0.8777"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"464/625 [=====================>........] - ETA: 0s - loss: 0.3380 - binary_accuracy: 0.8776"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"482/625 [======================>.......] - ETA: 0s - loss: 0.3381 - binary_accuracy: 0.8782"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"499/625 [======================>.......] - ETA: 0s - loss: 0.3368 - binary_accuracy: 0.8790"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"516/625 [=======================>......] - ETA: 0s - loss: 0.3359 - binary_accuracy: 0.8795"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"534/625 [========================>.....] - ETA: 0s - loss: 0.3359 - binary_accuracy: 0.8789"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"552/625 [=========================>....] - ETA: 0s - loss: 0.3358 - binary_accuracy: 0.8787"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"570/625 [==========================>...] - ETA: 0s - loss: 0.3353 - binary_accuracy: 0.8791"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"589/625 [===========================>..] - ETA: 0s - loss: 0.3359 - binary_accuracy: 0.8786"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"607/625 [============================>.] - ETA: 0s - loss: 0.3361 - binary_accuracy: 0.8788"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - ETA: 0s - loss: 0.3355 - binary_accuracy: 0.8788"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.3355 - binary_accuracy: 0.8788 - val_loss: 0.3450 - val_binary_accuracy: 0.8670\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 6/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.2845 - binary_accuracy: 0.9062"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/625 [..............................] - ETA: 1s - loss: 0.3080 - binary_accuracy: 0.9062"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 35/625 [>.............................] - ETA: 1s - loss: 0.3110 - binary_accuracy: 0.8946"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 52/625 [=>............................] - ETA: 1s - loss: 0.3131 - binary_accuracy: 0.8906"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 69/625 [==>...........................] - ETA: 1s - loss: 0.3152 - binary_accuracy: 0.8881"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 87/625 [===>..........................] - ETA: 1s - loss: 0.3131 - binary_accuracy: 0.8876"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"105/625 [====>.........................] - ETA: 1s - loss: 0.3173 - binary_accuracy: 0.8848"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"122/625 [====>.........................] - ETA: 1s - loss: 0.3163 - binary_accuracy: 0.8868"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"139/625 [=====>........................] - ETA: 1s - loss: 0.3135 - binary_accuracy: 0.8860"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"156/625 [======>.......................] - ETA: 1s - loss: 0.3171 - binary_accuracy: 0.8836"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"174/625 [=======>......................] - ETA: 1s - loss: 0.3149 - binary_accuracy: 0.8847"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"192/625 [========>.....................] - ETA: 1s - loss: 0.3137 - binary_accuracy: 0.8846"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"210/625 [=========>....................] - ETA: 1s - loss: 0.3153 - binary_accuracy: 0.8839"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"228/625 [=========>....................] - ETA: 1s - loss: 0.3149 - binary_accuracy: 0.8834"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"245/625 [==========>...................] - ETA: 1s - loss: 0.3143 - binary_accuracy: 0.8837"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"262/625 [===========>..................] - ETA: 1s - loss: 0.3133 - binary_accuracy: 0.8850"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"279/625 [============>.................] - ETA: 1s - loss: 0.3112 - binary_accuracy: 0.8865"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"297/625 [=============>................] - ETA: 0s - loss: 0.3139 - binary_accuracy: 0.8840"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"314/625 [==============>...............] - ETA: 0s - loss: 0.3140 - binary_accuracy: 0.8837"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"332/625 [==============>...............] - ETA: 0s - loss: 0.3137 - binary_accuracy: 0.8838"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"349/625 [===============>..............] - ETA: 0s - loss: 0.3129 - binary_accuracy: 0.8840"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"366/625 [================>.............] - ETA: 0s - loss: 0.3114 - binary_accuracy: 0.8845"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"384/625 [=================>............] - ETA: 0s - loss: 0.3111 - binary_accuracy: 0.8844"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"402/625 [==================>...........] - ETA: 0s - loss: 0.3096 - binary_accuracy: 0.8851"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"419/625 [===================>..........] - ETA: 0s - loss: 0.3085 - binary_accuracy: 0.8857"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"436/625 [===================>..........] - ETA: 0s - loss: 0.3076 - binary_accuracy: 0.8862"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"453/625 [====================>.........] - ETA: 0s - loss: 0.3071 - binary_accuracy: 0.8865"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"471/625 [=====================>........] - ETA: 0s - loss: 0.3070 - binary_accuracy: 0.8869"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"489/625 [======================>.......] - ETA: 0s - loss: 0.3065 - binary_accuracy: 0.8874"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"506/625 [=======================>......] - ETA: 0s - loss: 0.3054 - binary_accuracy: 0.8877"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"523/625 [========================>.....] - ETA: 0s - loss: 0.3050 - binary_accuracy: 0.8879"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"540/625 [========================>.....] - ETA: 0s - loss: 0.3054 - binary_accuracy: 0.8873"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"558/625 [=========================>....] - ETA: 0s - loss: 0.3049 - binary_accuracy: 0.8880"
]
},
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"575/625 [==========================>...] - ETA: 0s - loss: 0.3050 - binary_accuracy: 0.8879"
]
},
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"592/625 [===========================>..] - ETA: 0s - loss: 0.3055 - binary_accuracy: 0.8880"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"609/625 [============================>.] - ETA: 0s - loss: 0.3061 - binary_accuracy: 0.8877"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.3057 - binary_accuracy: 0.8880 - val_loss: 0.3263 - val_binary_accuracy: 0.8720\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 7/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.2590 - binary_accuracy: 0.9062"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 19/625 [..............................] - ETA: 1s - loss: 0.2842 - binary_accuracy: 0.9112"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 37/625 [>.............................] - ETA: 1s - loss: 0.2857 - binary_accuracy: 0.9062"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 55/625 [=>............................] - ETA: 1s - loss: 0.2852 - binary_accuracy: 0.9023"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 73/625 [==>...........................] - ETA: 1s - loss: 0.2915 - binary_accuracy: 0.8964"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 90/625 [===>..........................] - ETA: 1s - loss: 0.2890 - binary_accuracy: 0.8990"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"107/625 [====>.........................] - ETA: 1s - loss: 0.2925 - binary_accuracy: 0.8957"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"124/625 [====>.........................] - ETA: 1s - loss: 0.2914 - binary_accuracy: 0.8969"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"141/625 [=====>........................] - ETA: 1s - loss: 0.2892 - binary_accuracy: 0.8956"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"158/625 [======>.......................] - ETA: 1s - loss: 0.2927 - binary_accuracy: 0.8936"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"175/625 [=======>......................] - ETA: 1s - loss: 0.2891 - binary_accuracy: 0.8952"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"192/625 [========>.....................] - ETA: 1s - loss: 0.2885 - binary_accuracy: 0.8944"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"209/625 [=========>....................] - ETA: 1s - loss: 0.2892 - binary_accuracy: 0.8949"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"227/625 [=========>....................] - ETA: 1s - loss: 0.2899 - binary_accuracy: 0.8943"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"245/625 [==========>...................] - ETA: 1s - loss: 0.2892 - binary_accuracy: 0.8941"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"262/625 [===========>..................] - ETA: 1s - loss: 0.2884 - binary_accuracy: 0.8952"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"279/625 [============>.................] - ETA: 1s - loss: 0.2863 - binary_accuracy: 0.8971"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"297/625 [=============>................] - ETA: 0s - loss: 0.2892 - binary_accuracy: 0.8947"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"314/625 [==============>...............] - ETA: 0s - loss: 0.2891 - binary_accuracy: 0.8947"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"332/625 [==============>...............] - ETA: 0s - loss: 0.2890 - binary_accuracy: 0.8944"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"349/625 [===============>..............] - ETA: 0s - loss: 0.2879 - binary_accuracy: 0.8940"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"366/625 [================>.............] - ETA: 0s - loss: 0.2865 - binary_accuracy: 0.8945"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"383/625 [=================>............] - ETA: 0s - loss: 0.2859 - binary_accuracy: 0.8946"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"400/625 [==================>...........] - ETA: 0s - loss: 0.2852 - binary_accuracy: 0.8950"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"417/625 [===================>..........] - ETA: 0s - loss: 0.2842 - binary_accuracy: 0.8957"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"434/625 [===================>..........] - ETA: 0s - loss: 0.2832 - binary_accuracy: 0.8964"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"452/625 [====================>.........] - ETA: 0s - loss: 0.2829 - binary_accuracy: 0.8962"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"470/625 [=====================>........] - ETA: 0s - loss: 0.2825 - binary_accuracy: 0.8968"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"488/625 [======================>.......] - ETA: 0s - loss: 0.2824 - binary_accuracy: 0.8968"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"506/625 [=======================>......] - ETA: 0s - loss: 0.2811 - binary_accuracy: 0.8972"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"524/625 [========================>.....] - ETA: 0s - loss: 0.2808 - binary_accuracy: 0.8972"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"542/625 [=========================>....] - ETA: 0s - loss: 0.2810 - binary_accuracy: 0.8967"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"559/625 [=========================>....] - ETA: 0s - loss: 0.2807 - binary_accuracy: 0.8973"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"576/625 [==========================>...] - ETA: 0s - loss: 0.2806 - binary_accuracy: 0.8974"
]
},
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"594/625 [===========================>..] - ETA: 0s - loss: 0.2811 - binary_accuracy: 0.8975"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"612/625 [============================>.] - ETA: 0s - loss: 0.2817 - binary_accuracy: 0.8970"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.2815 - binary_accuracy: 0.8972 - val_loss: 0.3131 - val_binary_accuracy: 0.8716\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 8/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.2767 - binary_accuracy: 0.9375"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 19/625 [..............................] - ETA: 1s - loss: 0.2635 - binary_accuracy: 0.9145"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 36/625 [>.............................] - ETA: 1s - loss: 0.2633 - binary_accuracy: 0.9106"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 53/625 [=>............................] - ETA: 1s - loss: 0.2603 - binary_accuracy: 0.9080"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 70/625 [==>...........................] - ETA: 1s - loss: 0.2633 - binary_accuracy: 0.9062"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 87/625 [===>..........................] - ETA: 1s - loss: 0.2620 - binary_accuracy: 0.9059"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"104/625 [===>..........................] - ETA: 1s - loss: 0.2686 - binary_accuracy: 0.9020"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"121/625 [====>.........................] - ETA: 1s - loss: 0.2673 - binary_accuracy: 0.9037"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"139/625 [=====>........................] - ETA: 1s - loss: 0.2656 - binary_accuracy: 0.9015"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"156/625 [======>.......................] - ETA: 1s - loss: 0.2684 - binary_accuracy: 0.8998"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"173/625 [=======>......................] - ETA: 1s - loss: 0.2668 - binary_accuracy: 0.8997"
]
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"191/625 [========>.....................] - ETA: 1s - loss: 0.2658 - binary_accuracy: 0.9012"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"209/625 [=========>....................] - ETA: 1s - loss: 0.2669 - binary_accuracy: 0.9016"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"226/625 [=========>....................] - ETA: 1s - loss: 0.2670 - binary_accuracy: 0.9013"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"244/625 [==========>...................] - ETA: 1s - loss: 0.2673 - binary_accuracy: 0.9002"
]
},
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"261/625 [===========>..................] - ETA: 1s - loss: 0.2665 - binary_accuracy: 0.9015"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"279/625 [============>.................] - ETA: 1s - loss: 0.2648 - binary_accuracy: 0.9033"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"296/625 [=============>................] - ETA: 0s - loss: 0.2676 - binary_accuracy: 0.9019"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"313/625 [==============>...............] - ETA: 0s - loss: 0.2678 - binary_accuracy: 0.9016"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"330/625 [==============>...............] - ETA: 0s - loss: 0.2673 - binary_accuracy: 0.9019"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"347/625 [===============>..............] - ETA: 0s - loss: 0.2666 - binary_accuracy: 0.9018"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"365/625 [================>.............] - ETA: 0s - loss: 0.2652 - binary_accuracy: 0.9025"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"383/625 [=================>............] - ETA: 0s - loss: 0.2651 - binary_accuracy: 0.9023"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"402/625 [==================>...........] - ETA: 0s - loss: 0.2643 - binary_accuracy: 0.9027"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"420/625 [===================>..........] - ETA: 0s - loss: 0.2636 - binary_accuracy: 0.9035"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"438/625 [====================>.........] - ETA: 0s - loss: 0.2626 - binary_accuracy: 0.9034"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"456/625 [====================>.........] - ETA: 0s - loss: 0.2619 - binary_accuracy: 0.9036"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"474/625 [=====================>........] - ETA: 0s - loss: 0.2624 - binary_accuracy: 0.9035"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"492/625 [======================>.......] - ETA: 0s - loss: 0.2615 - binary_accuracy: 0.9041"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"510/625 [=======================>......] - ETA: 0s - loss: 0.2601 - binary_accuracy: 0.9047"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"528/625 [========================>.....] - ETA: 0s - loss: 0.2603 - binary_accuracy: 0.9045"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"545/625 [=========================>....] - ETA: 0s - loss: 0.2610 - binary_accuracy: 0.9042"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"562/625 [=========================>....] - ETA: 0s - loss: 0.2602 - binary_accuracy: 0.9051"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"579/625 [==========================>...] - ETA: 0s - loss: 0.2611 - binary_accuracy: 0.9046"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"596/625 [===========================>..] - ETA: 0s - loss: 0.2616 - binary_accuracy: 0.9046"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"613/625 [============================>.] - ETA: 0s - loss: 0.2622 - binary_accuracy: 0.9041"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.2618 - binary_accuracy: 0.9043 - val_loss: 0.3036 - val_binary_accuracy: 0.8754\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 9/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.2473 - binary_accuracy: 0.9375"
]
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/625 [..............................] - ETA: 1s - loss: 0.2433 - binary_accuracy: 0.9167"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 36/625 [>.............................] - ETA: 1s - loss: 0.2474 - binary_accuracy: 0.9141"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 54/625 [=>............................] - ETA: 1s - loss: 0.2477 - binary_accuracy: 0.9161"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 72/625 [==>...........................] - ETA: 1s - loss: 0.2505 - binary_accuracy: 0.9123"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 90/625 [===>..........................] - ETA: 1s - loss: 0.2495 - binary_accuracy: 0.9139"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"108/625 [====>.........................] - ETA: 1s - loss: 0.2542 - binary_accuracy: 0.9123"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"126/625 [=====>........................] - ETA: 1s - loss: 0.2522 - binary_accuracy: 0.9129"
]
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"144/625 [=====>........................] - ETA: 1s - loss: 0.2513 - binary_accuracy: 0.9104"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"161/625 [======>.......................] - ETA: 1s - loss: 0.2536 - binary_accuracy: 0.9082"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"178/625 [=======>......................] - ETA: 1s - loss: 0.2517 - binary_accuracy: 0.9089"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"196/625 [========>.....................] - ETA: 1s - loss: 0.2519 - binary_accuracy: 0.9088"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"215/625 [=========>....................] - ETA: 1s - loss: 0.2514 - binary_accuracy: 0.9099"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"233/625 [==========>...................] - ETA: 1s - loss: 0.2527 - binary_accuracy: 0.9092"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"251/625 [===========>..................] - ETA: 1s - loss: 0.2507 - binary_accuracy: 0.9097"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"269/625 [===========>..................] - ETA: 1s - loss: 0.2505 - binary_accuracy: 0.9103"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"287/625 [============>.................] - ETA: 0s - loss: 0.2513 - binary_accuracy: 0.9101"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"305/625 [=============>................] - ETA: 0s - loss: 0.2522 - binary_accuracy: 0.9096"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"323/625 [==============>...............] - ETA: 0s - loss: 0.2525 - binary_accuracy: 0.9091"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"377/625 [=================>............] - ETA: 0s - loss: 0.2499 - binary_accuracy: 0.9100"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"413/625 [==================>...........] - ETA: 0s - loss: 0.2479 - binary_accuracy: 0.9107"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"432/625 [===================>..........] - ETA: 0s - loss: 0.2466 - binary_accuracy: 0.9112"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"450/625 [====================>.........] - ETA: 0s - loss: 0.2464 - binary_accuracy: 0.9108"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"468/625 [=====================>........] - ETA: 0s - loss: 0.2457 - binary_accuracy: 0.9114"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"486/625 [======================>.......] - ETA: 0s - loss: 0.2464 - binary_accuracy: 0.9108"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"504/625 [=======================>......] - ETA: 0s - loss: 0.2451 - binary_accuracy: 0.9113"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"522/625 [========================>.....] - ETA: 0s - loss: 0.2444 - binary_accuracy: 0.9116"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"540/625 [========================>.....] - ETA: 0s - loss: 0.2450 - binary_accuracy: 0.9111"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"558/625 [=========================>....] - ETA: 0s - loss: 0.2448 - binary_accuracy: 0.9116"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"576/625 [==========================>...] - ETA: 0s - loss: 0.2451 - binary_accuracy: 0.9115"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"594/625 [===========================>..] - ETA: 0s - loss: 0.2457 - binary_accuracy: 0.9112"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"612/625 [============================>.] - ETA: 0s - loss: 0.2465 - binary_accuracy: 0.9107"
]
},
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.2462 - binary_accuracy: 0.9106 - val_loss: 0.2971 - val_binary_accuracy: 0.8776\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 10/10\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/625 [..............................] - ETA: 2s - loss: 0.2502 - binary_accuracy: 0.9375"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 18/625 [..............................] - ETA: 1s - loss: 0.2283 - binary_accuracy: 0.9306"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 35/625 [>.............................] - ETA: 1s - loss: 0.2322 - binary_accuracy: 0.9268"
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 52/625 [=>............................] - ETA: 1s - loss: 0.2317 - binary_accuracy: 0.9261"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 69/625 [==>...........................] - ETA: 1s - loss: 0.2334 - binary_accuracy: 0.9244"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 87/625 [===>..........................] - ETA: 1s - loss: 0.2325 - binary_accuracy: 0.9224"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"105/625 [====>.........................] - ETA: 1s - loss: 0.2385 - binary_accuracy: 0.9193"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"123/625 [====>.........................] - ETA: 1s - loss: 0.2373 - binary_accuracy: 0.9192"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"140/625 [=====>........................] - ETA: 1s - loss: 0.2352 - binary_accuracy: 0.9176"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"158/625 [======>.......................] - ETA: 1s - loss: 0.2387 - binary_accuracy: 0.9146"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"175/625 [=======>......................] - ETA: 1s - loss: 0.2359 - binary_accuracy: 0.9155"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"193/625 [========>.....................] - ETA: 1s - loss: 0.2354 - binary_accuracy: 0.9161"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"210/625 [=========>....................] - ETA: 1s - loss: 0.2370 - binary_accuracy: 0.9159"
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"228/625 [=========>....................] - ETA: 1s - loss: 0.2373 - binary_accuracy: 0.9145"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"245/625 [==========>...................] - ETA: 1s - loss: 0.2370 - binary_accuracy: 0.9134"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"262/625 [===========>..................] - ETA: 1s - loss: 0.2367 - binary_accuracy: 0.9145"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"280/625 [============>.................] - ETA: 1s - loss: 0.2349 - binary_accuracy: 0.9157"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"298/625 [=============>................] - ETA: 0s - loss: 0.2372 - binary_accuracy: 0.9151"
]
},
{
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"315/625 [==============>...............] - ETA: 0s - loss: 0.2377 - binary_accuracy: 0.9151"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"333/625 [==============>...............] - ETA: 0s - loss: 0.2364 - binary_accuracy: 0.9155"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"351/625 [===============>..............] - ETA: 0s - loss: 0.2354 - binary_accuracy: 0.9152"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"369/625 [================>.............] - ETA: 0s - loss: 0.2345 - binary_accuracy: 0.9156"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"386/625 [=================>............] - ETA: 0s - loss: 0.2342 - binary_accuracy: 0.9159"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"403/625 [==================>...........] - ETA: 0s - loss: 0.2330 - binary_accuracy: 0.9162"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"421/625 [===================>..........] - ETA: 0s - loss: 0.2321 - binary_accuracy: 0.9167"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"439/625 [====================>.........] - ETA: 0s - loss: 0.2313 - binary_accuracy: 0.9165"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"457/625 [====================>.........] - ETA: 0s - loss: 0.2312 - binary_accuracy: 0.9165"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"474/625 [=====================>........] - ETA: 0s - loss: 0.2318 - binary_accuracy: 0.9163"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"492/625 [======================>.......] - ETA: 0s - loss: 0.2312 - binary_accuracy: 0.9164"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"509/625 [=======================>......] - ETA: 0s - loss: 0.2298 - binary_accuracy: 0.9171"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"527/625 [========================>.....] - ETA: 0s - loss: 0.2301 - binary_accuracy: 0.9169"
]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"544/625 [=========================>....] - ETA: 0s - loss: 0.2304 - binary_accuracy: 0.9165"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"562/625 [=========================>....] - ETA: 0s - loss: 0.2301 - binary_accuracy: 0.9170"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"580/625 [==========================>...] - ETA: 0s - loss: 0.2314 - binary_accuracy: 0.9167"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"597/625 [===========================>..] - ETA: 0s - loss: 0.2317 - binary_accuracy: 0.9164"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"614/625 [============================>.] - ETA: 0s - loss: 0.2324 - binary_accuracy: 0.9159"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"625/625 [==============================] - 2s 3ms/step - loss: 0.2322 - binary_accuracy: 0.9158 - val_loss: 0.2925 - val_binary_accuracy: 0.8790\n"
]
}
],
"source": [
"epochs = 10\n",
"history = model.fit(\n",
" train_ds,\n",
" validation_data=val_ds,\n",
" epochs=epochs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9EEGuDVuzb5r"
},
"source": [
"### 모델 평가하기\n",
"\n",
"모델의 성능을 확인해 보죠. 두 개의 값이 반환됩니다. 손실(오차를 나타내는 숫자이므로 낮을수록 좋습니다)과 정확도입니다."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:20:49.149216Z",
"iopub.status.busy": "2022-12-14T20:20:49.148750Z",
"iopub.status.idle": "2022-12-14T20:20:50.802300Z",
"shell.execute_reply": "2022-12-14T20:20:50.801378Z"
},
"id": "zOMKywn4zReN"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/782 [..............................] - ETA: 54s - loss: 0.3112 - binary_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\b\b\b\b\b\b\r",
" 27/782 [>.............................] - ETA: 1s - loss: 0.2977 - binary_accuracy: 0.8854 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 53/782 [=>............................] - ETA: 1s - loss: 0.3014 - binary_accuracy: 0.8833"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 78/782 [=>............................] - ETA: 1s - loss: 0.3030 - binary_accuracy: 0.8774"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"103/782 [==>...........................] - ETA: 1s - loss: 0.2994 - binary_accuracy: 0.8792"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"129/782 [===>..........................] - ETA: 1s - loss: 0.3013 - binary_accuracy: 0.8748"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"155/782 [====>.........................] - ETA: 1s - loss: 0.3048 - binary_accuracy: 0.8744"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"181/782 [=====>........................] - ETA: 1s - loss: 0.3074 - binary_accuracy: 0.8724"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"206/782 [======>.......................] - ETA: 1s - loss: 0.3087 - binary_accuracy: 0.8724"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"231/782 [=======>......................] - ETA: 1s - loss: 0.3132 - binary_accuracy: 0.8712"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"256/782 [========>.....................] - ETA: 1s - loss: 0.3113 - binary_accuracy: 0.8735"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"281/782 [=========>....................] - ETA: 1s - loss: 0.3088 - binary_accuracy: 0.8737"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"306/782 [==========>...................] - ETA: 0s - loss: 0.3062 - binary_accuracy: 0.8743"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"332/782 [===========>..................] - ETA: 0s - loss: 0.3076 - binary_accuracy: 0.8734"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"357/782 [============>.................] - ETA: 0s - loss: 0.3080 - binary_accuracy: 0.8724"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"382/782 [=============>................] - ETA: 0s - loss: 0.3089 - binary_accuracy: 0.8724"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"403/782 [==============>...............] - ETA: 0s - loss: 0.3082 - binary_accuracy: 0.8728"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"429/782 [===============>..............] - ETA: 0s - loss: 0.3077 - binary_accuracy: 0.8735"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"455/782 [================>.............] - ETA: 0s - loss: 0.3072 - binary_accuracy: 0.8742"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"481/782 [=================>............] - ETA: 0s - loss: 0.3064 - binary_accuracy: 0.8744"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"507/782 [==================>...........] - ETA: 0s - loss: 0.3080 - binary_accuracy: 0.8736"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"533/782 [===================>..........] - ETA: 0s - loss: 0.3082 - binary_accuracy: 0.8737"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"559/782 [====================>.........] - ETA: 0s - loss: 0.3081 - binary_accuracy: 0.8734"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"584/782 [=====================>........] - ETA: 0s - loss: 0.3106 - binary_accuracy: 0.8719"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"610/782 [======================>.......] - ETA: 0s - loss: 0.3104 - binary_accuracy: 0.8722"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"635/782 [=======================>......] - ETA: 0s - loss: 0.3103 - binary_accuracy: 0.8719"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"660/782 [========================>.....] - ETA: 0s - loss: 0.3105 - binary_accuracy: 0.8715"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"685/782 [=========================>....] - ETA: 0s - loss: 0.3098 - binary_accuracy: 0.8721"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"711/782 [==========================>...] - ETA: 0s - loss: 0.3099 - binary_accuracy: 0.8720"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"736/782 [===========================>..] - ETA: 0s - loss: 0.3096 - binary_accuracy: 0.8723"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"761/782 [============================>.] - ETA: 0s - loss: 0.3106 - binary_accuracy: 0.8723"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"782/782 [==============================] - 2s 2ms/step - loss: 0.3107 - binary_accuracy: 0.8730\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loss: 0.31068193912506104\n",
"Accuracy: 0.8730400204658508\n"
]
}
],
"source": [
"loss, accuracy = model.evaluate(test_ds)\n",
"\n",
"print(\"Loss: \", loss)\n",
"print(\"Accuracy: \", accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z1iEXVTR0Z2t"
},
"source": [
"이 상당히 단순한 접근 방식은 약 86%의 정확도를 달성합니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ldbQqCw2Xc1W"
},
"source": [
"### 정확도와 손실 그래프 그리기\n",
"\n",
"`model.fit()`은 훈련 중에 발생한 모든 것을 가진 사전을 포함하는 `History` 객체를 반환합니다."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:20:50.806531Z",
"iopub.status.busy": "2022-12-14T20:20:50.805841Z",
"iopub.status.idle": "2022-12-14T20:20:50.810915Z",
"shell.execute_reply": "2022-12-14T20:20:50.810147Z"
},
"id": "-YcvZsdvWfDf"
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['loss', 'binary_accuracy', 'val_loss', 'val_binary_accuracy'])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history_dict = history.history\n",
"history_dict.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1_CH32qJXruI"
},
"source": [
"네 개의 항목이 있습니다. 훈련과 검증 단계에서 모니터링하는 지표들입니다. 훈련 손실과 검증 손실을 그래프로 그려 보고, 훈련 정확도와 검증 정확도도 그래프로 그려서 비교해 보겠습니다:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:20:50.814544Z",
"iopub.status.busy": "2022-12-14T20:20:50.813963Z",
"iopub.status.idle": "2022-12-14T20:20:50.964782Z",
"shell.execute_reply": "2022-12-14T20:20:50.964092Z"
},
"id": "2SEMeQ5YXs8z"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"acc = history_dict['binary_accuracy']\n",
"val_acc = history_dict['val_binary_accuracy']\n",
"loss = history_dict['loss']\n",
"val_loss = history_dict['val_loss']\n",
"\n",
"epochs = range(1, len(acc) + 1)\n",
"\n",
"# \"bo\" is for \"blue dot\"\n",
"plt.plot(epochs, loss, 'bo', label='Training loss')\n",
"# b is for \"solid blue line\"\n",
"plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
"plt.title('Training and validation loss')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:20:50.968546Z",
"iopub.status.busy": "2022-12-14T20:20:50.967932Z",
"iopub.status.idle": "2022-12-14T20:20:51.106303Z",
"shell.execute_reply": "2022-12-14T20:20:51.105355Z"
},
"id": "Z3PJemLPXwz_"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(epochs, acc, 'bo', label='Training acc')\n",
"plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
"plt.title('Training and validation accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(loc='lower right')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hFFyCuJoXy7r"
},
"source": [
"이 그래프에서 점선은 훈련 손실과 훈련 정확도를 나타냅니다. 실선은 검증 손실과 검증 정확도입니다.\n",
"\n",
"훈련 손실은 각 epoch마다 *감소*하고 훈련 정확성은 각 epoch마다 *증가*합니다. 경사 하강 최적화를 사용할 때 이와 같이 예상됩니다. 모든 반복에서 원하는 수량을 최소화해야 합니다.\n",
"\n",
"하지만 검증 손실과 검증 정확도에서는 그렇지 못합니다. 훈련 정확도 이전이 피크인 것 같습니다. 이는 과대적합 때문입니다. 이전에 본 적 없는 데이터보다 훈련 데이터에서 모델이 더 잘 동작합니다. 이 지점부터는 모델이 과도하게 최적화되어 테스트 데이터에서 *일반화*되지 않는 훈련 데이터의 *특정* 표현을 학습합니다.\n",
"\n",
"여기에서는 과대적합을 막기 위해 단순히 검증 정확도가 더 이상 증가하지 않는 경우에 훈련을 중단할 수 있습니다. 이를 수행하는 한 가지 방법은 `tf.keras.callbacks.EarlyStopping` 콜백을 사용하는 것입니다."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-to23J3Vy5d3"
},
"source": [
"## 모델 내보내기\n",
"\n",
"위의 코드에서는 모델에 텍스트를 제공하기 전에 `TextVectorization` 레이어를 데이터세트에 적용했습니다. 모델이 원시 문자열을 처리할 수 있도록 하려면(예: 배포를 단순화하기 위해) 모델 내부에 `TextVectorization` 레이어를 포함할 수 있습니다. 이를 위해 방금 훈련한 가중치를 사용하여 새 모델을 만들 수 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:20:51.110444Z",
"iopub.status.busy": "2022-12-14T20:20:51.109782Z",
"iopub.status.idle": "2022-12-14T20:20:54.259636Z",
"shell.execute_reply": "2022-12-14T20:20:54.258851Z"
},
"id": "FWXsMvryuZuq"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/782 [..............................] - ETA: 3:12 - loss: 0.1965 - 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",
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"184/782 [======>.......................] - ETA: 2s - loss: 0.3096 - accuracy: 0.8725"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"495/782 [=================>............] - ETA: 1s - loss: 0.3077 - accuracy: 0.8734"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"509/782 [==================>...........] - ETA: 0s - loss: 0.3084 - accuracy: 0.8732"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"523/782 [===================>..........] - ETA: 0s - loss: 0.3082 - accuracy: 0.8734"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"537/782 [===================>..........] - ETA: 0s - loss: 0.3083 - accuracy: 0.8735"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"551/782 [====================>.........] - ETA: 0s - loss: 0.3078 - accuracy: 0.8738"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"565/782 [====================>.........] - ETA: 0s - loss: 0.3081 - accuracy: 0.8736"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"579/782 [=====================>........] - ETA: 0s - loss: 0.3090 - accuracy: 0.8726"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"593/782 [=====================>........] - ETA: 0s - loss: 0.3100 - accuracy: 0.8718"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"607/782 [======================>.......] - ETA: 0s - loss: 0.3106 - accuracy: 0.8720"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"621/782 [======================>.......] - ETA: 0s - loss: 0.3102 - accuracy: 0.8720"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"636/782 [=======================>......] - ETA: 0s - loss: 0.3104 - accuracy: 0.8719"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"650/782 [=======================>......] - ETA: 0s - loss: 0.3105 - accuracy: 0.8715"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"665/782 [========================>.....] - ETA: 0s - loss: 0.3099 - accuracy: 0.8719"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"679/782 [=========================>....] - ETA: 0s - loss: 0.3100 - accuracy: 0.8719"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"693/782 [=========================>....] - ETA: 0s - loss: 0.3099 - accuracy: 0.8723"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"708/782 [==========================>...] - ETA: 0s - loss: 0.3097 - accuracy: 0.8722"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"722/782 [==========================>...] - ETA: 0s - loss: 0.3093 - accuracy: 0.8723"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"737/782 [===========================>..] - ETA: 0s - loss: 0.3099 - accuracy: 0.8723"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"751/782 [===========================>..] - ETA: 0s - loss: 0.3103 - accuracy: 0.8722"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"766/782 [============================>.] - ETA: 0s - loss: 0.3104 - accuracy: 0.8727"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"781/782 [============================>.] - ETA: 0s - loss: 0.3107 - accuracy: 0.8730"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"782/782 [==============================] - 3s 4ms/step - loss: 0.3107 - accuracy: 0.8730\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8730400204658508\n"
]
}
],
"source": [
"export_model = tf.keras.Sequential([\n",
" vectorize_layer,\n",
" model,\n",
" layers.Activation('sigmoid')\n",
"])\n",
"\n",
"export_model.compile(\n",
" loss=losses.BinaryCrossentropy(from_logits=False), optimizer=\"adam\", metrics=['accuracy']\n",
")\n",
"\n",
"# Test it with `raw_test_ds`, which yields raw strings\n",
"loss, accuracy = export_model.evaluate(raw_test_ds)\n",
"print(accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TwQgoN88LoEF"
},
"source": [
"### 새로운 데이터로 추론하기\n",
"\n",
"새로운 예에 대한 예측을 얻으려면 간단히 `model.predict()`를 호출하면 됩니다."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"execution": {
"iopub.execute_input": "2022-12-14T20:20:54.263501Z",
"iopub.status.busy": "2022-12-14T20:20:54.262920Z",
"iopub.status.idle": "2022-12-14T20:20:54.450233Z",
"shell.execute_reply": "2022-12-14T20:20:54.449500Z"
},
"id": "QW355HH5L49K"
},
"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 137ms/step\n"
]
},
{
"data": {
"text/plain": [
"array([[0.6096767],\n",
" [0.4326959],\n",
" [0.3506354]], dtype=float32)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"examples = [\n",
" \"The movie was great!\",\n",
" \"The movie was okay.\",\n",
" \"The movie was terrible...\"\n",
"]\n",
"\n",
"export_model.predict(examples)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MaxlpFWpzR6c"
},
"source": [
"모델 내부에 텍스트 전처리 논리를 포함하면 배포를 단순화하고 [훈련/테스트 왜곡](https://developers.google.com/machine-learning/guides/rules-of-ml#training-serving_skew) 가능성을 줄이는 프로덕션용 모델을 내보낼 수 있습니다.\n",
"\n",
"TextVectorization 레이어를 적용할 위치를 선택할 때 염두에 두어야 할 성능 차이가 있습니다. 레이어를 모델 외부에서 사용하면 GPU에서 훈련할 때 비동기 CPU 처리 및 데이터 버퍼링을 수행할 수 있습니다. 따라서 GPU에서 모델을 훈련하는 경우 모델을 개발하는 동안 최상의 성능을 얻기 위해 이 옵션을 사용하고 배포 준비가 완료되면 모델 내부에 TextVectorization 레이어를 포함하도록 전환할 수 있습니다.\n",
"\n",
"모델 저장에 대해 자세히 알아보려면 이 [튜토리얼](https://www.tensorflow.org/tutorials/keras/save_and_load)을 방문하세요."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eSSuci_6nCEG"
},
"source": [
"## 연습: 스택 오버플로 질문에 대한 다중 클래스 분류\n",
"\n",
"이 튜토리얼은 IMDB 데이터세트에서 이진 분류자를 처음부터 훈련하는 방법을 보여주었습니다. 연습으로, 이 노트북을 수정하여 [스택 오버플로](http://stackoverflow.com/)에서 프로그래밍 질문의 태그를 예측하도록 다중 클래스 분류자를 훈련할 수 있습니다.\n",
"\n",
"스택 오버플로에 게시된 수천 개의 프로그래밍 질문(예: \"Python에서 값을 기준으로 사전을 정렬할 수 있는 방법은?\")의 본문이 포함된 [데이터세트](https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz)가 준비되어 있습니다. 이들 각각은 정확히 하나의 태그(Python, CSharp, JavaScript 또는 Java)로 레이블이 지정됩니다. 여러분이 할 작업은 질문을 입력으로 받아 적절한 태그(이 경우 Python)를 예측하는 것입니다.\n",
"\n",
"작업할 데이터세트에는 1,700만 개 이상의 게시물이 포함된 [BigQuery](https://console.cloud.google.com/marketplace/details/stack-exchange/stack-overflow)의 훨씬 더 큰 공개 스택 오버플로 데이터세트에서 추출한 수천 개의 질문이 포함되어 있습니다.\n",
"\n",
"데이터세트를 다운로드해 보면 이전에 작업한 IMDB 데이터세트와 유사한 디렉터리 구조를 가지고 있음을 알 수 있습니다.\n",
"\n",
"```\n",
"train/\n",
"...python/\n",
"......0.txt\n",
"......1.txt\n",
"...javascript/\n",
"......0.txt\n",
"......1.txt\n",
"...csharp/\n",
"......0.txt\n",
"......1.txt\n",
"...java/\n",
"......0.txt\n",
"......1.txt\n",
"```\n",
"\n",
"참고: 분류 문제의 난이도를 높이기 위해 프로그래밍 질문에서 Python, CSharp, JavaScript 또는 Java라는 단어의 출현은 *blank*라는 단어로 대체되었습니다(많은 질문에 해당 언어가 포함됨).\n",
"\n",
"이 연습을 완료하려면 다음과 같이 수정하여 스택 오버플로 데이터세트와 함께 작동하도록 이 노트북을 수정해야 합니다.\n",
"\n",
"1. 노트북 상단에서, 미리 준비된 [스택 오버플로 데이터세트](https://storage.googleapis.com/download.tensorflow.org/data/stack_overflow_16k.tar.gz)를 다운로드하는 코드로 IMDB 데이터세트를 다운로드하는 코드를 업데이트합니다. 스택 오버플로 데이터세트는 유사한 디렉터리 구조를 가지므로 많이 수정할 필요가 없습니다.\n",
"\n",
"2. 이제 4개의 출력 클래스가 있으므로 `Dense(4)`를 읽도록 모델의 마지막 레이어를 수정합니다.\n",
"\n",
"3. 모델을 컴파일할 때 손실을 `tf.keras.losses.SparseCategoricalCrossentropy`로 변경합니다. 이것은 각 클래스의 레이블이 정수일 때(이 경우 0, *1*, *2* 또는 *3*일 수 있음) 다중 클래스 분류 문제에 사용할 올바른 손실 함수입니다. 또한 이것은 다중 클래스 분류 문제이기 때문에 메트릭을 `metrics=['accuracy']`로 변경합니다(`tf.metrics.BinaryAccuracy`는 이진 분류자에만 사용됨).\n",
"\n",
"4. 시간 경과에 따른 정확도를 표시할 때 `binary_accuracy` 및 `val_binary_accuracy`를 각각 `accuracy` 및 `val_accuracy`로 변경합니다.\n",
"\n",
"5. 이러한 변경이 완료되면 다중 클래스 분류자를 훈련할 수 있습니다. "
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"## 더 알아보기\n",
"\n",
"이 튜토리얼은 텍스트 분류를 처음부터 알아보았습니다. 일반적인 텍스트 분류 워크플로에 대해 자세히 알아보려면 Google Developers의 [텍스트 분류 가이드](https://developers.google.com/machine-learning/guides/text-classification/)를 확인하세요.\n"
]
}
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"name": "text_classification.ipynb",
"toc_visible": true
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TensorFlow.org에서 보기 ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Google Colab에서 실행![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo}
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