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Text query to image - Integrated project 4
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Enhanced-Conclusions.md

Выводы и рекомендации Mazaltov at Alef Invest:

  1. В рамках данного проекта была проведена работа по обучению моделей для предсказания соответствия между текстовыми запросами и изображениями.

  2. Исходные данные включали:

    • Набор из 1000 уникальных изображений
    • Датасет с наименованиями файлов изображений
    • Датасет с экспертной оценкой соответствия изображений и запросов (около 15% выборки)
    • Датасет с оценкой соответствия, полученной краудсорсингом (около 95% выборки)

  3. Для обучения были выбраны две модели: линейная регрессия и полносвязная нейронная сеть с 4 слоями.

  4. Оптимальная конфигурация нейронной сети была определена путем перебора количества нейронов на каждом из первых трех слоев. Лучшие результаты показала модель с архитектурой:

    • 150 нейронов на первом слое
    • 50 нейронов на втором слое
    • 5 нейронов на третьем слое
    • 1 нейрон на выходном слое

  5. Обе модели показали схожие значения метрики MSE, что указывает на сопоставимую производительность.

  6. Для тестирования была разработана функция, принимающая текстовое описание и возвращающая наиболее подходящее изображение согласно метрике.

  7. Результаты тестирования показали, что обе модели демонстрируют неудовлетворительную производительность в предсказании изображений по текстовым запросам. Линейная регрессия выдает одно и то же изображение на все запросы, а нейронная сеть предлагает изображения, не соответствующие описанию.

  8. Возможные причины низкой производительности моделей:

    • Некачественная разметка исходных данных
    • Сильное смещение баланса оценок в сторону нуля
    • Неоптимальный выбор архитектуры модели или метрики оценки

  9. Рекомендации для улучшения результатов:

    а) Улучшение качества данных:

    • Провести аудит и валидацию существующей разметки
    • Дополнить датасет качественно размеченными примерами с высоким соответствием между изображениями и текстовыми описаниями
    • Рассмотреть возможность привлечения экспертов для дополнительной проверки и уточнения разметки

    б) Предобработка и анализ данных:

    • Провести более глубокий анализ распределения оценок соответствия
    • Применить методы балансировки данных, если обнаружится сильный перекос
    • Исследовать и применить более продвинутые методы векторизации текста и изображений

    в) Совершенствование моделей:

    • Экспериментировать с другими архитектурами нейронных сетей, например, CNN для обработки изображений и LSTM или Transformer для обработки текста
    • Рассмотреть применение предобученных моделей и transfer learning (например, BERT для текста и ResNet для изображений)
    • Исследовать возможность использования многозадачного обучения или ансамблевых методов

    г) Оптимизация процесса обучения:

    • Экспериментировать с различными функциями потерь и метриками оценки
    • Применить методы регуляризации для предотвращения переобучения
    • Использовать техники, такие как learning rate scheduling и early stopping

    д) Расширение контекста:

    • Рассмотреть возможность включения дополнительной информации о контексте изображений и запросов
    • Исследовать методы учета семантической близости между запросами

    е) Итеративное улучшение:

    • Внедрить процесс постоянной оценки и улучшения модели на основе обратной связи от пользователей
    • Регулярно обновлять модель новыми данными и проводить повторное обучение

  10. Заключение: Несмотря на текущие ограничения, данный проект заложил основу для создания системы сопоставления текстовых запросов и изображений. Реализация предложенных рекомендаций должна значительно улучшить качество предсказаний и приблизить систему к практическому применению.

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Text_query_to_image.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "e1f51763",
"metadata": {},
"source": [
"# Сборный проект-4\n",
"\n",
"Нам поручено разработать демонстрационную версию поиска изображений по запросу.\n",
"\n",
"Для демонстрационной версии нужно обучить модель, которая получит векторное представление изображения, векторное представление текста, а на выходе выдаст число от 0 до 1 — покажет, насколько текст и картинка подходят друг другу.\n",
"\n",
"## Описание данных\n",
"\n",
"Данные доступны по [ссылке](https://code.s3.yandex.net/datasets/dsplus_integrated_project_4.zip).\n",
"\n",
"В файле `train_dataset.csv` находится информация, необходимая для обучения: имя файла изображения, идентификатор описания и текст описания. Для одной картинки может быть доступно до 5 описаний. Идентификатор описания имеет формат `<имя файла изображения>#<порядковый номер описания>`.\n",
"\n",
"В папке `train_images` содержатся изображения для тренировки модели.\n",
"\n",
"В файле `CrowdAnnotations.tsv` — данные по соответствию изображения и описания, полученные с помощью краудсорсинга. Номера колонок и соответствующий тип данных:\n",
"\n",
"1. Имя файла изображения.\n",
"2. Идентификатор описания.\n",
"3. Доля людей, подтвердивших, что описание соответствует изображению.\n",
"4. Количество человек, подтвердивших, что описание соответствует изображению.\n",
"5. Количество человек, подтвердивших, что описание не соответствует изображению.\n",
"\n",
"В файле `ExpertAnnotations.tsv` содержатся данные по соответствию изображения и описания, полученные в результате опроса экспертов. Номера колонок и соответствующий тип данных:\n",
"\n",
"1. Имя файла изображения.\n",
"2. Идентификатор описания.\n",
"\n",
"3, 4, 5 — оценки трёх экспертов.\n",
"\n",
"Эксперты ставят оценки по шкале от 1 до 4, где 1 — изображение и запрос совершенно не соответствуют друг другу, 2 — запрос содержит элементы описания изображения, но в целом запрос тексту не соответствует, 3 — запрос и текст соответствуют с точностью до некоторых деталей, 4 — запрос и текст соответствуют полностью.\n",
"\n",
"В файле `test_queries.csv` находится информация, необходимая для тестирования: идентификатор запроса, текст запроса и релевантное изображение. Для одной картинки может быть доступно до 5 описаний. Идентификатор описания имеет формат `<имя файла изображения>#<порядковый номер описания>`.\n",
"\n",
"В папке `test_images` содержатся изображения для тестирования модели."
]
},
{
"cell_type": "markdown",
"id": "f6e20718",
"metadata": {},
"source": [
"## Знакомство с данными. Предобработка данных."
]
},
{
"cell_type": "markdown",
"id": "13d44002",
"metadata": {},
"source": [
"**Импортируем необходимые библиотеки.**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "06e03d65",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n",
" warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import torch.nn as nn\n",
"import torchvision.models as models\n",
"import spacy\n",
"from torchvision import transforms\n",
"from PIL import Image\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.model_selection import GroupShuffleSplit\n",
"from tensorflow import keras\n",
"from tensorflow.keras.layers import Dense \n",
"from tensorflow.keras.models import Sequential\n",
"from random import randrange"
]
},
{
"cell_type": "markdown",
"id": "2f4a5b1c",
"metadata": {},
"source": [
"**Загразим таблицу с информацией для обучения.**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ff871b0f",
"metadata": {},
"outputs": [],
"source": [
"path = './to_upload/'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4a106e03",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(path+'train_dataset.csv')\n",
"df.columns=['file_name', 'query_id', 'query_text']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bcf228bc",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>query_id</th>\n",
" <th>query_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1056338697_4f7d7ce270.jpg</td>\n",
" <td>2549968784_39bfbe44f9.jpg#2</td>\n",
" <td>A young child is wearing blue goggles and sitt...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1262583859_653f1469a9.jpg</td>\n",
" <td>2549968784_39bfbe44f9.jpg#2</td>\n",
" <td>A young child is wearing blue goggles and sitt...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2447284966_d6bbdb4b6e.jpg</td>\n",
" <td>2549968784_39bfbe44f9.jpg#2</td>\n",
" <td>A young child is wearing blue goggles and sitt...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2549968784_39bfbe44f9.jpg</td>\n",
" <td>2549968784_39bfbe44f9.jpg#2</td>\n",
" <td>A young child is wearing blue goggles and sitt...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2621415349_ef1a7e73be.jpg</td>\n",
" <td>2549968784_39bfbe44f9.jpg#2</td>\n",
" <td>A young child is wearing blue goggles and sitt...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name query_id \\\n",
"0 1056338697_4f7d7ce270.jpg 2549968784_39bfbe44f9.jpg#2 \n",
"1 1262583859_653f1469a9.jpg 2549968784_39bfbe44f9.jpg#2 \n",
"2 2447284966_d6bbdb4b6e.jpg 2549968784_39bfbe44f9.jpg#2 \n",
"3 2549968784_39bfbe44f9.jpg 2549968784_39bfbe44f9.jpg#2 \n",
"4 2621415349_ef1a7e73be.jpg 2549968784_39bfbe44f9.jpg#2 \n",
"\n",
" query_text \n",
"0 A young child is wearing blue goggles and sitt... \n",
"1 A young child is wearing blue goggles and sitt... \n",
"2 A young child is wearing blue goggles and sitt... \n",
"3 A young child is wearing blue goggles and sitt... \n",
"4 A young child is wearing blue goggles and sitt... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2276f0af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5822 entries, 0 to 5821\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 file_name 5822 non-null object\n",
" 1 query_id 5822 non-null object\n",
" 2 query_text 5822 non-null object\n",
"dtypes: object(3)\n",
"memory usage: 136.6+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "58d35a18",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.duplicated().sum()"
]
},
{
"cell_type": "markdown",
"id": "e75bcbbd",
"metadata": {},
"source": [
"В таблице 5822 строки. Пропусков и дубликатов нет. Проанализируем данные."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "69cfc4d7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"В датасете 1000 уникальных имен файлов.\n"
]
}
],
"source": [
"print('В датасете', len(df['file_name'].unique()), 'уникальных имен файлов.')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a54a2536",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"В датасете 977 уникальных текстов запросов.\n"
]
}
],
"source": [
"print('В датасете', len(df['query_id'].unique()), 'уникальных текстов запросов.')"
]
},
{
"cell_type": "markdown",
"id": "7b0e5ece",
"metadata": {},
"source": [
"Сохраним в переменную *images_df* уникальные имена файлов изображений, а в переменную *queries_df* - уникальные id и тексты запросов."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "71188090",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"images_df = pd.DataFrame(df['file_name']).drop_duplicates('file_name').reset_index(drop=True)\n",
"len(images_df['file_name'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5c0f9a4e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"977"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"queries_df = df[['query_id', 'query_text']].drop_duplicates().reset_index(drop=True)\n",
"len(queries_df)"
]
},
{
"cell_type": "markdown",
"id": "7f0e1200",
"metadata": {},
"source": [
"Переведем слова в запросах в леммы."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ce6c0e19",
"metadata": {},
"outputs": [],
"source": [
"nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c065fa69",
"metadata": {},
"outputs": [],
"source": [
"def lemmatize_text(df):\n",
" df['lem_query_text'] = df['query_text'].apply(lambda text: \" \".join([token.lemma_ for token in nlp(text)])) \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9a2f0914",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"queries_df = lemmatize_text(queries_df)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4f1447e8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>query_id</th>\n",
" <th>query_text</th>\n",
" <th>lem_query_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2549968784_39bfbe44f9.jpg#2</td>\n",
" <td>A young child is wearing blue goggles and sitt...</td>\n",
" <td>a young child be wear blue goggle and sit in a...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2718495608_d8533e3ac5.jpg#2</td>\n",
" <td>A girl wearing a yellow shirt and sunglasses s...</td>\n",
" <td>a girl wear a yellow shirt and sunglass smile .</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3181701312_70a379ab6e.jpg#2</td>\n",
" <td>A man sleeps under a blanket on a city street .</td>\n",
" <td>a man sleep under a blanket on a city street .</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3207358897_bfa61fa3c6.jpg#2</td>\n",
" <td>A woman plays with long red ribbons in an empt...</td>\n",
" <td>a woman play with long red ribbon in an empty ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3286822339_5535af6b93.jpg#2</td>\n",
" <td>Chinese market street in the winter time .</td>\n",
" <td>chinese market street in the winter time .</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" query_id \\\n",
"0 2549968784_39bfbe44f9.jpg#2 \n",
"1 2718495608_d8533e3ac5.jpg#2 \n",
"2 3181701312_70a379ab6e.jpg#2 \n",
"3 3207358897_bfa61fa3c6.jpg#2 \n",
"4 3286822339_5535af6b93.jpg#2 \n",
"\n",
" query_text \\\n",
"0 A young child is wearing blue goggles and sitt... \n",
"1 A girl wearing a yellow shirt and sunglasses s... \n",
"2 A man sleeps under a blanket on a city street . \n",
"3 A woman plays with long red ribbons in an empt... \n",
"4 Chinese market street in the winter time . \n",
"\n",
" lem_query_text \n",
"0 a young child be wear blue goggle and sit in a... \n",
"1 a girl wear a yellow shirt and sunglass smile . \n",
"2 a man sleep under a blanket on a city street . \n",
"3 a woman play with long red ribbon in an empty ... \n",
"4 chinese market street in the winter time . "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"queries_df.head()"
]
},
{
"cell_type": "markdown",
"id": "9860a8b5",
"metadata": {},
"source": [
"**Загрузим и обработаем данные, полученные с помощью краудсорсинга**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bc1e832a",
"metadata": {},
"outputs": [],
"source": [
"train_crowd_target = pd.read_table(path+'CrowdAnnotations.tsv',\n",
" names=['file_name', \n",
" 'query_id', \n",
" 'target_crowd', \n",
" 'num_votes_up', \n",
" 'num_votes_down'])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "92287250",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"text/plain": [
" file_name query_id target_crowd \\\n",
"0 1056338697_4f7d7ce270.jpg 1056338697_4f7d7ce270.jpg#2 1.0 \n",
"1 1056338697_4f7d7ce270.jpg 114051287_dd85625a04.jpg#2 0.0 \n",
"2 1056338697_4f7d7ce270.jpg 1427391496_ea512cbe7f.jpg#2 0.0 \n",
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"4 1056338697_4f7d7ce270.jpg 2083434441_a93bc6306b.jpg#2 0.0 \n",
"\n",
" num_votes_up num_votes_down \n",
"0 3 0 \n",
"1 0 3 \n",
"2 0 3 \n",
"3 0 3 \n",
"4 0 3 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_crowd_target.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "b192ff26",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 47830 entries, 0 to 47829\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 file_name 47830 non-null object \n",
" 1 query_id 47830 non-null object \n",
" 2 target_crowd 47830 non-null float64\n",
" 3 num_votes_up 47830 non-null int64 \n",
" 4 num_votes_down 47830 non-null int64 \n",
"dtypes: float64(1), int64(2), object(2)\n",
"memory usage: 1.8+ MB\n"
]
}
],
"source": [
"train_crowd_target.info()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b4b48714",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_crowd_target.duplicated().sum()"
]
},
{
"cell_type": "markdown",
"id": "f5b4ad0d",
"metadata": {},
"source": [
"В датасете 47830 строк. Пропусков и явных дубликатов нет."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "96097201",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"В таблице 1000 уникальных имен файлов.\n"
]
}
],
"source": [
"print('В таблице', len(train_crowd_target['file_name'].unique()), 'уникальных имен файлов.')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3e115b24",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"В таблице 1000 уникальных текстов запросов.\n"
]
}
],
"source": [
"print('В таблице', len(train_crowd_target['query_id'].unique()), 'уникальных текстов запросов.')"
]
},
{
"cell_type": "markdown",
"id": "0a3be46d",
"metadata": {},
"source": [
"Округлим значения оценки до 4-го знака после запятой."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "41dd45f9",
"metadata": {},
"outputs": [],
"source": [
"train_crowd_target['target_crowd'] = train_crowd_target['target_crowd'].round(4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a62a4f3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "cbb8fa46",
"metadata": {},
"source": [
"**Загрузим данные с разметкой, полученной с помощью экспертной оценки.**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f2bbaab0",
"metadata": {},
"outputs": [],
"source": [
"train_expert_target = pd.read_table(path+'ExpertAnnotations.tsv',\n",
" names=['file_name', \n",
" 'query_id', \n",
" 'eval_1', \n",
" 'eval_2', \n",
" 'eval_3'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "3701d915",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"text/plain": [
" file_name query_id eval_1 eval_2 \\\n",
"0 1056338697_4f7d7ce270.jpg 2549968784_39bfbe44f9.jpg#2 1 1 \n",
"1 1056338697_4f7d7ce270.jpg 2718495608_d8533e3ac5.jpg#2 1 1 \n",
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"4 1056338697_4f7d7ce270.jpg 3286822339_5535af6b93.jpg#2 1 1 \n",
"\n",
" eval_3 \n",
"0 1 \n",
"1 2 \n",
"2 2 \n",
"3 2 \n",
"4 2 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_expert_target.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "5eb60207",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5822 entries, 0 to 5821\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 file_name 5822 non-null object\n",
" 1 query_id 5822 non-null object\n",
" 2 eval_1 5822 non-null int64 \n",
" 3 eval_2 5822 non-null int64 \n",
" 4 eval_3 5822 non-null int64 \n",
"dtypes: int64(3), object(2)\n",
"memory usage: 227.5+ KB\n"
]
}
],
"source": [
"train_expert_target.info()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "4ca40462",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <thead>\n",
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" <th>eval_1</th>\n",
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" <tr>\n",
" <th>mean</th>\n",
" <td>1.436620</td>\n",
" <td>1.624356</td>\n",
" <td>1.881999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.787084</td>\n",
" <td>0.856222</td>\n",
" <td>0.904087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
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" <tr>\n",
" <th>75%</th>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
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" </tbody>\n",
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],
"text/plain": [
" eval_1 eval_2 eval_3\n",
"count 5822.000000 5822.000000 5822.000000\n",
"mean 1.436620 1.624356 1.881999\n",
"std 0.787084 0.856222 0.904087\n",
"min 1.000000 1.000000 1.000000\n",
"25% 1.000000 1.000000 1.000000\n",
"50% 1.000000 1.000000 2.000000\n",
"75% 2.000000 2.000000 2.000000\n",
"max 4.000000 4.000000 4.000000"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_expert_target.describe()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "120fefac",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"В таблице 1000 уникальных имен файлов.\n"
]
}
],
"source": [
"print('В таблице', len(train_expert_target['file_name'].unique()), 'уникальных имен файлов.')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "841657a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"В таблице 977 уникальных текстов запросов.\n"
]
}
],
"source": [
"print('В таблице', len(train_expert_target['query_id'].unique()), 'уникальных текстов запросов.')"
]
},
{
"cell_type": "markdown",
"id": "18bb424c",
"metadata": {},
"source": [
"Удалим из датасета строки, в которых у всех трех экспертов разделились мнения."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "03755749",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"126"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_expert_target.loc[(train_expert_target['eval_1'] != train_expert_target['eval_2']) &\n",
" (train_expert_target['eval_2'] != train_expert_target['eval_3']) &\n",
" (train_expert_target['eval_3'] != train_expert_target['eval_1']), 'file_name'].count()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c1355f0a",
"metadata": {},
"outputs": [],
"source": [
"train_expert_target = train_expert_target.drop(\n",
" train_expert_target[(train_expert_target['eval_1'] != train_expert_target['eval_2']) &\n",
" (train_expert_target['eval_2'] != train_expert_target['eval_3']) &\n",
" (train_expert_target['eval_3'] != train_expert_target['eval_1'])].index\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b61da8fc",
"metadata": {},
"source": [
"По остальным строкам выберем итоговой оценкой ту, за которую проголосовало большинство экспертов. Также приведем оценки экспертов к нашей единой шкале - от 0 до 1. Для этого из оценки экспертов нужно вычесть единицу и разделить на 3."
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "15a0a3b4",
"metadata": {},
"outputs": [],
"source": [
"def most_votes(df):\n",
" \n",
" df['target_experts'] = 0\n",
" \n",
" df.loc[(df['eval_1'] == df['eval_2']) &\n",
" (df['eval_2'] == df['eval_3']), 'target_experts'] = ((df['eval_1'] - 1) / 3).round(4)\n",
" \n",
" df.loc[df['target_experts'] == 0, 'target_experts'] = ((df['eval_2'] - 1) / 3).round(4)\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "aad66012",
"metadata": {},
"outputs": [],
"source": [
"train_expert_target = most_votes(train_expert_target)"
]
},
{
"cell_type": "markdown",
"id": "53689752",
"metadata": {},
"source": [
"Проверим, что все посчитано корректно."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "660399b0",
"metadata": {
"scrolled": false
},
"outputs": [
{
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" <td>1056338697_4f7d7ce270.jpg</td>\n",
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"text/plain": [
" file_name query_id eval_1 eval_2 \\\n",
"0 1056338697_4f7d7ce270.jpg 2549968784_39bfbe44f9.jpg#2 1 1 \n",
"1 1056338697_4f7d7ce270.jpg 2718495608_d8533e3ac5.jpg#2 1 1 \n",
"2 1056338697_4f7d7ce270.jpg 3181701312_70a379ab6e.jpg#2 1 1 \n",
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"\n",
" eval_3 target_experts \n",
"0 1 0.0000 \n",
"1 2 0.0000 \n",
"2 2 0.0000 \n",
"3 2 0.3333 \n",
"4 2 0.0000 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_expert_target.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abfcfc42",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "3bd33a3e",
"metadata": {},
"source": [
"**Объединим таблицы в один датасет.**\n",
"\n",
"Посчитаем количество пересечений запросов между таблицами с оценками и таблицей с данными."
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "b01976f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(df['file_name'].unique()).intersection(train_crowd_target['file_name'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "5b4b9c14",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1000"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(df['file_name'].unique()).intersection(train_expert_target['file_name'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "0f3a568a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"977"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(df['query_id'].unique()).intersection(train_crowd_target['query_id'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "104c422a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"977"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(df['query_id'].unique()).intersection(train_expert_target['query_id'].unique()))"
]
},
{
"cell_type": "markdown",
"id": "00605fb5",
"metadata": {},
"source": [
"В таблице, полученной краудсорсингом, больше id уникальных запросов, чем в наших данных. Удалим строки с id запросов, которые нам не известны."
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "2ad65b4a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23\n"
]
}
],
"source": [
"diff = []\n",
"for query_id in train_crowd_target['query_id'].unique():\n",
" if query_id not in df['query_id'].unique():\n",
" diff.append(query_id)\n",
"\n",
"print(len(diff))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "d796a2c4",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"47830\n",
"46721\n"
]
}
],
"source": [
"print(len(train_crowd_target))\n",
"for query_id in diff:\n",
" train_crowd_target = train_crowd_target.drop(\n",
" train_crowd_target[train_crowd_target['query_id'] == query_id].index\n",
" )\n",
"print(len(train_crowd_target))"
]
},
{
"cell_type": "markdown",
"id": "dd20ba65",
"metadata": {},
"source": [
"Проверим, что удаление произведено корректно."
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "fcccf81f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"977"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set(df['query_id'].unique()).intersection(train_crowd_target['query_id'].unique()))"
]
},
{
"cell_type": "markdown",
"id": "3df5d93c",
"metadata": {},
"source": [
"**Объединим таблицы с оценками.**"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "47dcb64c",
"metadata": {},
"outputs": [],
"source": [
"train_data = train_crowd_target.merge(train_expert_target,\n",
" how='outer', \n",
" on=['file_name', 'query_id']).drop(['eval_1', \n",
" 'eval_2', \n",
" 'eval_3',\n",
" 'num_votes_up',\n",
" 'num_votes_down'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "8ab7c1fe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50159"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_data)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "375de6f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"file_name 0\n",
"query_id 0\n",
"target_crowd 3438\n",
"target_experts 44463\n",
"dtype: int64"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.isna().sum()"
]
},
{
"cell_type": "markdown",
"id": "95e57078",
"metadata": {},
"source": [
"**Объединим общую таблицу с оценками с таблицей с текстами запросов.**"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "6c571c2d",
"metadata": {},
"outputs": [],
"source": [
"train_data = train_data.merge(queries_df, \n",
" how='inner', \n",
" on=['query_id'])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "ce788ff8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50159"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_data)"
]
},
{
"cell_type": "markdown",
"id": "6fa6c98c",
"metadata": {},
"source": [
"Найдем пропуски."
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "6ea03758",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"file_name 0\n",
"query_id 0\n",
"target_crowd 3438\n",
"target_experts 44463\n",
"query_text 0\n",
"lem_query_text 0\n",
"dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.isna().sum()"
]
},
{
"cell_type": "markdown",
"id": "17f6710d",
"metadata": {},
"source": [
"**Определим итоговые оценки.**"
]
},
{
"cell_type": "markdown",
"id": "1e4dd7ba",
"metadata": {},
"source": [
"В качестве итоговой оценки в строках с пропуском в экспертной оценке возьмем оценку краудсорсинга и наоборот."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "162295b9",
"metadata": {},
"outputs": [],
"source": [
"train_data.loc[train_data['target_crowd'].isna(),'total_target'] = train_data['target_experts']\n",
"train_data.loc[train_data['target_experts'].isna(),'total_target'] = train_data['target_crowd']"
]
},
{
"cell_type": "markdown",
"id": "df1e687c",
"metadata": {},
"source": [
"С строках с наличием обеих оценок в качестве итоговой возьмем среднее значение."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "a1e0f7de",
"metadata": {},
"outputs": [],
"source": [
"train_data.loc[train_data['total_target'].isna(),\n",
" 'total_target'] = train_data[['target_experts', 'target_crowd']].mean(axis=1)"
]
},
{
"cell_type": "markdown",
"id": "eb403531",
"metadata": {},
"source": [
"Проверим, что все значения итоговых оценок заполнены."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "e369b4d7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data['total_target'].isna().sum()"
]
},
{
"cell_type": "markdown",
"id": "bead0dcc",
"metadata": {},
"source": [
"Удалим лишние столбцы."
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "6cc65146",
"metadata": {},
"outputs": [],
"source": [
"train_data = train_data.drop(['target_experts', 'target_crowd'], axis=1)"
]
},
{
"cell_type": "markdown",
"id": "36f57461",
"metadata": {},
"source": [
"Посмотрим на распределение оценок."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "7a9c3d4d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"train_data['total_target'].hist(bins=5)\n",
"plt.title('Распределение оценок соответствия запросов изображениям');"
]
},
{
"cell_type": "markdown",
"id": "63122bce",
"metadata": {},
"source": [
"Количество близких к нулю оценок значительно преобладает над количеством оценок, близких единице."
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "65c518ec",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.055\n"
]
}
],
"source": [
"print((train_data.loc[train_data['total_target'] > 0.5, \n",
" 'total_target'].count() / len(train_data['total_target'])).round(3))"
]
},
{
"cell_type": "markdown",
"id": "74bd2e04",
"metadata": {},
"source": [
"Доля оценок больше 0.5 составляет только 5 % от всей выборки. Скорее всего, будет сложно получить качественную модель с таким дисбалансом в оценках."
]
},
{
"cell_type": "markdown",
"id": "2f067aca",
"metadata": {},
"source": [
"## Проверка данных\n",
"\n",
"В некоторых странах, где работает наша компания, действуют ограничения по обработке изображений: поисковым сервисам и сервисам, предоставляющим возможность поиска, запрещено без разрешения родителей или законных представителей предоставлять любую информацию, в том числе, но не исключительно тексты, изображения, видео и аудио, содержащие описание, изображение или запись голоса детей. Ребёнком считается любой человек, не достигший 16 лет.\n",
"\n",
"В нашем сервисе строго следуют законам стран, в которых работают. Поэтому при попытке посмотреть изображения, запрещённые законодательством, вместо картинок показывается дисклеймер:\n",
"\n",
"> This image is unavailable in your country in compliance with local laws\n",
">\n",
"\n",
"Однако у нас нет возможности воспользоваться данным функционалом. Поэтому необходимо удалить из обучающей выборки все изображения, которые нарушают данный закон."
]
},
{
"cell_type": "markdown",
"id": "dd1bb3b0",
"metadata": {},
"source": [
"**Удалим из обучающей выборки все запросы, в которых присутствует упоминение детей.**"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "f1e856f9",
"metadata": {},
"outputs": [],
"source": [
"stop_words = ['boy', 'girl', 'child', 'teenage', 'teenager', 'kid', 'baby']"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "5e060a2a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14151\n",
"CPU times: total: 3.33 s\n",
"Wall time: 3.32 s\n"
]
}
],
"source": [
"%%time\n",
"stop_words_indices = []\n",
"for i in range(len(train_data)):\n",
" for word in stop_words:\n",
" if (word in train_data.loc[i, 'lem_query_text'].lower()) and (\n",
" i not in stop_words_indices):\n",
" \n",
" stop_words_indices.append(i)\n",
" \n",
"print(len(stop_words_indices))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "8f3996bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50159\n",
"36008\n"
]
}
],
"source": [
"print(len(train_data))\n",
"train_data = train_data.drop(stop_words_indices).reset_index()\n",
"print(len(train_data))"
]
},
{
"cell_type": "markdown",
"id": "54788528",
"metadata": {},
"source": [
"В окончательном наборе данных осталось около 36 тысяч строк.\n",
"\n",
"Разделим выборку на обучающую и валидационную в пропорции 7:3."
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "24261ce6",
"metadata": {},
"outputs": [],
"source": [
"gss = GroupShuffleSplit(n_splits=1, train_size=0.7, random_state=10101)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "2de16965",
"metadata": {},
"outputs": [],
"source": [
"train_indices, valid_indices = next(\n",
" gss.split(X=train_data.drop(['total_target'], axis=1), \n",
" y=train_data['total_target'], groups=train_data['file_name'])\n",
")\n",
"\n",
"X_train, X_valid, y_train, y_valid = (\n",
" train_data.drop('total_target', axis=1).loc[train_indices],\n",
" train_data.drop('total_target', axis=1).loc[valid_indices],\n",
" train_data['total_target'].loc[train_indices],\n",
" train_data['total_target'].loc[valid_indices]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "d6b5fae4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"700\n",
"300\n"
]
}
],
"source": [
"print(len(X_train['file_name'].unique()))\n",
"print(len(X_valid['file_name'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "6adc4ace",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25164\n",
"10844\n"
]
}
],
"source": [
"print(len(X_train['file_name']))\n",
"print(len(X_valid['file_name']))"
]
},
{
"cell_type": "markdown",
"id": "e9fc7fd7",
"metadata": {},
"source": [
"В обучаещей выборке 700 уникальных изображений, в валидационной - 300. Разделение прошло корректно."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7481dbb4",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "1f334a64",
"metadata": {},
"source": [
"## Векторизация изображений\n",
"\n",
"Перейдём к векторизации изображений.\n",
"\n",
"Самый примитивный способ — прочесть изображение и превратить полученную матрицу в вектор. Такой способ нам не подходит: длина векторов может быть сильно разной, так как размеры изображений разные. Поэтому обратимся к свёрточным сетям: они позволяют \"выделить\" главные компоненты изображений. Возьмем архитектуру ResNet-18, предварительно натренированную на датасете ImageNet, и исключить из нее полносвязный слой, который отвечает за конечное предсказание."
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "22ea3ad6",
"metadata": {},
"outputs": [],
"source": [
"# загразим модель\n",
"resnet = models.resnet18(weights='IMAGENET1K_V1')\n",
"\n",
"# заморозим веса\n",
"for param in resnet.parameters():\n",
" param.requires_grad_(False)\n",
"\n",
"# используем все слои, кроме двух последних\n",
"modules = list(resnet.children())[:-1]\n",
"resnet = nn.Sequential(*modules)\n",
"\n",
"# переводим модель в режим предсказания\n",
"resnet.eval();"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "cacb94ae",
"metadata": {},
"outputs": [],
"source": [
"# приведем изображение к нужному формату\n",
"norm = transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
"preprocess = transforms.Compose([\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" norm,\n",
"])"
]
},
{
"cell_type": "markdown",
"id": "bc164fdf",
"metadata": {},
"source": [
"Зададим две функции. \n",
"- Первая функция принимает на вход имя файла изображения и имя папки, где хранится изображение, а на выходе выдает векторное представление данного изображения.\n",
"- Вторая функция принимает датафрейм с именами файлов изображений в столбце *file_name* и имя папки с изображениями, а на выходе выдает тот же датафрейм, но с добаленным столбцом *img_vector*, в котором хранятся векторы изображений, полученные от первой функции."
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "71307e94",
"metadata": {},
"outputs": [],
"source": [
"def img_to_vect(image_name, img_folder_name):\n",
" \n",
" img = Image.open(path + img_folder_name + '/' + image_name).convert('RGB')\n",
" image_tensor = preprocess(img).unsqueeze(0)\n",
" img_vector = resnet(image_tensor).flatten().numpy()\n",
" \n",
" return img_vector"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "95d62bf9",
"metadata": {},
"outputs": [],
"source": [
"def df_img_vectorizer(df, img_folder_name):\n",
" \n",
" df['img_vector'] = df['file_name'].apply(lambda x: img_to_vect(x, img_folder_name))\n",
" \n",
" return df"
]
},
{
"cell_type": "markdown",
"id": "55b969dd",
"metadata": {},
"source": [
"Чтобы сэкономить вычислительные мощности, посчитаем векторы только для уникальных изображений и сохраним их в датафрейм *images_df*, а затем объединим данный датафрейм с нашими обучающей и валидационной таблицей оценок."
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "62a8e27a",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 55.8 s\n",
"Wall time: 55.8 s\n"
]
}
],
"source": [
"%%time\n",
"images_df = df_img_vectorizer(images_df, 'train_images')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "d5160616",
"metadata": {},
"outputs": [
{
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" file_name \\\n",
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},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"images_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "135003be",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"X_train = X_train.merge(images_df, on='file_name', how='left')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "8eade1a9",
"metadata": {},
"outputs": [
{
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" index file_name query_id \\\n",
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]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "d6257a04",
"metadata": {},
"outputs": [],
"source": [
"X_valid = X_valid.merge(images_df, on='file_name', how='left')"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "bb422332",
"metadata": {},
"outputs": [
{
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" <td>a woman be signal be to traffic , as see from ...</td>\n",
" <td>[0.041608997, 1.8561997, 1.1910261, 2.038075, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11</td>\n",
" <td>3256275785_9c3af57576.jpg</td>\n",
" <td>1056338697_4f7d7ce270.jpg#2</td>\n",
" <td>A woman is signaling is to traffic , as seen f...</td>\n",
" <td>a woman be signal be to traffic , as see from ...</td>\n",
" <td>[1.6143728, 0.81100106, 0.2598831, 0.6190957, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>13</td>\n",
" <td>3328646934_5cca4cebce.jpg</td>\n",
" <td>1056338697_4f7d7ce270.jpg#2</td>\n",
" <td>A woman is signaling is to traffic , as seen f...</td>\n",
" <td>a woman be signal be to traffic , as see from ...</td>\n",
" <td>[0.2790242, 0.13148071, 0.08582045, 1.4495662,...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index file_name query_id \\\n",
"0 2 2594042571_2e4666507e.jpg 1056338697_4f7d7ce270.jpg#2 \n",
"1 3 2843695880_eeea6c67db.jpg 1056338697_4f7d7ce270.jpg#2 \n",
"2 7 311146855_0b65fdb169.jpg 1056338697_4f7d7ce270.jpg#2 \n",
"3 11 3256275785_9c3af57576.jpg 1056338697_4f7d7ce270.jpg#2 \n",
"4 13 3328646934_5cca4cebce.jpg 1056338697_4f7d7ce270.jpg#2 \n",
"\n",
" query_text \\\n",
"0 A woman is signaling is to traffic , as seen f... \n",
"1 A woman is signaling is to traffic , as seen f... \n",
"2 A woman is signaling is to traffic , as seen f... \n",
"3 A woman is signaling is to traffic , as seen f... \n",
"4 A woman is signaling is to traffic , as seen f... \n",
"\n",
" lem_query_text \\\n",
"0 a woman be signal be to traffic , as see from ... \n",
"1 a woman be signal be to traffic , as see from ... \n",
"2 a woman be signal be to traffic , as see from ... \n",
"3 a woman be signal be to traffic , as see from ... \n",
"4 a woman be signal be to traffic , as see from ... \n",
"\n",
" img_vector \n",
"0 [0.1596264, 1.5516877, 0.8373307, 0.7252195, 0... \n",
"1 [0.5625148, 0.06339559, 0.105744384, 0.5107134... \n",
"2 [0.041608997, 1.8561997, 1.1910261, 2.038075, ... \n",
"3 [1.6143728, 0.81100106, 0.2598831, 0.6190957, ... \n",
"4 [0.2790242, 0.13148071, 0.08582045, 1.4495662,... "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_valid.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a47b8e99",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "ba6764a4",
"metadata": {},
"source": [
"## Векторизация текстов\n",
"\n",
"Векторизуем тексты с помощью tf-idf. Для этого обучим tf-idf на текстах обучающей выборки. \n",
"\n",
"Также зададим функцию, которая примет на вход датафрейм со столбцом *lem_query_text*, а в качестве результата выдаст массив векторных представлений текстов."
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "506870fd",
"metadata": {},
"outputs": [],
"source": [
"tfidf = TfidfVectorizer()\n",
"tfidf.fit(X_train['lem_query_text'].drop_duplicates());"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "1b87e296",
"metadata": {},
"outputs": [],
"source": [
"def text_to_vect(df): \n",
" text_features_array = tfidf.transform(df['lem_query_text']).toarray() \n",
" return text_features_array"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b78d64d",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "c3fbf411",
"metadata": {},
"source": [
"## Объединение векторов\n",
"\n",
"Объедините векторы изображений и векторы текстов с целевой переменной. \n",
"\n",
"Создадим функцию, принимающую на вход датафрейм со столбцами *lem_query_text* и *img_vector*, и выдающую на выходе массив объединенных векторов изображений и текстов, который можно использовать для обучений моделей."
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "e022289b",
"metadata": {},
"outputs": [],
"source": [
"def make_features(df):\n",
" \n",
" # массив с векторами признаков изображений\n",
" img_array = np.array(df['img_vector'].values.tolist())\n",
" \n",
" # массив с векторами признаков текста\n",
" tfidf_array = text_to_vect(df)\n",
" \n",
" # объединенный массив с полным набором признаков\n",
" features_array = np.concatenate((tfidf_array, img_array), axis=1)\n",
" \n",
" return features_array"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "4b8a14b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25164, 1526)\n"
]
}
],
"source": [
"train_features = make_features(X_train)\n",
"print(train_features.shape)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "2a000139",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10844, 1526)\n"
]
}
],
"source": [
"valid_features = make_features(X_valid)\n",
"print(valid_features.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "77350848",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "94c3d62f",
"metadata": {},
"source": [
"## Обучение модели предсказания соответствия\n",
"\n",
"Обучим две модели: линейную регрессию и полносвязную нейронную сеть. В качестве метрики будем использовать среднеквадратическое отклонение *mean_squared_error*."
]
},
{
"cell_type": "markdown",
"id": "902cef2e",
"metadata": {},
"source": [
"**Обучим модель линейной регрессии.**"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "7e389923",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"lin_reg = LinearRegression()\n",
"lin_reg.fit(train_features, y_train);"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "81bf90c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.05102441738093748\n"
]
}
],
"source": [
"lin_reg_preds = lin_reg.predict(valid_features)\n",
"print(mean_squared_error(y_valid, lin_reg_preds))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afbed133",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e7e0741f",
"metadata": {},
"source": [
"**Обучим полносвязную нейронную сеть с 4 слоями.**"
]
},
{
"cell_type": "markdown",
"id": "4ed65c05",
"metadata": {},
"source": [
"Возьмем полносвязную нейронную сеть с 4 слоями. Переберем количество нейронов на первых трех слоях, чтобы выбрать лучшую модель. Обучение для каждой конфигурации будем проводить в течение 100 эпох."
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "94a308b1",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0516 - mean_squared_error: 0.0516 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0462 - mean_squared_error: 0.0462 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0462 - mean_squared_error: 0.0462 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0428 - mean_squared_error: 0.0428 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0422 - mean_squared_error: 0.0422 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0413 - mean_squared_error: 0.0413 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0413 - mean_squared_error: 0.0413 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0402 - mean_squared_error: 0.0402 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0396 - mean_squared_error: 0.0396 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0393 - mean_squared_error: 0.0393 - val_loss: 0.0504 - val_mean_squared_error: 0.0504\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0376 - mean_squared_error: 0.0376 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0394 - mean_squared_error: 0.0394 - val_loss: 0.0486 - val_mean_squared_error: 0.0486\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0354 - mean_squared_error: 0.0354 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0365 - mean_squared_error: 0.0365 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0353 - mean_squared_error: 0.0353 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0351 - mean_squared_error: 0.0351 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0488 - val_mean_squared_error: 0.0488\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0509 - val_mean_squared_error: 0.0509\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0351 - mean_squared_error: 0.0351 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0341 - mean_squared_error: 0.0341 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0326 - mean_squared_error: 0.0326 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0330 - mean_squared_error: 0.0330 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0332 - mean_squared_error: 0.0332 - val_loss: 0.0510 - val_mean_squared_error: 0.0510\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0496 - val_mean_squared_error: 0.0496\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0315 - mean_squared_error: 0.0315 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0318 - mean_squared_error: 0.0318 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0309 - mean_squared_error: 0.0309 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0288 - mean_squared_error: 0.0288 - val_loss: 0.0502 - val_mean_squared_error: 0.0502\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0298 - mean_squared_error: 0.0298 - val_loss: 0.0508 - val_mean_squared_error: 0.0508\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0298 - mean_squared_error: 0.0298 - val_loss: 0.0495 - val_mean_squared_error: 0.0495\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0299 - mean_squared_error: 0.0299 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0293 - mean_squared_error: 0.0293 - val_loss: 0.0501 - val_mean_squared_error: 0.0501\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0297 - mean_squared_error: 0.0297 - val_loss: 0.0492 - val_mean_squared_error: 0.0492\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0285 - mean_squared_error: 0.0285 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0281 - mean_squared_error: 0.0281 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0287 - mean_squared_error: 0.0287 - val_loss: 0.0517 - val_mean_squared_error: 0.0517\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0494 - val_mean_squared_error: 0.0494\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0285 - mean_squared_error: 0.0285 - val_loss: 0.0497 - val_mean_squared_error: 0.0497\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0259 - mean_squared_error: 0.0259 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0263 - mean_squared_error: 0.0263 - val_loss: 0.0498 - val_mean_squared_error: 0.0498\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0265 - mean_squared_error: 0.0265 - val_loss: 0.0522 - val_mean_squared_error: 0.0522\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0271 - mean_squared_error: 0.0271 - val_loss: 0.0589 - val_mean_squared_error: 0.0589\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0261 - mean_squared_error: 0.0261 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0260 - mean_squared_error: 0.0260 - val_loss: 0.0536 - val_mean_squared_error: 0.0536\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0255 - mean_squared_error: 0.0255 - val_loss: 0.0505 - val_mean_squared_error: 0.0505\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0251 - mean_squared_error: 0.0251 - val_loss: 0.0547 - val_mean_squared_error: 0.0547\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0261 - mean_squared_error: 0.0261 - val_loss: 0.0498 - val_mean_squared_error: 0.0498\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0260 - mean_squared_error: 0.0260 - val_loss: 0.0551 - val_mean_squared_error: 0.0551\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0248 - mean_squared_error: 0.0248 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0245 - mean_squared_error: 0.0245 - val_loss: 0.0510 - val_mean_squared_error: 0.0510\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0249 - mean_squared_error: 0.0249 - val_loss: 0.0539 - val_mean_squared_error: 0.0539\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0242 - mean_squared_error: 0.0242 - val_loss: 0.0534 - val_mean_squared_error: 0.0534\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0234 - mean_squared_error: 0.0234 - val_loss: 0.0564 - val_mean_squared_error: 0.0564\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0237 - mean_squared_error: 0.0237 - val_loss: 0.0576 - val_mean_squared_error: 0.0576\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0554 - val_mean_squared_error: 0.0554\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0238 - mean_squared_error: 0.0238 - val_loss: 0.0583 - val_mean_squared_error: 0.0583\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0532 - val_mean_squared_error: 0.0532\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0500 - val_mean_squared_error: 0.0500\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0550 - val_mean_squared_error: 0.0550\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0519 - val_mean_squared_error: 0.0519\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0510 - val_mean_squared_error: 0.0510\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0216 - mean_squared_error: 0.0216 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0229 - mean_squared_error: 0.0229 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0557 - val_mean_squared_error: 0.0557\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0559 - val_mean_squared_error: 0.0559\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0566 - val_mean_squared_error: 0.0566\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0212 - mean_squared_error: 0.0212 - val_loss: 0.0492 - val_mean_squared_error: 0.0492\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0209 - mean_squared_error: 0.0209 - val_loss: 0.0545 - val_mean_squared_error: 0.0545\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0204 - mean_squared_error: 0.0204 - val_loss: 0.0520 - val_mean_squared_error: 0.0520\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0539 - val_mean_squared_error: 0.0539\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0209 - mean_squared_error: 0.0209 - val_loss: 0.0562 - val_mean_squared_error: 0.0562\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0553 - val_mean_squared_error: 0.0553\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0200 - mean_squared_error: 0.0200 - val_loss: 0.0553 - val_mean_squared_error: 0.0553\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0193 - mean_squared_error: 0.0193 - val_loss: 0.0584 - val_mean_squared_error: 0.0584\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0208 - mean_squared_error: 0.0208 - val_loss: 0.0531 - val_mean_squared_error: 0.0531\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0191 - mean_squared_error: 0.0191 - val_loss: 0.0549 - val_mean_squared_error: 0.0549\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0533 - val_mean_squared_error: 0.0533\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0200 - mean_squared_error: 0.0200 - val_loss: 0.0520 - val_mean_squared_error: 0.0520\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0186 - mean_squared_error: 0.0186 - val_loss: 0.0574 - val_mean_squared_error: 0.0574\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 775us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0488 - mean_squared_error: 0.0488 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0455 - mean_squared_error: 0.0455 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0436 - mean_squared_error: 0.0436 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0450 - mean_squared_error: 0.0450 - val_loss: 0.0471 - val_mean_squared_error: 0.0471\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0461 - mean_squared_error: 0.0461 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0447 - mean_squared_error: 0.0447 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0423 - mean_squared_error: 0.0423 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0415 - mean_squared_error: 0.0415 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0412 - mean_squared_error: 0.0412 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0407 - mean_squared_error: 0.0407 - val_loss: 0.0456 - val_mean_squared_error: 0.0456\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0393 - mean_squared_error: 0.0393 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0402 - mean_squared_error: 0.0402 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0376 - mean_squared_error: 0.0376 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0370 - mean_squared_error: 0.0370 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0370 - mean_squared_error: 0.0370 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0366 - mean_squared_error: 0.0366 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0511 - val_mean_squared_error: 0.0511\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0366 - mean_squared_error: 0.0366 - val_loss: 0.0471 - val_mean_squared_error: 0.0471\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0346 - mean_squared_error: 0.0346 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0344 - mean_squared_error: 0.0344 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0344 - mean_squared_error: 0.0344 - val_loss: 0.0510 - val_mean_squared_error: 0.0510\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0319 - mean_squared_error: 0.0319 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0307 - mean_squared_error: 0.0307 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0562 - val_mean_squared_error: 0.0562\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0486 - val_mean_squared_error: 0.0486\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0305 - mean_squared_error: 0.0305 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0302 - mean_squared_error: 0.0302 - val_loss: 0.0481 - val_mean_squared_error: 0.0481\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0314 - mean_squared_error: 0.0314 - val_loss: 0.0532 - val_mean_squared_error: 0.0532\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0312 - mean_squared_error: 0.0312 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0310 - mean_squared_error: 0.0310 - val_loss: 0.0506 - val_mean_squared_error: 0.0506\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0302 - mean_squared_error: 0.0302 - val_loss: 0.0501 - val_mean_squared_error: 0.0501\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0297 - mean_squared_error: 0.0297 - val_loss: 0.0486 - val_mean_squared_error: 0.0486\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0290 - mean_squared_error: 0.0290 - val_loss: 0.0611 - val_mean_squared_error: 0.0611\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0292 - mean_squared_error: 0.0292 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0290 - mean_squared_error: 0.0290 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0291 - mean_squared_error: 0.0291 - val_loss: 0.0559 - val_mean_squared_error: 0.0559\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0275 - mean_squared_error: 0.0275 - val_loss: 0.0492 - val_mean_squared_error: 0.0492\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0278 - mean_squared_error: 0.0278 - val_loss: 0.0550 - val_mean_squared_error: 0.0550\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0292 - mean_squared_error: 0.0292 - val_loss: 0.0504 - val_mean_squared_error: 0.0504\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0286 - mean_squared_error: 0.0286 - val_loss: 0.0520 - val_mean_squared_error: 0.0520\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0276 - mean_squared_error: 0.0276 - val_loss: 0.0492 - val_mean_squared_error: 0.0492\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0544 - val_mean_squared_error: 0.0544\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0281 - mean_squared_error: 0.0281 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0265 - mean_squared_error: 0.0265 - val_loss: 0.0649 - val_mean_squared_error: 0.0649\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0258 - mean_squared_error: 0.0258 - val_loss: 0.0586 - val_mean_squared_error: 0.0586\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0260 - mean_squared_error: 0.0260 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0256 - mean_squared_error: 0.0256 - val_loss: 0.0565 - val_mean_squared_error: 0.0565\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0259 - mean_squared_error: 0.0259 - val_loss: 0.0557 - val_mean_squared_error: 0.0557\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0526 - val_mean_squared_error: 0.0526\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0252 - mean_squared_error: 0.0252 - val_loss: 0.0560 - val_mean_squared_error: 0.0560\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0251 - mean_squared_error: 0.0251 - val_loss: 0.0523 - val_mean_squared_error: 0.0523\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0266 - mean_squared_error: 0.0266 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0243 - mean_squared_error: 0.0243 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0255 - mean_squared_error: 0.0255 - val_loss: 0.0501 - val_mean_squared_error: 0.0501\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0250 - mean_squared_error: 0.0250 - val_loss: 0.0549 - val_mean_squared_error: 0.0549\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0249 - mean_squared_error: 0.0249 - val_loss: 0.0517 - val_mean_squared_error: 0.0517\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0248 - mean_squared_error: 0.0248 - val_loss: 0.0556 - val_mean_squared_error: 0.0556\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0567 - val_mean_squared_error: 0.0567\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0237 - mean_squared_error: 0.0237 - val_loss: 0.0670 - val_mean_squared_error: 0.0670\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0586 - val_mean_squared_error: 0.0586\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0239 - mean_squared_error: 0.0239 - val_loss: 0.0605 - val_mean_squared_error: 0.0605\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0621 - val_mean_squared_error: 0.0621\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0232 - mean_squared_error: 0.0232 - val_loss: 0.0679 - val_mean_squared_error: 0.0679\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0227 - mean_squared_error: 0.0227 - val_loss: 0.0599 - val_mean_squared_error: 0.0599\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0236 - mean_squared_error: 0.0236 - val_loss: 0.0666 - val_mean_squared_error: 0.0666\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0620 - val_mean_squared_error: 0.0620\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0559 - val_mean_squared_error: 0.0559\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0636 - val_mean_squared_error: 0.0636\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0614 - val_mean_squared_error: 0.0614\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0672 - val_mean_squared_error: 0.0672\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0216 - mean_squared_error: 0.0216 - val_loss: 0.0541 - val_mean_squared_error: 0.0541\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0219 - mean_squared_error: 0.0219 - val_loss: 0.0565 - val_mean_squared_error: 0.0565\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0222 - mean_squared_error: 0.0222 - val_loss: 0.0598 - val_mean_squared_error: 0.0598\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0231 - mean_squared_error: 0.0231 - val_loss: 0.0649 - val_mean_squared_error: 0.0649\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 787us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0529 - mean_squared_error: 0.0529 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0466 - mean_squared_error: 0.0466 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0440 - mean_squared_error: 0.0440 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0430 - mean_squared_error: 0.0430 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0411 - mean_squared_error: 0.0411 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0428 - mean_squared_error: 0.0428 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0403 - mean_squared_error: 0.0403 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0394 - mean_squared_error: 0.0394 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0392 - mean_squared_error: 0.0392 - val_loss: 0.0451 - val_mean_squared_error: 0.0451\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0383 - mean_squared_error: 0.0383 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.0494 - val_mean_squared_error: 0.0494\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0500 - val_mean_squared_error: 0.0500\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0504 - val_mean_squared_error: 0.0504\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0357 - mean_squared_error: 0.0357 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0365 - mean_squared_error: 0.0365 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0528 - val_mean_squared_error: 0.0528\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0341 - mean_squared_error: 0.0341 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0516 - val_mean_squared_error: 0.0516\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0543 - val_mean_squared_error: 0.0543\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0353 - mean_squared_error: 0.0353 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0326 - mean_squared_error: 0.0326 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0509 - val_mean_squared_error: 0.0509\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0309 - mean_squared_error: 0.0309 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0313 - mean_squared_error: 0.0313 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0300 - mean_squared_error: 0.0300 - val_loss: 0.0528 - val_mean_squared_error: 0.0528\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0306 - mean_squared_error: 0.0306 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0304 - mean_squared_error: 0.0304 - val_loss: 0.0538 - val_mean_squared_error: 0.0538\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0292 - mean_squared_error: 0.0292 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0297 - mean_squared_error: 0.0297 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0584 - val_mean_squared_error: 0.0584\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0295 - mean_squared_error: 0.0295 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0281 - mean_squared_error: 0.0281 - val_loss: 0.0575 - val_mean_squared_error: 0.0575\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0275 - mean_squared_error: 0.0275 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0508 - val_mean_squared_error: 0.0508\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0272 - mean_squared_error: 0.0272 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0271 - mean_squared_error: 0.0271 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0268 - mean_squared_error: 0.0268 - val_loss: 0.0528 - val_mean_squared_error: 0.0528\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0265 - mean_squared_error: 0.0265 - val_loss: 0.0575 - val_mean_squared_error: 0.0575\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0511 - val_mean_squared_error: 0.0511\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0275 - mean_squared_error: 0.0275 - val_loss: 0.0502 - val_mean_squared_error: 0.0502\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0254 - mean_squared_error: 0.0254 - val_loss: 0.0586 - val_mean_squared_error: 0.0586\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0256 - mean_squared_error: 0.0256 - val_loss: 0.0533 - val_mean_squared_error: 0.0533\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0266 - mean_squared_error: 0.0266 - val_loss: 0.0553 - val_mean_squared_error: 0.0553\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0258 - mean_squared_error: 0.0258 - val_loss: 0.0615 - val_mean_squared_error: 0.0615\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0251 - mean_squared_error: 0.0251 - val_loss: 0.0512 - val_mean_squared_error: 0.0512\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0258 - mean_squared_error: 0.0258 - val_loss: 0.0562 - val_mean_squared_error: 0.0562\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0236 - mean_squared_error: 0.0236 - val_loss: 0.0671 - val_mean_squared_error: 0.0671\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0240 - mean_squared_error: 0.0240 - val_loss: 0.0526 - val_mean_squared_error: 0.0526\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0240 - mean_squared_error: 0.0240 - val_loss: 0.0550 - val_mean_squared_error: 0.0550\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0236 - mean_squared_error: 0.0236 - val_loss: 0.0598 - val_mean_squared_error: 0.0598\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0554 - val_mean_squared_error: 0.0554\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0563 - val_mean_squared_error: 0.0563\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0227 - mean_squared_error: 0.0227 - val_loss: 0.0511 - val_mean_squared_error: 0.0511\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0226 - mean_squared_error: 0.0226 - val_loss: 0.0624 - val_mean_squared_error: 0.0624\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0552 - val_mean_squared_error: 0.0552\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0578 - val_mean_squared_error: 0.0578\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0572 - val_mean_squared_error: 0.0572\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0589 - val_mean_squared_error: 0.0589\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0699 - val_mean_squared_error: 0.0699\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0219 - mean_squared_error: 0.0219 - val_loss: 0.0566 - val_mean_squared_error: 0.0566\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0216 - mean_squared_error: 0.0216 - val_loss: 0.0579 - val_mean_squared_error: 0.0579\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0219 - mean_squared_error: 0.0219 - val_loss: 0.0549 - val_mean_squared_error: 0.0549\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0206 - mean_squared_error: 0.0206 - val_loss: 0.0595 - val_mean_squared_error: 0.0595\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0208 - mean_squared_error: 0.0208 - val_loss: 0.0580 - val_mean_squared_error: 0.0580\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0572 - val_mean_squared_error: 0.0572\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0201 - mean_squared_error: 0.0201 - val_loss: 0.0594 - val_mean_squared_error: 0.0594\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0197 - mean_squared_error: 0.0197 - val_loss: 0.0593 - val_mean_squared_error: 0.0593\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0564 - val_mean_squared_error: 0.0564\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0601 - val_mean_squared_error: 0.0601\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0662 - val_mean_squared_error: 0.0662\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0191 - mean_squared_error: 0.0191 - val_loss: 0.0581 - val_mean_squared_error: 0.0581\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0652 - val_mean_squared_error: 0.0652\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0197 - mean_squared_error: 0.0197 - val_loss: 0.0624 - val_mean_squared_error: 0.0624\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0199 - mean_squared_error: 0.0199 - val_loss: 0.0609 - val_mean_squared_error: 0.0609\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0193 - mean_squared_error: 0.0193 - val_loss: 0.0563 - val_mean_squared_error: 0.0563\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0191 - mean_squared_error: 0.0191 - val_loss: 0.0569 - val_mean_squared_error: 0.0569\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 779us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0535 - mean_squared_error: 0.0535 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0463 - mean_squared_error: 0.0463 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0453 - mean_squared_error: 0.0453 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0464 - mean_squared_error: 0.0464 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0428 - mean_squared_error: 0.0428 - val_loss: 0.0481 - val_mean_squared_error: 0.0481\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0425 - mean_squared_error: 0.0425 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0422 - mean_squared_error: 0.0422 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0412 - mean_squared_error: 0.0412 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0402 - mean_squared_error: 0.0402 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0393 - mean_squared_error: 0.0393 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0415 - mean_squared_error: 0.0415 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0399 - mean_squared_error: 0.0399 - val_loss: 0.0504 - val_mean_squared_error: 0.0504\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0481 - val_mean_squared_error: 0.0481\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0383 - mean_squared_error: 0.0383 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0365 - mean_squared_error: 0.0365 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0370 - mean_squared_error: 0.0370 - val_loss: 0.0502 - val_mean_squared_error: 0.0502\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0370 - mean_squared_error: 0.0370 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0360 - mean_squared_error: 0.0360 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0543 - val_mean_squared_error: 0.0543\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0352 - mean_squared_error: 0.0352 - val_loss: 0.0481 - val_mean_squared_error: 0.0481\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0361 - mean_squared_error: 0.0361 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0341 - mean_squared_error: 0.0341 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0500 - val_mean_squared_error: 0.0500\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0340 - mean_squared_error: 0.0340 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0341 - mean_squared_error: 0.0341 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0490 - val_mean_squared_error: 0.0490\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0510 - val_mean_squared_error: 0.0510\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0511 - val_mean_squared_error: 0.0511\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0315 - mean_squared_error: 0.0315 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0494 - val_mean_squared_error: 0.0494\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0312 - mean_squared_error: 0.0312 - val_loss: 0.0502 - val_mean_squared_error: 0.0502\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0309 - mean_squared_error: 0.0309 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0306 - mean_squared_error: 0.0306 - val_loss: 0.0523 - val_mean_squared_error: 0.0523\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0313 - mean_squared_error: 0.0313 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0309 - mean_squared_error: 0.0309 - val_loss: 0.0532 - val_mean_squared_error: 0.0532\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0298 - mean_squared_error: 0.0298 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0292 - mean_squared_error: 0.0292 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0295 - mean_squared_error: 0.0295 - val_loss: 0.0522 - val_mean_squared_error: 0.0522\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0289 - mean_squared_error: 0.0289 - val_loss: 0.0518 - val_mean_squared_error: 0.0518\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0304 - mean_squared_error: 0.0304 - val_loss: 0.0509 - val_mean_squared_error: 0.0509\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0293 - mean_squared_error: 0.0293 - val_loss: 0.0528 - val_mean_squared_error: 0.0528\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0281 - mean_squared_error: 0.0281 - val_loss: 0.0551 - val_mean_squared_error: 0.0551\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0278 - mean_squared_error: 0.0278 - val_loss: 0.0548 - val_mean_squared_error: 0.0548\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0280 - mean_squared_error: 0.0280 - val_loss: 0.0557 - val_mean_squared_error: 0.0557\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0278 - mean_squared_error: 0.0278 - val_loss: 0.0568 - val_mean_squared_error: 0.0568\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0281 - mean_squared_error: 0.0281 - val_loss: 0.0527 - val_mean_squared_error: 0.0527\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0268 - mean_squared_error: 0.0268 - val_loss: 0.0548 - val_mean_squared_error: 0.0548\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0276 - mean_squared_error: 0.0276 - val_loss: 0.0522 - val_mean_squared_error: 0.0522\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0601 - val_mean_squared_error: 0.0601\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0270 - mean_squared_error: 0.0270 - val_loss: 0.0534 - val_mean_squared_error: 0.0534\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0554 - val_mean_squared_error: 0.0554\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0263 - mean_squared_error: 0.0263 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0269 - mean_squared_error: 0.0269 - val_loss: 0.0511 - val_mean_squared_error: 0.0511\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0253 - mean_squared_error: 0.0253 - val_loss: 0.0544 - val_mean_squared_error: 0.0544\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0261 - mean_squared_error: 0.0261 - val_loss: 0.0539 - val_mean_squared_error: 0.0539\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0263 - mean_squared_error: 0.0263 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0247 - mean_squared_error: 0.0247 - val_loss: 0.0652 - val_mean_squared_error: 0.0652\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0258 - mean_squared_error: 0.0258 - val_loss: 0.0534 - val_mean_squared_error: 0.0534\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0252 - mean_squared_error: 0.0252 - val_loss: 0.0574 - val_mean_squared_error: 0.0574\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0250 - mean_squared_error: 0.0250 - val_loss: 0.0548 - val_mean_squared_error: 0.0548\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0243 - mean_squared_error: 0.0243 - val_loss: 0.0536 - val_mean_squared_error: 0.0536\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0240 - mean_squared_error: 0.0240 - val_loss: 0.0531 - val_mean_squared_error: 0.0531\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0238 - mean_squared_error: 0.0238 - val_loss: 0.0568 - val_mean_squared_error: 0.0568\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0242 - mean_squared_error: 0.0242 - val_loss: 0.0544 - val_mean_squared_error: 0.0544\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0245 - mean_squared_error: 0.0245 - val_loss: 0.0573 - val_mean_squared_error: 0.0573\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0234 - mean_squared_error: 0.0234 - val_loss: 0.0571 - val_mean_squared_error: 0.0571\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0231 - mean_squared_error: 0.0231 - val_loss: 0.0526 - val_mean_squared_error: 0.0526\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0229 - mean_squared_error: 0.0229 - val_loss: 0.0543 - val_mean_squared_error: 0.0543\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0235 - mean_squared_error: 0.0235 - val_loss: 0.0544 - val_mean_squared_error: 0.0544\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0229 - mean_squared_error: 0.0229 - val_loss: 0.0560 - val_mean_squared_error: 0.0560\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0230 - mean_squared_error: 0.0230 - val_loss: 0.0558 - val_mean_squared_error: 0.0558\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0228 - mean_squared_error: 0.0228 - val_loss: 0.0544 - val_mean_squared_error: 0.0544\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0623 - val_mean_squared_error: 0.0623\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0573 - val_mean_squared_error: 0.0573\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0219 - mean_squared_error: 0.0219 - val_loss: 0.0578 - val_mean_squared_error: 0.0578\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0564 - val_mean_squared_error: 0.0564\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0222 - mean_squared_error: 0.0222 - val_loss: 0.0585 - val_mean_squared_error: 0.0585\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0220 - mean_squared_error: 0.0220 - val_loss: 0.0523 - val_mean_squared_error: 0.0523\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0544 - val_mean_squared_error: 0.0544\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0583 - val_mean_squared_error: 0.0583\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0550 - val_mean_squared_error: 0.0550\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0209 - mean_squared_error: 0.0209 - val_loss: 0.0645 - val_mean_squared_error: 0.0645\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0586 - val_mean_squared_error: 0.0586\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0213 - mean_squared_error: 0.0213 - val_loss: 0.0575 - val_mean_squared_error: 0.0575\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 807us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0429 - mean_squared_error: 0.0429 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0445 - mean_squared_error: 0.0445 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0422 - mean_squared_error: 0.0422 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0420 - mean_squared_error: 0.0420 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0434 - mean_squared_error: 0.0434 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.0451 - val_mean_squared_error: 0.0451\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0451 - val_mean_squared_error: 0.0451\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0403 - mean_squared_error: 0.0403 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0450 - val_mean_squared_error: 0.0450\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.0452 - val_mean_squared_error: 0.0452\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0446 - val_mean_squared_error: 0.0446\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0446 - val_mean_squared_error: 0.0446\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0366 - mean_squared_error: 0.0366 - val_loss: 0.0446 - val_mean_squared_error: 0.0446\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0451 - val_mean_squared_error: 0.0451\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0456 - val_mean_squared_error: 0.0456\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.0452 - val_mean_squared_error: 0.0452\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0452 - val_mean_squared_error: 0.0452\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0549 - val_mean_squared_error: 0.0549\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0363 - mean_squared_error: 0.0363 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0351 - mean_squared_error: 0.0351 - val_loss: 0.0449 - val_mean_squared_error: 0.0449\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0352 - mean_squared_error: 0.0352 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0338 - mean_squared_error: 0.0338 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0328 - mean_squared_error: 0.0328 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0319 - mean_squared_error: 0.0319 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0526 - val_mean_squared_error: 0.0526\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0307 - mean_squared_error: 0.0307 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0304 - mean_squared_error: 0.0304 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0289 - mean_squared_error: 0.0289 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0301 - mean_squared_error: 0.0301 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0300 - mean_squared_error: 0.0300 - val_loss: 0.0487 - val_mean_squared_error: 0.0487\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0287 - mean_squared_error: 0.0287 - val_loss: 0.0490 - val_mean_squared_error: 0.0490\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0279 - mean_squared_error: 0.0279 - val_loss: 0.0481 - val_mean_squared_error: 0.0481\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0508 - val_mean_squared_error: 0.0508\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0287 - mean_squared_error: 0.0287 - val_loss: 0.0498 - val_mean_squared_error: 0.0498\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0279 - mean_squared_error: 0.0279 - val_loss: 0.0488 - val_mean_squared_error: 0.0488\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0277 - mean_squared_error: 0.0277 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0518 - val_mean_squared_error: 0.0518\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0261 - mean_squared_error: 0.0261 - val_loss: 0.0490 - val_mean_squared_error: 0.0490\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0258 - mean_squared_error: 0.0258 - val_loss: 0.0506 - val_mean_squared_error: 0.0506\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0245 - mean_squared_error: 0.0245 - val_loss: 0.0504 - val_mean_squared_error: 0.0504\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0245 - mean_squared_error: 0.0245 - val_loss: 0.0565 - val_mean_squared_error: 0.0565\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0252 - mean_squared_error: 0.0252 - val_loss: 0.0534 - val_mean_squared_error: 0.0534\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0253 - mean_squared_error: 0.0253 - val_loss: 0.0522 - val_mean_squared_error: 0.0522\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0252 - mean_squared_error: 0.0252 - val_loss: 0.0536 - val_mean_squared_error: 0.0536\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0535 - val_mean_squared_error: 0.0535\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0239 - mean_squared_error: 0.0239 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0235 - mean_squared_error: 0.0235 - val_loss: 0.0532 - val_mean_squared_error: 0.0532\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0238 - mean_squared_error: 0.0238 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0494 - val_mean_squared_error: 0.0494\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0559 - val_mean_squared_error: 0.0559\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0226 - mean_squared_error: 0.0226 - val_loss: 0.0558 - val_mean_squared_error: 0.0558\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0599 - val_mean_squared_error: 0.0599\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0587 - val_mean_squared_error: 0.0587\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0505 - val_mean_squared_error: 0.0505\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0577 - val_mean_squared_error: 0.0577\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0217 - mean_squared_error: 0.0217 - val_loss: 0.0579 - val_mean_squared_error: 0.0579\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0559 - val_mean_squared_error: 0.0559\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0220 - mean_squared_error: 0.0220 - val_loss: 0.0630 - val_mean_squared_error: 0.0630\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0210 - mean_squared_error: 0.0210 - val_loss: 0.0630 - val_mean_squared_error: 0.0630\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0210 - mean_squared_error: 0.0210 - val_loss: 0.0559 - val_mean_squared_error: 0.0559\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0197 - mean_squared_error: 0.0197 - val_loss: 0.0563 - val_mean_squared_error: 0.0563\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0203 - mean_squared_error: 0.0203 - val_loss: 0.0526 - val_mean_squared_error: 0.0526\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0583 - val_mean_squared_error: 0.0583\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0196 - mean_squared_error: 0.0196 - val_loss: 0.0681 - val_mean_squared_error: 0.0681\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0199 - mean_squared_error: 0.0199 - val_loss: 0.0565 - val_mean_squared_error: 0.0565\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0200 - mean_squared_error: 0.0200 - val_loss: 0.0621 - val_mean_squared_error: 0.0621\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0184 - mean_squared_error: 0.0184 - val_loss: 0.0618 - val_mean_squared_error: 0.0618\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0191 - mean_squared_error: 0.0191 - val_loss: 0.0646 - val_mean_squared_error: 0.0646\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0187 - mean_squared_error: 0.0187 - val_loss: 0.0618 - val_mean_squared_error: 0.0618\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0180 - mean_squared_error: 0.0180 - val_loss: 0.0601 - val_mean_squared_error: 0.0601\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0184 - mean_squared_error: 0.0184 - val_loss: 0.0628 - val_mean_squared_error: 0.0628\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0186 - mean_squared_error: 0.0186 - val_loss: 0.0602 - val_mean_squared_error: 0.0602\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0181 - mean_squared_error: 0.0181 - val_loss: 0.0558 - val_mean_squared_error: 0.0558\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0534 - val_mean_squared_error: 0.0534\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0596 - val_mean_squared_error: 0.0596\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0178 - mean_squared_error: 0.0178 - val_loss: 0.0577 - val_mean_squared_error: 0.0577\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0170 - mean_squared_error: 0.0170 - val_loss: 0.0584 - val_mean_squared_error: 0.0584\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0174 - mean_squared_error: 0.0174 - val_loss: 0.0570 - val_mean_squared_error: 0.0570\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0164 - mean_squared_error: 0.0164 - val_loss: 0.0563 - val_mean_squared_error: 0.0563\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 821us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0534 - mean_squared_error: 0.0534 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0466 - mean_squared_error: 0.0466 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0452 - mean_squared_error: 0.0452 - val_loss: 0.0471 - val_mean_squared_error: 0.0471\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0463 - mean_squared_error: 0.0463 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0437 - mean_squared_error: 0.0437 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0425 - mean_squared_error: 0.0425 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0444 - mean_squared_error: 0.0444 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0409 - mean_squared_error: 0.0409 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0414 - mean_squared_error: 0.0414 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0403 - mean_squared_error: 0.0403 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0426 - mean_squared_error: 0.0426 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0407 - mean_squared_error: 0.0407 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0401 - mean_squared_error: 0.0401 - val_loss: 0.0451 - val_mean_squared_error: 0.0451\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0397 - mean_squared_error: 0.0397 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0390 - mean_squared_error: 0.0390 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0535 - val_mean_squared_error: 0.0535\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0367 - mean_squared_error: 0.0367 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0343 - mean_squared_error: 0.0343 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0344 - mean_squared_error: 0.0344 - val_loss: 0.0496 - val_mean_squared_error: 0.0496\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0340 - mean_squared_error: 0.0340 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0340 - mean_squared_error: 0.0340 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0336 - mean_squared_error: 0.0336 - val_loss: 0.0506 - val_mean_squared_error: 0.0506\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0314 - mean_squared_error: 0.0314 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0320 - mean_squared_error: 0.0320 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0308 - mean_squared_error: 0.0308 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0298 - mean_squared_error: 0.0298 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0297 - mean_squared_error: 0.0297 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0291 - mean_squared_error: 0.0291 - val_loss: 0.0512 - val_mean_squared_error: 0.0512\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0283 - mean_squared_error: 0.0283 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0283 - mean_squared_error: 0.0283 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0286 - mean_squared_error: 0.0286 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0277 - mean_squared_error: 0.0277 - val_loss: 0.0533 - val_mean_squared_error: 0.0533\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0272 - mean_squared_error: 0.0272 - val_loss: 0.0494 - val_mean_squared_error: 0.0494\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0291 - mean_squared_error: 0.0291 - val_loss: 0.0577 - val_mean_squared_error: 0.0577\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0271 - mean_squared_error: 0.0271 - val_loss: 0.0529 - val_mean_squared_error: 0.0529\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0275 - mean_squared_error: 0.0275 - val_loss: 0.0616 - val_mean_squared_error: 0.0616\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0520 - val_mean_squared_error: 0.0520\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0271 - mean_squared_error: 0.0271 - val_loss: 0.0510 - val_mean_squared_error: 0.0510\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0250 - mean_squared_error: 0.0250 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0280 - mean_squared_error: 0.0280 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0535 - val_mean_squared_error: 0.0535\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0259 - mean_squared_error: 0.0259 - val_loss: 0.0546 - val_mean_squared_error: 0.0546\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0251 - mean_squared_error: 0.0251 - val_loss: 0.0508 - val_mean_squared_error: 0.0508\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0248 - mean_squared_error: 0.0248 - val_loss: 0.0557 - val_mean_squared_error: 0.0557\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0248 - mean_squared_error: 0.0248 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0257 - mean_squared_error: 0.0257 - val_loss: 0.0515 - val_mean_squared_error: 0.0515\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0250 - mean_squared_error: 0.0250 - val_loss: 0.0573 - val_mean_squared_error: 0.0573\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0234 - mean_squared_error: 0.0234 - val_loss: 0.0541 - val_mean_squared_error: 0.0541\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0529 - val_mean_squared_error: 0.0529\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0232 - mean_squared_error: 0.0232 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0240 - mean_squared_error: 0.0240 - val_loss: 0.0614 - val_mean_squared_error: 0.0614\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0234 - mean_squared_error: 0.0234 - val_loss: 0.0577 - val_mean_squared_error: 0.0577\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0570 - val_mean_squared_error: 0.0570\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0590 - val_mean_squared_error: 0.0590\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0227 - mean_squared_error: 0.0227 - val_loss: 0.0553 - val_mean_squared_error: 0.0553\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0219 - mean_squared_error: 0.0219 - val_loss: 0.0607 - val_mean_squared_error: 0.0607\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0221 - mean_squared_error: 0.0221 - val_loss: 0.0534 - val_mean_squared_error: 0.0534\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0213 - mean_squared_error: 0.0213 - val_loss: 0.0542 - val_mean_squared_error: 0.0542\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0213 - mean_squared_error: 0.0213 - val_loss: 0.0601 - val_mean_squared_error: 0.0601\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0209 - mean_squared_error: 0.0209 - val_loss: 0.0569 - val_mean_squared_error: 0.0569\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0623 - val_mean_squared_error: 0.0623\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0206 - mean_squared_error: 0.0206 - val_loss: 0.0620 - val_mean_squared_error: 0.0620\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0641 - val_mean_squared_error: 0.0641\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0194 - mean_squared_error: 0.0194 - val_loss: 0.0620 - val_mean_squared_error: 0.0620\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0209 - mean_squared_error: 0.0209 - val_loss: 0.0574 - val_mean_squared_error: 0.0574\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0602 - val_mean_squared_error: 0.0602\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0196 - mean_squared_error: 0.0196 - val_loss: 0.0548 - val_mean_squared_error: 0.0548\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0194 - mean_squared_error: 0.0194 - val_loss: 0.0610 - val_mean_squared_error: 0.0610\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0212 - mean_squared_error: 0.0212 - val_loss: 0.0603 - val_mean_squared_error: 0.0603\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0593 - val_mean_squared_error: 0.0593\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0588 - val_mean_squared_error: 0.0588\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0186 - mean_squared_error: 0.0186 - val_loss: 0.0537 - val_mean_squared_error: 0.0537\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0190 - mean_squared_error: 0.0190 - val_loss: 0.0688 - val_mean_squared_error: 0.0688\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0654 - val_mean_squared_error: 0.0654\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0184 - mean_squared_error: 0.0184 - val_loss: 0.0629 - val_mean_squared_error: 0.0629\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0188 - mean_squared_error: 0.0188 - val_loss: 0.0537 - val_mean_squared_error: 0.0537\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0182 - mean_squared_error: 0.0182 - val_loss: 0.0607 - val_mean_squared_error: 0.0607\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0185 - mean_squared_error: 0.0185 - val_loss: 0.0594 - val_mean_squared_error: 0.0594\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0186 - mean_squared_error: 0.0186 - val_loss: 0.0619 - val_mean_squared_error: 0.0619\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0176 - mean_squared_error: 0.0176 - val_loss: 0.0592 - val_mean_squared_error: 0.0592\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0189 - mean_squared_error: 0.0189 - val_loss: 0.0623 - val_mean_squared_error: 0.0623\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0188 - mean_squared_error: 0.0188 - val_loss: 0.0614 - val_mean_squared_error: 0.0614\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 861us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0518 - mean_squared_error: 0.0518 - val_loss: 0.0492 - val_mean_squared_error: 0.0492\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0452 - mean_squared_error: 0.0452 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0457 - mean_squared_error: 0.0457 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0435 - mean_squared_error: 0.0435 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0435 - mean_squared_error: 0.0435 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0421 - mean_squared_error: 0.0421 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0415 - mean_squared_error: 0.0415 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0424 - mean_squared_error: 0.0424 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0417 - mean_squared_error: 0.0417 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0420 - mean_squared_error: 0.0420 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0456 - val_mean_squared_error: 0.0456\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.0451 - val_mean_squared_error: 0.0451\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0361 - mean_squared_error: 0.0361 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0351 - mean_squared_error: 0.0351 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0340 - mean_squared_error: 0.0340 - val_loss: 0.0450 - val_mean_squared_error: 0.0450\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0464 - val_mean_squared_error: 0.0464\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0453 - val_mean_squared_error: 0.0453\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0328 - mean_squared_error: 0.0328 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0320 - mean_squared_error: 0.0320 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0332 - mean_squared_error: 0.0332 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0312 - mean_squared_error: 0.0312 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0301 - mean_squared_error: 0.0301 - val_loss: 0.0462 - val_mean_squared_error: 0.0462\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0306 - mean_squared_error: 0.0306 - val_loss: 0.0495 - val_mean_squared_error: 0.0495\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0471 - val_mean_squared_error: 0.0471\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0295 - mean_squared_error: 0.0295 - val_loss: 0.0486 - val_mean_squared_error: 0.0486\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0306 - mean_squared_error: 0.0306 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0278 - mean_squared_error: 0.0278 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0298 - mean_squared_error: 0.0298 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0295 - mean_squared_error: 0.0295 - val_loss: 0.0490 - val_mean_squared_error: 0.0490\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0285 - mean_squared_error: 0.0285 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0280 - mean_squared_error: 0.0280 - val_loss: 0.0488 - val_mean_squared_error: 0.0488\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0285 - mean_squared_error: 0.0285 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0555 - val_mean_squared_error: 0.0555\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0275 - mean_squared_error: 0.0275 - val_loss: 0.0470 - val_mean_squared_error: 0.0470\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0270 - mean_squared_error: 0.0270 - val_loss: 0.0496 - val_mean_squared_error: 0.0496\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0277 - mean_squared_error: 0.0277 - val_loss: 0.0500 - val_mean_squared_error: 0.0500\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0265 - mean_squared_error: 0.0265 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0270 - mean_squared_error: 0.0270 - val_loss: 0.0508 - val_mean_squared_error: 0.0508\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0271 - mean_squared_error: 0.0271 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0266 - mean_squared_error: 0.0266 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0268 - mean_squared_error: 0.0268 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0260 - mean_squared_error: 0.0260 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0257 - mean_squared_error: 0.0257 - val_loss: 0.0482 - val_mean_squared_error: 0.0482\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0259 - mean_squared_error: 0.0259 - val_loss: 0.0489 - val_mean_squared_error: 0.0489\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0252 - mean_squared_error: 0.0252 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0261 - mean_squared_error: 0.0261 - val_loss: 0.0536 - val_mean_squared_error: 0.0536\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0253 - mean_squared_error: 0.0253 - val_loss: 0.0480 - val_mean_squared_error: 0.0480\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0243 - mean_squared_error: 0.0243 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0254 - mean_squared_error: 0.0254 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0254 - mean_squared_error: 0.0254 - val_loss: 0.0502 - val_mean_squared_error: 0.0502\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0243 - mean_squared_error: 0.0243 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0239 - mean_squared_error: 0.0239 - val_loss: 0.0487 - val_mean_squared_error: 0.0487\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0252 - mean_squared_error: 0.0252 - val_loss: 0.0537 - val_mean_squared_error: 0.0537\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0517 - val_mean_squared_error: 0.0517\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0241 - mean_squared_error: 0.0241 - val_loss: 0.0484 - val_mean_squared_error: 0.0484\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0577 - val_mean_squared_error: 0.0577\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0247 - mean_squared_error: 0.0247 - val_loss: 0.0573 - val_mean_squared_error: 0.0573\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0235 - mean_squared_error: 0.0235 - val_loss: 0.0513 - val_mean_squared_error: 0.0513\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0234 - mean_squared_error: 0.0234 - val_loss: 0.0537 - val_mean_squared_error: 0.0537\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0230 - mean_squared_error: 0.0230 - val_loss: 0.0520 - val_mean_squared_error: 0.0520\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0230 - mean_squared_error: 0.0230 - val_loss: 0.0496 - val_mean_squared_error: 0.0496\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0229 - mean_squared_error: 0.0229 - val_loss: 0.0506 - val_mean_squared_error: 0.0506\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0216 - mean_squared_error: 0.0216 - val_loss: 0.0531 - val_mean_squared_error: 0.0531\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0222 - mean_squared_error: 0.0222 - val_loss: 0.0600 - val_mean_squared_error: 0.0600\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0530 - val_mean_squared_error: 0.0530\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0584 - val_mean_squared_error: 0.0584\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0224 - mean_squared_error: 0.0224 - val_loss: 0.0512 - val_mean_squared_error: 0.0512\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0211 - mean_squared_error: 0.0211 - val_loss: 0.0538 - val_mean_squared_error: 0.0538\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0227 - mean_squared_error: 0.0227 - val_loss: 0.0527 - val_mean_squared_error: 0.0527\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0571 - val_mean_squared_error: 0.0571\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0210 - mean_squared_error: 0.0210 - val_loss: 0.0558 - val_mean_squared_error: 0.0558\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 893us/step\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\V\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.0506 - mean_squared_error: 0.0506 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 2/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 3/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0458 - mean_squared_error: 0.0458 - val_loss: 0.0466 - val_mean_squared_error: 0.0466\n",
"Epoch 4/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - val_loss: 0.0458 - val_mean_squared_error: 0.0458\n",
"Epoch 5/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 6/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0438 - mean_squared_error: 0.0438 - val_loss: 0.0456 - val_mean_squared_error: 0.0456\n",
"Epoch 7/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0426 - mean_squared_error: 0.0426 - val_loss: 0.0460 - val_mean_squared_error: 0.0460\n",
"Epoch 8/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0423 - mean_squared_error: 0.0423 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 9/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0433 - mean_squared_error: 0.0433 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 10/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0416 - mean_squared_error: 0.0416 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 11/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 12/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 13/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 14/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0448 - val_mean_squared_error: 0.0448\n",
"Epoch 15/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0390 - mean_squared_error: 0.0390 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 16/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.0448 - val_mean_squared_error: 0.0448\n",
"Epoch 17/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0383 - mean_squared_error: 0.0383 - val_loss: 0.0449 - val_mean_squared_error: 0.0449\n",
"Epoch 18/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0450 - val_mean_squared_error: 0.0450\n",
"Epoch 19/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0486 - val_mean_squared_error: 0.0486\n",
"Epoch 20/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.0449 - val_mean_squared_error: 0.0449\n",
"Epoch 21/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0479 - val_mean_squared_error: 0.0479\n",
"Epoch 22/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0447 - val_mean_squared_error: 0.0447\n",
"Epoch 23/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0445 - val_mean_squared_error: 0.0445\n",
"Epoch 24/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0353 - mean_squared_error: 0.0353 - val_loss: 0.0447 - val_mean_squared_error: 0.0447\n",
"Epoch 25/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0351 - mean_squared_error: 0.0351 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 26/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0353 - mean_squared_error: 0.0353 - val_loss: 0.0467 - val_mean_squared_error: 0.0467\n",
"Epoch 27/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0477 - val_mean_squared_error: 0.0477\n",
"Epoch 28/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0459 - val_mean_squared_error: 0.0459\n",
"Epoch 29/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0340 - mean_squared_error: 0.0340 - val_loss: 0.0452 - val_mean_squared_error: 0.0452\n",
"Epoch 30/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0455 - val_mean_squared_error: 0.0455\n",
"Epoch 31/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0330 - mean_squared_error: 0.0330 - val_loss: 0.0473 - val_mean_squared_error: 0.0473\n",
"Epoch 32/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 33/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 34/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0332 - mean_squared_error: 0.0332 - val_loss: 0.0483 - val_mean_squared_error: 0.0483\n",
"Epoch 35/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0318 - mean_squared_error: 0.0318 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 36/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0315 - mean_squared_error: 0.0315 - val_loss: 0.0461 - val_mean_squared_error: 0.0461\n",
"Epoch 37/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0472 - val_mean_squared_error: 0.0472\n",
"Epoch 38/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0301 - mean_squared_error: 0.0301 - val_loss: 0.0457 - val_mean_squared_error: 0.0457\n",
"Epoch 39/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0465 - val_mean_squared_error: 0.0465\n",
"Epoch 40/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0304 - mean_squared_error: 0.0304 - val_loss: 0.0528 - val_mean_squared_error: 0.0528\n",
"Epoch 41/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0307 - mean_squared_error: 0.0307 - val_loss: 0.0476 - val_mean_squared_error: 0.0476\n",
"Epoch 42/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0295 - mean_squared_error: 0.0295 - val_loss: 0.0454 - val_mean_squared_error: 0.0454\n",
"Epoch 43/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0291 - mean_squared_error: 0.0291 - val_loss: 0.0475 - val_mean_squared_error: 0.0475\n",
"Epoch 44/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0291 - mean_squared_error: 0.0291 - val_loss: 0.0463 - val_mean_squared_error: 0.0463\n",
"Epoch 45/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0506 - val_mean_squared_error: 0.0506\n",
"Epoch 46/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0290 - mean_squared_error: 0.0290 - val_loss: 0.0468 - val_mean_squared_error: 0.0468\n",
"Epoch 47/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0278 - mean_squared_error: 0.0278 - val_loss: 0.0511 - val_mean_squared_error: 0.0511\n",
"Epoch 48/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0272 - mean_squared_error: 0.0272 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 49/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0268 - mean_squared_error: 0.0268 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 50/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0474 - val_mean_squared_error: 0.0474\n",
"Epoch 51/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0522 - val_mean_squared_error: 0.0522\n",
"Epoch 52/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0268 - mean_squared_error: 0.0268 - val_loss: 0.0469 - val_mean_squared_error: 0.0469\n",
"Epoch 53/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0478 - val_mean_squared_error: 0.0478\n",
"Epoch 54/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0262 - mean_squared_error: 0.0262 - val_loss: 0.0552 - val_mean_squared_error: 0.0552\n",
"Epoch 55/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 56/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0251 - mean_squared_error: 0.0251 - val_loss: 0.0535 - val_mean_squared_error: 0.0535\n",
"Epoch 57/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0259 - mean_squared_error: 0.0259 - val_loss: 0.0491 - val_mean_squared_error: 0.0491\n",
"Epoch 58/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 59/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0259 - mean_squared_error: 0.0259 - val_loss: 0.0519 - val_mean_squared_error: 0.0519\n",
"Epoch 60/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0249 - mean_squared_error: 0.0249 - val_loss: 0.0517 - val_mean_squared_error: 0.0517\n",
"Epoch 61/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0527 - val_mean_squared_error: 0.0527\n",
"Epoch 62/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0240 - mean_squared_error: 0.0240 - val_loss: 0.0524 - val_mean_squared_error: 0.0524\n",
"Epoch 63/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0520 - val_mean_squared_error: 0.0520\n",
"Epoch 64/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0245 - mean_squared_error: 0.0245 - val_loss: 0.0488 - val_mean_squared_error: 0.0488\n",
"Epoch 65/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0493 - val_mean_squared_error: 0.0493\n",
"Epoch 66/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0235 - mean_squared_error: 0.0235 - val_loss: 0.0546 - val_mean_squared_error: 0.0546\n",
"Epoch 67/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0503 - val_mean_squared_error: 0.0503\n",
"Epoch 68/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0496 - val_mean_squared_error: 0.0496\n",
"Epoch 69/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0231 - mean_squared_error: 0.0231 - val_loss: 0.0537 - val_mean_squared_error: 0.0537\n",
"Epoch 70/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0220 - mean_squared_error: 0.0220 - val_loss: 0.0521 - val_mean_squared_error: 0.0521\n",
"Epoch 71/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0223 - mean_squared_error: 0.0223 - val_loss: 0.0548 - val_mean_squared_error: 0.0548\n",
"Epoch 72/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0218 - mean_squared_error: 0.0218 - val_loss: 0.0485 - val_mean_squared_error: 0.0485\n",
"Epoch 73/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0228 - mean_squared_error: 0.0228 - val_loss: 0.0566 - val_mean_squared_error: 0.0566\n",
"Epoch 74/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
"Epoch 75/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0212 - mean_squared_error: 0.0212 - val_loss: 0.0537 - val_mean_squared_error: 0.0537\n",
"Epoch 76/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0216 - mean_squared_error: 0.0216 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
"Epoch 77/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0215 - mean_squared_error: 0.0215 - val_loss: 0.0509 - val_mean_squared_error: 0.0509\n",
"Epoch 78/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0212 - mean_squared_error: 0.0212 - val_loss: 0.0545 - val_mean_squared_error: 0.0545\n",
"Epoch 79/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0204 - mean_squared_error: 0.0204 - val_loss: 0.0643 - val_mean_squared_error: 0.0643\n",
"Epoch 80/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0210 - mean_squared_error: 0.0210 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
"Epoch 81/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0204 - mean_squared_error: 0.0204 - val_loss: 0.0498 - val_mean_squared_error: 0.0498\n",
"Epoch 82/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0203 - mean_squared_error: 0.0203 - val_loss: 0.0540 - val_mean_squared_error: 0.0540\n",
"Epoch 83/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0205 - mean_squared_error: 0.0205 - val_loss: 0.0566 - val_mean_squared_error: 0.0566\n",
"Epoch 84/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0213 - mean_squared_error: 0.0213 - val_loss: 0.0531 - val_mean_squared_error: 0.0531\n",
"Epoch 85/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0554 - val_mean_squared_error: 0.0554\n",
"Epoch 86/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0545 - val_mean_squared_error: 0.0545\n",
"Epoch 87/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0194 - mean_squared_error: 0.0194 - val_loss: 0.0567 - val_mean_squared_error: 0.0567\n",
"Epoch 88/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0628 - val_mean_squared_error: 0.0628\n",
"Epoch 89/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0195 - mean_squared_error: 0.0195 - val_loss: 0.0516 - val_mean_squared_error: 0.0516\n",
"Epoch 90/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0195 - mean_squared_error: 0.0195 - val_loss: 0.0518 - val_mean_squared_error: 0.0518\n",
"Epoch 91/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0186 - mean_squared_error: 0.0186 - val_loss: 0.0558 - val_mean_squared_error: 0.0558\n",
"Epoch 92/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0188 - mean_squared_error: 0.0188 - val_loss: 0.0528 - val_mean_squared_error: 0.0528\n",
"Epoch 93/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0182 - mean_squared_error: 0.0182 - val_loss: 0.0543 - val_mean_squared_error: 0.0543\n",
"Epoch 94/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0190 - mean_squared_error: 0.0190 - val_loss: 0.0586 - val_mean_squared_error: 0.0586\n",
"Epoch 95/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0180 - mean_squared_error: 0.0180 - val_loss: 0.0545 - val_mean_squared_error: 0.0545\n",
"Epoch 96/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0176 - mean_squared_error: 0.0176 - val_loss: 0.0543 - val_mean_squared_error: 0.0543\n",
"Epoch 97/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0190 - mean_squared_error: 0.0190 - val_loss: 0.0525 - val_mean_squared_error: 0.0525\n",
"Epoch 98/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0175 - mean_squared_error: 0.0175 - val_loss: 0.0570 - val_mean_squared_error: 0.0570\n",
"Epoch 99/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0181 - mean_squared_error: 0.0181 - val_loss: 0.0564 - val_mean_squared_error: 0.0564\n",
"Epoch 100/100\n",
"\u001b[1m787/787\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.0186 - mean_squared_error: 0.0186 - val_loss: 0.0598 - val_mean_squared_error: 0.0598\n",
"\u001b[1m339/339\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 894us/step\n",
"CPU times: total: 47min 23s\n",
"Wall time: 18min 50s\n"
]
}
],
"source": [
"%%time\n",
"best_metric = 1000\n",
"for i in range(50, 151, 100):\n",
" for j in range(20, 51, 30):\n",
" for k in range(5, 11, 5):\n",
" \n",
" units_4 = [i, j, k, 1]\n",
" input_dim = train_features.shape[1]\n",
" \n",
" dense_model = keras.models.Sequential()\n",
" dense_model.add(keras.layers.Dense(units=units_4[0], input_dim=input_dim, activation='relu'))\n",
" dense_model.add(Dense(units=units_4[1], input_dim=units_4[0], activation='relu'))\n",
" dense_model.add(Dense(units=units_4[2], input_dim=units_4[1], activation='relu'))\n",
" dense_model.add(Dense(units=units_4[3], input_dim=units_4[2], activation='sigmoid'))\n",
"\n",
" dense_model.compile(loss='mean_squared_error', optimizer='Adam', metrics=['mean_squared_error'])\n",
" \n",
" dense_model.fit(train_features, y_train, epochs=100,validation_data=(valid_features, y_valid))\n",
" \n",
" metric = mean_squared_error(y_valid, dense_model.predict(valid_features))\n",
" \n",
" if metric < best_metric:\n",
" best_metric = metric\n",
" best_units = [i, j, k]\n",
" best_dense_model = dense_model\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "0ea7d467",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.05237418097211279\n",
"[150, 50, 5]\n"
]
}
],
"source": [
"print(best_metric)\n",
"print(best_units)"
]
},
{
"cell_type": "markdown",
"id": "1a77ef94",
"metadata": {},
"source": [
"Обе модели показали низкое значение метрики. Тестирование проведем на обеих моделях."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34868bbb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "8f73a132",
"metadata": {},
"source": [
"## Тестирование модели\n",
"\n",
"Настало время протестировать модель. Для этого получите эмбеддинги для всех тестовых изображений из папки `test_images`, выберите случайные 10 запросов из файла `test_queries.csv` и для каждого запроса выведите наиболее релевантное изображение. Сравните визуально качество поиска."
]
},
{
"cell_type": "markdown",
"id": "25afb802",
"metadata": {},
"source": [
"Загрузим данные с именами изображений в переменную *test_images_df*."
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "7e56efa7",
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" </tr>\n",
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"text/plain": [
" file_name\n",
"0 3356748019_2251399314.jpg\n",
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"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"test_images_df = pd.read_csv(path + 'test_images.csv')\n",
"test_images_df.columns = ['file_name']\n",
"test_images_df.head()"
]
},
{
"cell_type": "markdown",
"id": "bc75d985",
"metadata": {},
"source": [
"Векторизуем тестовые изображения."
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "7a17452c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 6.33 s\n",
"Wall time: 6.53 s\n"
]
}
],
"source": [
"%%time\n",
"test_images_df = df_img_vectorizer(test_images_df, 'test_images')"
]
},
{
"cell_type": "markdown",
"id": "581a2899",
"metadata": {},
"source": [
"Загрузим данные с запросами в переменную *test_images_df*."
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "d4e39e45",
"metadata": {},
"outputs": [
{
"data": {
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"</div>"
],
"text/plain": [
" file_name \\\n",
"0 3356748019_2251399314.jpg \n",
"1 2887171449_f54a2b9f39.jpg \n",
"2 3089107423_81a24eaf18.jpg \n",
"3 1429546659_44cb09cbe2.jpg \n",
"4 1177994172_10d143cb8d.jpg \n",
"\n",
" img_vector \n",
"0 [0.66924864, 0.004511619, 0.23613332, 0.882862... \n",
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"4 [0.44396326, 2.334486, 0.0063389367, 2.394353,... "
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_images_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "12712742",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"<div>\n",
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" <th></th>\n",
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" <th>query_id</th>\n",
" <th>query_text</th>\n",
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" <td>Two boys are squirting water guns at each other .</td>\n",
" <td>1177994172_10d143cb8d.jpg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1177994172_10d143cb8d.jpg#2</td>\n",
" <td>Two boys spraying each other with water</td>\n",
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" </tr>\n",
" <tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" index query_id \\\n",
"0 0 1177994172_10d143cb8d.jpg#0 \n",
"1 1 1177994172_10d143cb8d.jpg#1 \n",
"2 2 1177994172_10d143cb8d.jpg#2 \n",
"3 3 1177994172_10d143cb8d.jpg#3 \n",
"4 4 1177994172_10d143cb8d.jpg#4 \n",
"\n",
" query_text \\\n",
"0 Two blonde boys , one in a camouflage shirt an... \n",
"1 Two boys are squirting water guns at each other . \n",
"2 Two boys spraying each other with water \n",
"3 Two children wearing jeans squirt water at eac... \n",
"4 Two young boys are squirting water at each oth... \n",
"\n",
" file_name \n",
"0 1177994172_10d143cb8d.jpg \n",
"1 1177994172_10d143cb8d.jpg \n",
"2 1177994172_10d143cb8d.jpg \n",
"3 1177994172_10d143cb8d.jpg \n",
"4 1177994172_10d143cb8d.jpg "
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_queries_df = pd.read_csv(path + 'test_queries.csv', sep='|')\n",
"test_queries_df.columns=['index', 'query_id', 'query_text', 'file_name']\n",
"test_queries_df.head()"
]
},
{
"cell_type": "markdown",
"id": "6db738e1",
"metadata": {},
"source": [
"Лемматизируем тексты запросов."
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "c759c2e6",
"metadata": {},
"outputs": [],
"source": [
"test_df = test_images_df"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "c76899b7",
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
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" </tr>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name \\\n",
"0 3356748019_2251399314.jpg \n",
"1 2887171449_f54a2b9f39.jpg \n",
"2 3089107423_81a24eaf18.jpg \n",
"3 1429546659_44cb09cbe2.jpg \n",
"4 1177994172_10d143cb8d.jpg \n",
"\n",
" img_vector \n",
"0 [0.66924864, 0.004511619, 0.23613332, 0.882862... \n",
"1 [0.94964164, 3.2524443, 0.69842505, 1.2815218,... \n",
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"4 [0.44396326, 2.334486, 0.0063389367, 2.394353,... "
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df.head()"
]
},
{
"cell_type": "markdown",
"id": "a4b34971",
"metadata": {},
"source": [
"**Тестирование.**\n",
"\n",
"Для тестирования напишем функцию, которая принимает на вход текстовое описание, делает его векторизацию и возвращает картинку с максимальным значением метрики. Если запрос ведёт на юридически вредный контент, функция выводит дисклеймер. Отдельно сделаем функцию для нейронной сети и отдельно для линейной регрессии."
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "84338678",
"metadata": {},
"outputs": [],
"source": [
"def eval_query_nn(query_text):\n",
" # лемматизируем текст запроса\n",
" lemm_text = \" \".join([token.lemma_ for token in nlp(query_text)])\n",
" # создаем маркер наличия стоп-слова\n",
" stop = 0\n",
" # проверяем на наличие стоп-слов в запросе\n",
" for word in stop_words:\n",
" if word in lemm_text:\n",
" stop = 1 \n",
" break \n",
" \n",
" if stop == 0:\n",
" # лемматизируем текст \n",
" test_df['lem_query_text'] = lemm_text\n",
" # создаем массив признаков\n",
" test_features = make_features(test_df)\n",
" # получаем предсказания\n",
" test_preds = best_dense_model.predict(test_features)\n",
" # находим индекс иозображения с наибольшей оценкой\n",
" img_index = np.argsort(test_preds.ravel())[::-1][0]\n",
" # выводим изображение на экран\n",
" display(Image.open(path + 'test_images/' + test_images_df.loc[img_index, \n",
" 'file_name']).convert('RGB'))\n",
" # выодим текст запроса на экран\n",
" print(query_text)\n",
" # выводим оценку, которую поставила модель, на экран\n",
" print(round(test_preds.ravel()[img_index], 3))\n",
" \n",
" else:\n",
" # если в запросе есть стоп-слова - выводим дисклеймер\n",
" result = print('This image is unavailable in your country in compliance with local laws.') "
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "caefbe9d",
"metadata": {},
"outputs": [],
"source": [
"def eval_query_lin_reg(query_text):\n",
" # лемматизируем текст запроса\n",
" lemm_text = \" \".join([token.lemma_ for token in nlp(query_text)])\n",
" # создаем маркер наличия стоп-слова\n",
" stop = 0\n",
" # проверяем на наличие стоп-слов в запросе\n",
" for word in stop_words:\n",
" if word in lemm_text:\n",
" stop = 1 \n",
" break \n",
" \n",
" if stop == 0:\n",
" # лемматизируем текст \n",
" test_df['lem_query_text'] = lemm_text\n",
" # создаем массив признаков\n",
" test_features = make_features(test_df)\n",
" # получаем предсказания\n",
" test_preds = lin_reg.predict(test_features)\n",
" # находим индекс иозображения с наибольшей оценкой\n",
" img_index = np.argsort(test_preds.ravel())[::-1][0]\n",
" # выводим изображение на экран\n",
" display(Image.open(path + 'test_images/' + test_images_df.loc[img_index, \n",
" 'file_name']).convert('RGB'))\n",
" # выодим текст запроса на экран\n",
" print(query_text)\n",
" # выводим оценку, которую поставила модель, на экран\n",
" print(round(test_preds.ravel()[img_index], 3))\n",
" \n",
" else:\n",
" # если в запросе есть стоп-слова - выводим дисклеймер\n",
" result = print('This image is unavailable in your country in compliance with local laws.') "
]
},
{
"cell_type": "markdown",
"id": "12ad424f",
"metadata": {},
"source": [
"Напечатаем несколько примеров предсказания картинки по тексту."
]
},
{
"cell_type": "markdown",
"id": "62a6a1dc",
"metadata": {},
"source": [
"**1. Нейронная сеть.**"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "3a0ad08a",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
]
},
{
"data": {
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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=500x387 at 0x1F9B8A30970>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Three women are dressed in costumes while one holds an umbrella .\n",
"0.096\n",
"None\n",
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
]
},
{
"data": {
"image/png": 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