Covid_Third_Wave_Forecasting/covid-19-prediction-using-neural-network.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/shiv28/covid-19-prediction-using-neural-network?scriptVersionId=89158432\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"markdown","id":"6d8ea2fa","metadata":{"id":"-suDX06ljokk","papermill":{"duration":0.065789,"end_time":"2022-03-03T03:20:01.46573","exception":false,"start_time":"2022-03-03T03:20:01.399941","status":"completed"},"tags":[]},"source":["## **Covid-19 prediction using Neural Network**\n"]},{"cell_type":"markdown","id":"5c0464e3","metadata":{"id":"mAjC5-lejoko","papermill":{"duration":0.06249,"end_time":"2022-03-03T03:20:01.597575","exception":false,"start_time":"2022-03-03T03:20:01.535085","status":"completed"},"tags":[]},"source":["## Abstract\n"]},{"cell_type":"markdown","id":"fb9abdef","metadata":{"id":"KMkVjv_Djokp","papermill":{"duration":0.06299,"end_time":"2022-03-03T03:20:01.725008","exception":false,"start_time":"2022-03-03T03:20:01.662018","status":"completed"},"tags":[]},"source":["This notebook is devoted to predicting the COVID-19 spread dynamics in the world using neural networks of different structures. We will see how to make predictions based on linear regression, back propagation  and long short-term memory neural networks\n"]},{"cell_type":"markdown","id":"16d3f47d","metadata":{"id":"D7KovMRhjokq","papermill":{"duration":0.06409,"end_time":"2022-03-03T03:20:01.854833","exception":false,"start_time":"2022-03-03T03:20:01.790743","status":"completed"},"tags":[]},"source":["## Introduction\n"]},{"cell_type":"markdown","id":"de2f1a7a","metadata":{"id":"Ck5QBtWtjokr","papermill":{"duration":0.06493,"end_time":"2022-03-03T03:20:01.98636","exception":false,"start_time":"2022-03-03T03:20:01.92143","status":"completed"},"tags":[]},"source":["Today there is a lot of open data about the spread of COVID-19 in the world. However, few tools are presented to predict  these processes.\n","\n","3 different mathematical models for the forecast calculation will taken.\n","\n","To do this, the division of the DataSet into training and test sets will be demonstrated, normalize data and make preliminary analysis.\n","\n","It will be shown how to build models and DataSets for using 2 different neural networks. \n","\n","The next step is to compare the accuracy and adequacy of the obtained models.\n"]},{"cell_type":"markdown","id":"77e4ae63","metadata":{"id":"ZtyRMAbsjokt","papermill":{"duration":0.061837,"end_time":"2022-03-03T03:20:02.111368","exception":false,"start_time":"2022-03-03T03:20:02.049531","status":"completed"},"tags":[]},"source":["## Steps or Stages\n"]},{"cell_type":"markdown","id":"b9eba145","metadata":{"id":"Sy4ehDlWjokv","papermill":{"duration":0.064005,"end_time":"2022-03-03T03:20:02.23843","exception":false,"start_time":"2022-03-03T03:20:02.174425","status":"completed"},"tags":[]},"source":["It consists of four stages:\n","\n","*   Download and preliminary analysis of data\n","*   Linear Regression\n","*   Back Propagation NN\n","*   Long Short-Term Memory - LSTM\n","\n"]},{"cell_type":"markdown","id":"67183c5f","metadata":{"papermill":{"duration":0.063495,"end_time":"2022-03-03T03:20:02.364136","exception":false,"start_time":"2022-03-03T03:20:02.300641","status":"completed"},"tags":[]},"source":["In first stage we pre-prepare data for analysis:\n","\n","*   change the data types of columns\n","*   rows filtering\n","*   elimination of missing data\n","*   DataSet transformation\n","*   data normalization\n","\n","During the next steps, we will demonstrate 3 different models .\n"]},{"cell_type":"markdown","id":"d59eeba9","metadata":{"id":"sVcOsbf9jokz","papermill":{"duration":0.064872,"end_time":"2022-03-03T03:20:02.493454","exception":false,"start_time":"2022-03-03T03:20:02.428582","status":"completed"},"tags":[]},"source":["## Prerequisites\n","\n","*   Python\n","*   Pandas\n","*   Statistics\n","*   NumPy\n","*   Matplotlib\n","*   Keras\n","*   Scikit-Learn\n"]},{"cell_type":"markdown","id":"38a30e7d","metadata":{"id":"p6vZQyCYjok0","papermill":{"duration":0.064901,"end_time":"2022-03-03T03:20:02.623136","exception":false,"start_time":"2022-03-03T03:20:02.558235","status":"completed"},"tags":[]},"source":["## Objectives\n"]},{"cell_type":"markdown","id":"d33b0bb5","metadata":{"id":"_MGHCRMGjok5","papermill":{"duration":0.064412,"end_time":"2022-03-03T03:20:02.754522","exception":false,"start_time":"2022-03-03T03:20:02.69011","status":"completed"},"tags":[]},"source":["## Required libraries Importing\n"]},{"cell_type":"markdown","id":"c1b4e5a2","metadata":{"id":"0EZW0Xaljok-","papermill":{"duration":0.066415,"end_time":"2022-03-03T03:20:02.888138","exception":false,"start_time":"2022-03-03T03:20:02.821723","status":"completed"},"tags":[]},"source":["Some libraries should be imported .\n"]},{"cell_type":"code","execution_count":1,"id":"11bb2ae0","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:03.028337Z","iopub.status.busy":"2022-03-03T03:20:03.027225Z","iopub.status.idle":"2022-03-03T03:20:03.033994Z","shell.execute_reply":"2022-03-03T03:20:03.034706Z","shell.execute_reply.started":"2022-03-03T03:13:34.385718Z"},"id":"bQjQjPofjok_","papermill":{"duration":0.081585,"end_time":"2022-03-03T03:20:03.035101","exception":false,"start_time":"2022-03-03T03:20:02.953516","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n"]},{"cell_type":"code","execution_count":2,"id":"61cd9508","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:03.169298Z","iopub.status.busy":"2022-03-03T03:20:03.168536Z","iopub.status.idle":"2022-03-03T03:20:05.10608Z","shell.execute_reply":"2022-03-03T03:20:05.106656Z","shell.execute_reply.started":"2022-03-03T03:13:34.391865Z"},"papermill":{"duration":2.008564,"end_time":"2022-03-03T03:20:05.106871","exception":false,"start_time":"2022-03-03T03:20:03.098307","status":"completed"},"tags":[]},"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>iso_code</th>\n","      <th>continent</th>\n","      <th>location</th>\n","      <th>date</th>\n","      <th>total_cases</th>\n","      <th>new_cases</th>\n","      <th>new_cases_smoothed</th>\n","      <th>total_deaths</th>\n","      <th>new_deaths</th>\n","      <th>new_deaths_smoothed</th>\n","      <th>...</th>\n","      <th>female_smokers</th>\n","      <th>male_smokers</th>\n","      <th>handwashing_facilities</th>\n","      <th>hospital_beds_per_thousand</th>\n","      <th>life_expectancy</th>\n","      <th>human_development_index</th>\n","      <th>excess_mortality_cumulative_absolute</th>\n","      <th>excess_mortality_cumulative</th>\n","      <th>excess_mortality</th>\n","      <th>excess_mortality_cumulative_per_million</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>2020-02-24</td>\n","      <td>5.0</td>\n","      <td>5.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>37.746</td>\n","      <td>0.5</td>\n","      <td>64.83</td>\n","      <td>0.511</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>2020-02-25</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>37.746</td>\n","      <td>0.5</td>\n","      <td>64.83</td>\n","      <td>0.511</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>2020-02-26</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>37.746</td>\n","      <td>0.5</td>\n","      <td>64.83</td>\n","      <td>0.511</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>2020-02-27</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>37.746</td>\n","      <td>0.5</td>\n","      <td>64.83</td>\n","      <td>0.511</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>2020-02-28</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>37.746</td>\n","      <td>0.5</td>\n","      <td>64.83</td>\n","      <td>0.511</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>165403</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>2022-02-25</td>\n","      <td>235467.0</td>\n","      <td>500.0</td>\n","      <td>348.143</td>\n","      <td>5392.0</td>\n","      <td>2.0</td>\n","      <td>1.000</td>\n","      <td>...</td>\n","      <td>1.6</td>\n","      <td>30.7</td>\n","      <td>36.791</td>\n","      <td>1.7</td>\n","      <td>61.49</td>\n","      <td>0.571</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>165404</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>2022-02-26</td>\n","      <td>235803.0</td>\n","      <td>336.0</td>\n","      <td>368.429</td>\n","      <td>5393.0</td>\n","      <td>1.0</td>\n","      <td>1.000</td>\n","      <td>...</td>\n","      <td>1.6</td>\n","      <td>30.7</td>\n","      <td>36.791</td>\n","      <td>1.7</td>\n","      <td>61.49</td>\n","      <td>0.571</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>165405</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>2022-02-27</td>\n","      <td>235803.0</td>\n","      <td>0.0</td>\n","      <td>350.143</td>\n","      <td>5393.0</td>\n","      <td>0.0</td>\n","      <td>1.000</td>\n","      <td>...</td>\n","      <td>1.6</td>\n","      <td>30.7</td>\n","      <td>36.791</td>\n","      <td>1.7</td>\n","      <td>61.49</td>\n","      <td>0.571</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>165406</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>2022-02-28</td>\n","      <td>236380.0</td>\n","      <td>577.0</td>\n","      <td>401.286</td>\n","      <td>5395.0</td>\n","      <td>2.0</td>\n","      <td>1.286</td>\n","      <td>...</td>\n","      <td>1.6</td>\n","      <td>30.7</td>\n","      <td>36.791</td>\n","      <td>1.7</td>\n","      <td>61.49</td>\n","      <td>0.571</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>165407</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>2022-03-01</td>\n","      <td>236871.0</td>\n","      <td>491.0</td>\n","      <td>413.000</td>\n","      <td>5395.0</td>\n","      <td>0.0</td>\n","      <td>1.000</td>\n","      <td>...</td>\n","      <td>1.6</td>\n","      <td>30.7</td>\n","      <td>36.791</td>\n","      <td>1.7</td>\n","      <td>61.49</td>\n","      <td>0.571</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>165408 rows × 67 columns</p>\n","</div>"],"text/plain":["       iso_code continent     location        date  total_cases  new_cases  \\\n","0           AFG      Asia  Afghanistan  2020-02-24          5.0        5.0   \n","1           AFG      Asia  Afghanistan  2020-02-25          5.0        0.0   \n","2           AFG      Asia  Afghanistan  2020-02-26          5.0        0.0   \n","3           AFG      Asia  Afghanistan  2020-02-27          5.0        0.0   \n","4           AFG      Asia  Afghanistan  2020-02-28          5.0        0.0   \n","...         ...       ...          ...         ...          ...        ...   \n","165403      ZWE    Africa     Zimbabwe  2022-02-25     235467.0      500.0   \n","165404      ZWE    Africa     Zimbabwe  2022-02-26     235803.0      336.0   \n","165405      ZWE    Africa     Zimbabwe  2022-02-27     235803.0        0.0   \n","165406      ZWE    Africa     Zimbabwe  2022-02-28     236380.0      577.0   \n","165407      ZWE    Africa     Zimbabwe  2022-03-01     236871.0      491.0   \n","\n","        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n","0                      NaN           NaN         NaN                  NaN   \n","1                      NaN           NaN         NaN                  NaN   \n","2                      NaN           NaN         NaN                  NaN   \n","3                      NaN           NaN         NaN                  NaN   \n","4                      NaN           NaN         NaN                  NaN   \n","...                    ...           ...         ...                  ...   \n","165403             348.143        5392.0         2.0                1.000   \n","165404             368.429        5393.0         1.0                1.000   \n","165405             350.143        5393.0         0.0                1.000   \n","165406             401.286        5395.0         2.0                1.286   \n","165407             413.000        5395.0         0.0                1.000   \n","\n","        ...  female_smokers  male_smokers  handwashing_facilities  \\\n","0       ...             NaN           NaN                  37.746   \n","1       ...             NaN           NaN                  37.746   \n","2       ...             NaN           NaN                  37.746   \n","3       ...             NaN           NaN                  37.746   \n","4       ...             NaN           NaN                  37.746   \n","...     ...             ...           ...                     ...   \n","165403  ...             1.6          30.7                  36.791   \n","165404  ...             1.6          30.7                  36.791   \n","165405  ...             1.6          30.7                  36.791   \n","165406  ...             1.6          30.7                  36.791   \n","165407  ...             1.6          30.7                  36.791   \n","\n","        hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n","0                              0.5            64.83                    0.511   \n","1                              0.5            64.83                    0.511   \n","2                              0.5            64.83                    0.511   \n","3                              0.5            64.83                    0.511   \n","4                              0.5            64.83                    0.511   \n","...                            ...              ...                      ...   \n","165403                         1.7            61.49                    0.571   \n","165404                         1.7            61.49                    0.571   \n","165405                         1.7            61.49                    0.571   \n","165406                         1.7            61.49                    0.571   \n","165407                         1.7            61.49                    0.571   \n","\n","        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n","0                                        NaN                          NaN   \n","1                                        NaN                          NaN   \n","2                                        NaN                          NaN   \n","3                                        NaN                          NaN   \n","4                                        NaN                          NaN   \n","...                                      ...                          ...   \n","165403                                   NaN                          NaN   \n","165404                                   NaN                          NaN   \n","165405                                   NaN                          NaN   \n","165406                                   NaN                          NaN   \n","165407                                   NaN                          NaN   \n","\n","        excess_mortality  excess_mortality_cumulative_per_million  \n","0                    NaN                                      NaN  \n","1                    NaN                                      NaN  \n","2                    NaN                                      NaN  \n","3                    NaN                                      NaN  \n","4                    NaN                                      NaN  \n","...                  ...                                      ...  \n","165403               NaN                                      NaN  \n","165404               NaN                                      NaN  \n","165405               NaN                                      NaN  \n","165406               NaN                                      NaN  \n","165407               NaN                                      NaN  \n","\n","[165408 rows x 67 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["covid_word = pd.read_csv('../input/covidowid/owid-covid-data (1).csv')\n","covid_word"]},{"cell_type":"markdown","id":"edbb5147","metadata":{"id":"C3AT_2fsjolB","papermill":{"duration":0.066331,"end_time":"2022-03-03T03:20:05.236301","exception":false,"start_time":"2022-03-03T03:20:05.16997","status":"completed"},"tags":[]},"source":["Let's study our DataSet. As you can see, it consist of 159321 rows × 67 columns. There is some missing data are observed in the DataSet. We should be sure that Python recognized types of data correctly.\n"]},{"cell_type":"code","execution_count":3,"id":"0d20f788","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:05.370885Z","iopub.status.busy":"2022-03-03T03:20:05.370124Z","iopub.status.idle":"2022-03-03T03:20:05.503262Z","shell.execute_reply":"2022-03-03T03:20:05.503878Z","shell.execute_reply.started":"2022-03-03T03:13:35.561903Z"},"id":"O2M9TmepjolC","papermill":{"duration":0.203592,"end_time":"2022-03-03T03:20:05.504086","exception":false,"start_time":"2022-03-03T03:20:05.300494","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 165408 entries, 0 to 165407\n","Data columns (total 67 columns):\n"," #   Column                                      Non-Null Count   Dtype  \n","---  ------                                      --------------   -----  \n"," 0   iso_code                                    165408 non-null  object \n"," 1   continent                                   155504 non-null  object \n"," 2   location                                    165408 non-null  object \n"," 3   date                                        165408 non-null  object \n"," 4   total_cases                                 162378 non-null  float64\n"," 5   new_cases                                   162230 non-null  float64\n"," 6   new_cases_smoothed                          160247 non-null  float64\n"," 7   total_deaths                                144574 non-null  float64\n"," 8   new_deaths                                  144611 non-null  float64\n"," 9   new_deaths_smoothed                         142515 non-null  float64\n"," 10  total_cases_per_million                     161624 non-null  float64\n"," 11  new_cases_per_million                       161476 non-null  float64\n"," 12  new_cases_smoothed_per_million              159499 non-null  float64\n"," 13  total_deaths_per_million                    143833 non-null  float64\n"," 14  new_deaths_per_million                      143870 non-null  float64\n"," 15  new_deaths_smoothed_per_million             141780 non-null  float64\n"," 16  reproduction_rate                           125067 non-null  float64\n"," 17  icu_patients                                23307 non-null   float64\n"," 18  icu_patients_per_million                    23307 non-null   float64\n"," 19  hosp_patients                               24450 non-null   float64\n"," 20  hosp_patients_per_million                   24450 non-null   float64\n"," 21  weekly_icu_admissions                       5394 non-null    float64\n"," 22  weekly_icu_admissions_per_million           5394 non-null    float64\n"," 23  weekly_hosp_admissions                      10858 non-null   float64\n"," 24  weekly_hosp_admissions_per_million          10858 non-null   float64\n"," 25  new_tests                                   67006 non-null   float64\n"," 26  total_tests                                 68944 non-null   float64\n"," 27  total_tests_per_thousand                    68944 non-null   float64\n"," 28  new_tests_per_thousand                      67006 non-null   float64\n"," 29  new_tests_smoothed                          83658 non-null   float64\n"," 30  new_tests_smoothed_per_thousand             83658 non-null   float64\n"," 31  positive_rate                               78590 non-null   float64\n"," 32  tests_per_case                              78027 non-null   float64\n"," 33  tests_units                                 85981 non-null   object \n"," 34  total_vaccinations                          44858 non-null   float64\n"," 35  people_vaccinated                           42688 non-null   float64\n"," 36  people_fully_vaccinated                     40047 non-null   float64\n"," 37  total_boosters                              17241 non-null   float64\n"," 38  new_vaccinations                            37155 non-null   float64\n"," 39  new_vaccinations_smoothed                   83919 non-null   float64\n"," 40  total_vaccinations_per_hundred              44858 non-null   float64\n"," 41  people_vaccinated_per_hundred               42688 non-null   float64\n"," 42  people_fully_vaccinated_per_hundred         40047 non-null   float64\n"," 43  total_boosters_per_hundred                  17241 non-null   float64\n"," 44  new_vaccinations_smoothed_per_million       83919 non-null   float64\n"," 45  new_people_vaccinated_smoothed              82654 non-null   float64\n"," 46  new_people_vaccinated_smoothed_per_hundred  82654 non-null   float64\n"," 47  stringency_index                            129843 non-null  float64\n"," 48  population                                  164337 non-null  float64\n"," 49  population_density                          147109 non-null  float64\n"," 50  median_age                                  137068 non-null  float64\n"," 51  aged_65_older                               135582 non-null  float64\n"," 52  aged_70_older                               136333 non-null  float64\n"," 53  gdp_per_capita                              137737 non-null  float64\n"," 54  extreme_poverty                             90712 non-null   float64\n"," 55  cardiovasc_death_rate                       136019 non-null  float64\n"," 56  diabetes_prevalence                         143150 non-null  float64\n"," 57  female_smokers                              105463 non-null  float64\n"," 58  male_smokers                                104016 non-null  float64\n"," 59  handwashing_facilities                      68189 non-null   float64\n"," 60  hospital_beds_per_thousand                  122981 non-null  float64\n"," 61  life_expectancy                             154405 non-null  float64\n"," 62  human_development_index                     135494 non-null  float64\n"," 63  excess_mortality_cumulative_absolute        5696 non-null    float64\n"," 64  excess_mortality_cumulative                 5696 non-null    float64\n"," 65  excess_mortality                            5696 non-null    float64\n"," 66  excess_mortality_cumulative_per_million     5696 non-null    float64\n","dtypes: float64(62), object(5)\n","memory usage: 84.6+ MB\n"]}],"source":["covid_word.info()"]},{"cell_type":"markdown","id":"05986c5b","metadata":{"id":"rR_i-AuyjolC","papermill":{"duration":0.063775,"end_time":"2022-03-03T03:20:05.631474","exception":false,"start_time":"2022-03-03T03:20:05.567699","status":"completed"},"tags":[]},"source":["As you can see, 66 columns of COVID-19 data were recognized correctly (float64). First 4 columns and tests_units were recognized as objects. Let's investigate them:\n"]},{"cell_type":"code","execution_count":4,"id":"2114c895","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:05.763812Z","iopub.status.busy":"2022-03-03T03:20:05.762719Z","iopub.status.idle":"2022-03-03T03:20:05.784606Z","shell.execute_reply":"2022-03-03T03:20:05.785279Z","shell.execute_reply.started":"2022-03-03T03:13:35.632808Z"},"id":"l9qfqY2SjolD","papermill":{"duration":0.090769,"end_time":"2022-03-03T03:20:05.785528","exception":false,"start_time":"2022-03-03T03:20:05.694759","status":"completed"},"tags":[]},"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>iso_code</th>\n","      <th>continent</th>\n","      <th>location</th>\n","      <th>tests_units</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AFG</td>\n","      <td>Asia</td>\n","      <td>Afghanistan</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>165403</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>tests performed</td>\n","    </tr>\n","    <tr>\n","      <th>165404</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>tests performed</td>\n","    </tr>\n","    <tr>\n","      <th>165405</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>tests performed</td>\n","    </tr>\n","    <tr>\n","      <th>165406</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>165407</th>\n","      <td>ZWE</td>\n","      <td>Africa</td>\n","      <td>Zimbabwe</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>165408 rows × 4 columns</p>\n","</div>"],"text/plain":["       iso_code continent     location      tests_units\n","0           AFG      Asia  Afghanistan              NaN\n","1           AFG      Asia  Afghanistan              NaN\n","2           AFG      Asia  Afghanistan              NaN\n","3           AFG      Asia  Afghanistan              NaN\n","4           AFG      Asia  Afghanistan              NaN\n","...         ...       ...          ...              ...\n","165403      ZWE    Africa     Zimbabwe  tests performed\n","165404      ZWE    Africa     Zimbabwe  tests performed\n","165405      ZWE    Africa     Zimbabwe  tests performed\n","165406      ZWE    Africa     Zimbabwe              NaN\n","165407      ZWE    Africa     Zimbabwe              NaN\n","\n","[165408 rows x 4 columns]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["fields = ['iso_code', 'continent', 'location', 'tests_units']\n","covid_word[fields]"]},{"cell_type":"markdown","id":"38a08716","metadata":{"id":"7GI9FShzjolE","papermill":{"duration":0.063651,"end_time":"2022-03-03T03:20:05.91325","exception":false,"start_time":"2022-03-03T03:20:05.849599","status":"completed"},"tags":[]},"source":["Let's show information about the field date.\n"]},{"cell_type":"code","execution_count":5,"id":"80ab8815","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:06.049383Z","iopub.status.busy":"2022-03-03T03:20:06.043894Z","iopub.status.idle":"2022-03-03T03:20:06.052577Z","shell.execute_reply":"2022-03-03T03:20:06.053218Z","shell.execute_reply.started":"2022-03-03T03:13:35.651842Z"},"id":"-hYrN453jolF","papermill":{"duration":0.076205,"end_time":"2022-03-03T03:20:06.053461","exception":false,"start_time":"2022-03-03T03:20:05.977256","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0         2020-02-24\n","1         2020-02-25\n","2         2020-02-26\n","3         2020-02-27\n","4         2020-02-28\n","             ...    \n","165403    2022-02-25\n","165404    2022-02-26\n","165405    2022-02-27\n","165406    2022-02-28\n","165407    2022-03-01\n","Name: date, Length: 165408, dtype: object"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["covid_word['date']"]},{"cell_type":"markdown","id":"0bf925f5","metadata":{"id":"OCAkVRmJjolG","papermill":{"duration":0.064478,"end_time":"2022-03-03T03:20:06.182772","exception":false,"start_time":"2022-03-03T03:20:06.118294","status":"completed"},"tags":[]},"source":["### Сhange the data types of columns\n"]},{"cell_type":"markdown","id":"b370e3f7","metadata":{"id":"YOwOer5pjolH","papermill":{"duration":0.0646,"end_time":"2022-03-03T03:20:06.314124","exception":false,"start_time":"2022-03-03T03:20:06.249524","status":"completed"},"tags":[]},"source":["As you can see, the columns: 'iso_code', 'continent', 'location', 'tests_units' have many repetitions and should be assigned to categorical fields **pandas.astype()**. \n","\n","The field 'data' should be converted into DataTime type **pandas.to_datetime**  \n","To see the results we can use **pandas.describe()**.\n"]},{"cell_type":"code","execution_count":6,"id":"ed3cf126","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:06.447782Z","iopub.status.busy":"2022-03-03T03:20:06.446921Z","iopub.status.idle":"2022-03-03T03:20:06.621725Z","shell.execute_reply":"2022-03-03T03:20:06.621028Z","shell.execute_reply.started":"2022-03-03T03:13:35.666512Z"},"id":"W8IQFt5ajolJ","papermill":{"duration":0.242532,"end_time":"2022-03-03T03:20:06.621901","exception":false,"start_time":"2022-03-03T03:20:06.379369","status":"completed"},"tags":[]},"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>iso_code</th>\n","      <th>continent</th>\n","      <th>location</th>\n","      <th>tests_units</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>165408</td>\n","      <td>155504</td>\n","      <td>165408</td>\n","      <td>85981</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>238</td>\n","      <td>6</td>\n","      <td>238</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>ARG</td>\n","      <td>Africa</td>\n","      <td>Argentina</td>\n","      <td>tests performed</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>791</td>\n","      <td>39197</td>\n","      <td>791</td>\n","      <td>62269</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       iso_code continent   location      tests_units\n","count    165408    155504     165408            85981\n","unique      238         6        238                4\n","top         ARG    Africa  Argentina  tests performed\n","freq        791     39197        791            62269"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["fields = ['iso_code', 'continent', 'location', 'tests_units']\n","covid_word[fields] =covid_word[fields].astype('category')\n","covid_word.loc[:, 'date'] = pd.to_datetime(covid_word['date'])\n","covid_word[fields].describe()"]},{"cell_type":"markdown","id":"2b6169a5","metadata":{"id":"WmHQN9iGjolK","papermill":{"duration":0.064601,"end_time":"2022-03-03T03:20:06.751715","exception":false,"start_time":"2022-03-03T03:20:06.687114","status":"completed"},"tags":[]},"source":["### Rows filtering\n"]},{"cell_type":"markdown","id":"0ea61c8f","metadata":{"id":"uaPESo_8jolL","papermill":{"duration":0.064448,"end_time":"2022-03-03T03:20:06.882592","exception":false,"start_time":"2022-03-03T03:20:06.818144","status":"completed"},"tags":[]},"source":["As we can see, the DataSet contains information about 6 continents and 238 countries.\n","The field 'tests_units' consist of 4 categories. To show a list of countries, we can use **pandas.Series.cat.categories**.\n"]},{"cell_type":"code","execution_count":7,"id":"e10b32e3","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:07.016686Z","iopub.status.busy":"2022-03-03T03:20:07.015967Z","iopub.status.idle":"2022-03-03T03:20:07.02313Z","shell.execute_reply":"2022-03-03T03:20:07.022567Z","shell.execute_reply.started":"2022-03-03T03:13:35.784029Z"},"id":"-IgQLE9RjolL","papermill":{"duration":0.075501,"end_time":"2022-03-03T03:20:07.023297","exception":false,"start_time":"2022-03-03T03:20:06.947796","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Index(['Afghanistan', 'Africa', 'Albania', 'Algeria', 'Andorra', 'Angola',\n","       'Anguilla', 'Antigua and Barbuda', 'Argentina', 'Armenia',\n","       ...\n","       'Uzbekistan', 'Vanuatu', 'Vatican', 'Venezuela', 'Vietnam',\n","       'Wallis and Futuna', 'World', 'Yemen', 'Zambia', 'Zimbabwe'],\n","      dtype='object', length=238)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["covid_word['location'].cat.categories"]},{"cell_type":"markdown","id":"011c3f87","metadata":{"id":"80889yb9jolM","papermill":{"duration":0.064707,"end_time":"2022-03-03T03:20:07.153305","exception":false,"start_time":"2022-03-03T03:20:07.088598","status":"completed"},"tags":[]},"source":["Let's investigate the dynamics of new cases of COVID-19 for a separate country. We will use India for saving models and further forecast. You can use your country. Let's use a pandas filter to do this.\n"]},{"cell_type":"code","execution_count":8,"id":"6c7fe3c1","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:07.288178Z","iopub.status.busy":"2022-03-03T03:20:07.287498Z","iopub.status.idle":"2022-03-03T03:20:07.335942Z","shell.execute_reply":"2022-03-03T03:20:07.336524Z","shell.execute_reply.started":"2022-03-03T03:13:35.792632Z"},"id":"CTqsGuIfjolN","papermill":{"duration":0.118112,"end_time":"2022-03-03T03:20:07.336741","exception":false,"start_time":"2022-03-03T03:20:07.218629","status":"completed"},"tags":[]},"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>iso_code</th>\n","      <th>continent</th>\n","      <th>location</th>\n","      <th>date</th>\n","      <th>total_cases</th>\n","      <th>new_cases</th>\n","      <th>new_cases_smoothed</th>\n","      <th>total_deaths</th>\n","      <th>new_deaths</th>\n","      <th>new_deaths_smoothed</th>\n","      <th>...</th>\n","      <th>female_smokers</th>\n","      <th>male_smokers</th>\n","      <th>handwashing_facilities</th>\n","      <th>hospital_beds_per_thousand</th>\n","      <th>life_expectancy</th>\n","      <th>human_development_index</th>\n","      <th>excess_mortality_cumulative_absolute</th>\n","      <th>excess_mortality_cumulative</th>\n","      <th>excess_mortality</th>\n","      <th>excess_mortality_cumulative_per_million</th>\n","    </tr>\n","    <tr>\n","      <th>date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2020-01-30</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2020-01-30</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2020-01-31</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2020-01-31</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-01</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2020-02-01</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-02</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2020-02-02</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-03</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2020-02-03</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-25</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2022-02-25</td>\n","      <td>42905844.0</td>\n","      <td>11499.0</td>\n","      <td>14762.714</td>\n","      <td>513481.0</td>\n","      <td>255.0</td>\n","      <td>321.571</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-26</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2022-02-26</td>\n","      <td>42916117.0</td>\n","      <td>10273.0</td>\n","      <td>13377.714</td>\n","      <td>513724.0</td>\n","      <td>243.0</td>\n","      <td>260.143</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-27</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2022-02-27</td>\n","      <td>42924130.0</td>\n","      <td>8013.0</td>\n","      <td>12229.429</td>\n","      <td>513843.0</td>\n","      <td>119.0</td>\n","      <td>247.714</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-28</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2022-02-28</td>\n","      <td>42931045.0</td>\n","      <td>6915.0</td>\n","      <td>11302.286</td>\n","      <td>514023.0</td>\n","      <td>180.0</td>\n","      <td>239.857</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2022-03-01</th>\n","      <td>IND</td>\n","      <td>Asia</td>\n","      <td>India</td>\n","      <td>2022-03-01</td>\n","      <td>42938599.0</td>\n","      <td>7554.0</td>\n","      <td>10224.000</td>\n","      <td>514246.0</td>\n","      <td>223.0</td>\n","      <td>232.000</td>\n","      <td>...</td>\n","      <td>1.9</td>\n","      <td>20.6</td>\n","      <td>59.55</td>\n","      <td>0.53</td>\n","      <td>69.66</td>\n","      <td>0.645</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>762 rows × 67 columns</p>\n","</div>"],"text/plain":["           iso_code continent location       date  total_cases  new_cases  \\\n","date                                                                        \n","2020-01-30      IND      Asia    India 2020-01-30          1.0        1.0   \n","2020-01-31      IND      Asia    India 2020-01-31          1.0        0.0   \n","2020-02-01      IND      Asia    India 2020-02-01          1.0        0.0   \n","2020-02-02      IND      Asia    India 2020-02-02          2.0        1.0   \n","2020-02-03      IND      Asia    India 2020-02-03          3.0        1.0   \n","...             ...       ...      ...        ...          ...        ...   \n","2022-02-25      IND      Asia    India 2022-02-25   42905844.0    11499.0   \n","2022-02-26      IND      Asia    India 2022-02-26   42916117.0    10273.0   \n","2022-02-27      IND      Asia    India 2022-02-27   42924130.0     8013.0   \n","2022-02-28      IND      Asia    India 2022-02-28   42931045.0     6915.0   \n","2022-03-01      IND      Asia    India 2022-03-01   42938599.0     7554.0   \n","\n","            new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n","date                                                                            \n","2020-01-30                 NaN           NaN         NaN                  NaN   \n","2020-01-31                 NaN           NaN         NaN                  NaN   \n","2020-02-01                 NaN           NaN         NaN                  NaN   \n","2020-02-02                 NaN           NaN         NaN                  NaN   \n","2020-02-03                 NaN           NaN         NaN                  NaN   \n","...                        ...           ...         ...                  ...   \n","2022-02-25           14762.714      513481.0       255.0              321.571   \n","2022-02-26           13377.714      513724.0       243.0              260.143   \n","2022-02-27           12229.429      513843.0       119.0              247.714   \n","2022-02-28           11302.286      514023.0       180.0              239.857   \n","2022-03-01           10224.000      514246.0       223.0              232.000   \n","\n","            ...  female_smokers  male_smokers  handwashing_facilities  \\\n","date        ...                                                         \n","2020-01-30  ...             1.9          20.6                   59.55   \n","2020-01-31  ...             1.9          20.6                   59.55   \n","2020-02-01  ...             1.9          20.6                   59.55   \n","2020-02-02  ...             1.9          20.6                   59.55   \n","2020-02-03  ...             1.9          20.6                   59.55   \n","...         ...             ...           ...                     ...   \n","2022-02-25  ...             1.9          20.6                   59.55   \n","2022-02-26  ...             1.9          20.6                   59.55   \n","2022-02-27  ...             1.9          20.6                   59.55   \n","2022-02-28  ...             1.9          20.6                   59.55   \n","2022-03-01  ...             1.9          20.6                   59.55   \n","\n","            hospital_beds_per_thousand  life_expectancy  \\\n","date                                                      \n","2020-01-30                        0.53            69.66   \n","2020-01-31                        0.53            69.66   \n","2020-02-01                        0.53            69.66   \n","2020-02-02                        0.53            69.66   \n","2020-02-03                        0.53            69.66   \n","...                                ...              ...   \n","2022-02-25                        0.53            69.66   \n","2022-02-26                        0.53            69.66   \n","2022-02-27                        0.53            69.66   \n","2022-02-28                        0.53            69.66   \n","2022-03-01                        0.53            69.66   \n","\n","            human_development_index  excess_mortality_cumulative_absolute  \\\n","date                                                                        \n","2020-01-30                    0.645                                   NaN   \n","2020-01-31                    0.645                                   NaN   \n","2020-02-01                    0.645                                   NaN   \n","2020-02-02                    0.645                                   NaN   \n","2020-02-03                    0.645                                   NaN   \n","...                             ...                                   ...   \n","2022-02-25                    0.645                                   NaN   \n","2022-02-26                    0.645                                   NaN   \n","2022-02-27                    0.645                                   NaN   \n","2022-02-28                    0.645                                   NaN   \n","2022-03-01                    0.645                                   NaN   \n","\n","            excess_mortality_cumulative  excess_mortality  \\\n","date                                                        \n","2020-01-30                          NaN               NaN   \n","2020-01-31                          NaN               NaN   \n","2020-02-01                          NaN               NaN   \n","2020-02-02                          NaN               NaN   \n","2020-02-03                          NaN               NaN   \n","...                                 ...               ...   \n","2022-02-25                          NaN               NaN   \n","2022-02-26                          NaN               NaN   \n","2022-02-27                          NaN               NaN   \n","2022-02-28                          NaN               NaN   \n","2022-03-01                          NaN               NaN   \n","\n","            excess_mortality_cumulative_per_million  \n","date                                                 \n","2020-01-30                                      NaN  \n","2020-01-31                                      NaN  \n","2020-02-01                                      NaN  \n","2020-02-02                                      NaN  \n","2020-02-03                                      NaN  \n","...                                             ...  \n","2022-02-25                                      NaN  \n","2022-02-26                                      NaN  \n","2022-02-27                                      NaN  \n","2022-02-28                                      NaN  \n","2022-03-01                                      NaN  \n","\n","[762 rows x 67 columns]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["covid_word.index = covid_word['date']\n","c_covid = covid_word[covid_word['location'] == \"India\"]\n","c_covid"]},{"cell_type":"markdown","id":"a0ca4459","metadata":{"id":"ZTv5QgLzjolO","papermill":{"duration":0.067932,"end_time":"2022-03-03T03:20:07.472299","exception":false,"start_time":"2022-03-03T03:20:07.404367","status":"completed"},"tags":[]},"source":["Let's choose the fields 'new_cases', 'new_cases_smoothed' for forecasting. First of all, we should visualize this data.\n"]},{"cell_type":"code","execution_count":9,"id":"3eb8dbff","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:07.614671Z","iopub.status.busy":"2022-03-03T03:20:07.613882Z","iopub.status.idle":"2022-03-03T03:20:08.122885Z","shell.execute_reply":"2022-03-03T03:20:08.123456Z","shell.execute_reply.started":"2022-03-03T03:13:35.827712Z"},"id":"TGc-RyczjolO","papermill":{"duration":0.583818,"end_time":"2022-03-03T03:20:08.123702","exception":false,"start_time":"2022-03-03T03:20:07.539884","status":"completed"},"tags":[]},"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":["import matplotlib.pyplot as plt\n","fields = ['new_cases', 'new_cases_smoothed']\n","c_covid[fields].plot()\n","plt.show()"]},{"cell_type":"markdown","id":"8dacf47a","metadata":{"id":"orrIf1ImjolQ","papermill":{"duration":0.068683,"end_time":"2022-03-03T03:20:08.260092","exception":false,"start_time":"2022-03-03T03:20:08.191409","status":"completed"},"tags":[]},"source":["### Elimination of missing data\n"]},{"cell_type":"markdown","id":"504dfe6b","metadata":{"id":"OyOgB9-NjolR","papermill":{"duration":0.068061,"end_time":"2022-03-03T03:20:08.396058","exception":false,"start_time":"2022-03-03T03:20:08.327997","status":"completed"},"tags":[]},"source":["As you can see, there are large fluctuations in the new cases plot. Let's try to make a forecast for these waves.\n","First of all, we should delete missing data using **pandas.DataFrame.dropna()**\n"]},{"cell_type":"code","execution_count":10,"id":"b2d44c8d","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:08.541414Z","iopub.status.busy":"2022-03-03T03:20:08.540341Z","iopub.status.idle":"2022-03-03T03:20:08.559094Z","shell.execute_reply":"2022-03-03T03:20:08.558458Z","shell.execute_reply.started":"2022-03-03T03:13:36.046918Z"},"id":"9GwadSLqjolS","papermill":{"duration":0.094439,"end_time":"2022-03-03T03:20:08.55926","exception":false,"start_time":"2022-03-03T03:20:08.464821","status":"completed"},"tags":[]},"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>new_cases</th>\n","      <th>new_cases_smoothed</th>\n","    </tr>\n","    <tr>\n","      <th>date</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2020-02-05</th>\n","      <td>0.0</td>\n","      <td>0.429</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-06</th>\n","      <td>0.0</td>\n","      <td>0.286</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-07</th>\n","      <td>0.0</td>\n","      <td>0.286</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-08</th>\n","      <td>0.0</td>\n","      <td>0.286</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-09</th>\n","      <td>0.0</td>\n","      <td>0.143</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-25</th>\n","      <td>11499.0</td>\n","      <td>14762.714</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-26</th>\n","      <td>10273.0</td>\n","      <td>13377.714</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-27</th>\n","      <td>8013.0</td>\n","      <td>12229.429</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-28</th>\n","      <td>6915.0</td>\n","      <td>11302.286</td>\n","    </tr>\n","    <tr>\n","      <th>2022-03-01</th>\n","      <td>7554.0</td>\n","      <td>10224.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>756 rows × 2 columns</p>\n","</div>"],"text/plain":["            new_cases  new_cases_smoothed\n","date                                     \n","2020-02-05        0.0               0.429\n","2020-02-06        0.0               0.286\n","2020-02-07        0.0               0.286\n","2020-02-08        0.0               0.286\n","2020-02-09        0.0               0.143\n","...               ...                 ...\n","2022-02-25    11499.0           14762.714\n","2022-02-26    10273.0           13377.714\n","2022-02-27     8013.0           12229.429\n","2022-02-28     6915.0           11302.286\n","2022-03-01     7554.0           10224.000\n","\n","[756 rows x 2 columns]"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["c_covid = c_covid[fields].dropna()\n","c_covid"]},{"cell_type":"markdown","id":"3087d853","metadata":{"id":"0g93hJUJjolT","papermill":{"duration":0.067137,"end_time":"2022-03-03T03:20:08.694221","exception":false,"start_time":"2022-03-03T03:20:08.627084","status":"completed"},"tags":[]},"source":["### Data transformation\n"]},{"cell_type":"markdown","id":"98f262ac","metadata":{"id":"kXxia8tOjolT","papermill":{"duration":0.068151,"end_time":"2022-03-03T03:20:08.829938","exception":false,"start_time":"2022-03-03T03:20:08.761787","status":"completed"},"tags":[]},"source":[" To check for dependencies, it is necessary to perform a correlation analysis between them. This requires:\n","\n","1.  duplicating of data and moving it vertically down for a certain number of days (lag)\n","2.  deleting the missing data at the beginning and end **pandas.DataFrame.shift()**\n","3.  calculating the correlation coefficient between the obtained series.\n","\n","It is convenient to create a separate function for lag:\n"]},{"cell_type":"code","execution_count":11,"id":"abb02841","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:08.969784Z","iopub.status.busy":"2022-03-03T03:20:08.969042Z","iopub.status.idle":"2022-03-03T03:20:08.976892Z","shell.execute_reply":"2022-03-03T03:20:08.977447Z","shell.execute_reply.started":"2022-03-03T03:13:36.062729Z"},"id":"TsFjwyuLjolV","papermill":{"duration":0.080084,"end_time":"2022-03-03T03:20:08.977678","exception":false,"start_time":"2022-03-03T03:20:08.897594","status":"completed"},"tags":[]},"outputs":[],"source":["def lag_correlation_ts(y, x, lag):\n","    \"\"\"\n","   \n","    y: fixed\n","    x: shifted\n","    lag: lag for shifting\n","    r: DataFrame of lags correlation coefficients\n","    \"\"\"\n","    r = [0] * (lag + 1)\n","    y = y.copy()\n","    x = x.copy()\n","    y.name = \"y\"\n","    x.name = \"x\"\n","\n","    for i in range(0, lag + 1):\n","        ds = y.copy().to_frame()\n","        ds = ds.join(x.shift(i), how='outer')\n","        r[i] = ds.corr().values[0][1]\n","    r = pd.DataFrame(r)\n","    r.index.names = ['Lag']\n","    r.columns = ['Correlation']\n","    return r"]},{"cell_type":"markdown","id":"81226454","metadata":{"id":"zEWzS05xjolX","papermill":{"duration":0.068427,"end_time":"2022-03-03T03:20:09.114389","exception":false,"start_time":"2022-03-03T03:20:09.045962","status":"completed"},"tags":[]},"source":["Let's create a target DataSet.\n"]},{"cell_type":"code","execution_count":12,"id":"52f311d0","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:09.258232Z","iopub.status.busy":"2022-03-03T03:20:09.257504Z","iopub.status.idle":"2022-03-03T03:20:09.266453Z","shell.execute_reply":"2022-03-03T03:20:09.26707Z","shell.execute_reply.started":"2022-03-03T03:13:36.073379Z"},"id":"x0dTv-hAjolY","papermill":{"duration":0.082393,"end_time":"2022-03-03T03:20:09.267284","exception":false,"start_time":"2022-03-03T03:20:09.184891","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["date\n","2020-02-05        0.0\n","2020-02-06        0.0\n","2020-02-07        0.0\n","2020-02-08        0.0\n","2020-02-09        0.0\n","               ...   \n","2022-02-25    11499.0\n","2022-02-26    10273.0\n","2022-02-27     8013.0\n","2022-02-28     6915.0\n","2022-03-01     7554.0\n","Name: new_cases, Length: 756, dtype: float64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["y_dataset = c_covid['new_cases']\n","y_dataset"]},{"cell_type":"markdown","id":"9035aa1c","metadata":{"id":"LxE_B-hWjolY","papermill":{"duration":0.068337,"end_time":"2022-03-03T03:20:09.405017","exception":false,"start_time":"2022-03-03T03:20:09.33668","status":"completed"},"tags":[]},"source":["Let's test a 50-day lag.\n"]},{"cell_type":"code","execution_count":13,"id":"0fef1326","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:09.566606Z","iopub.status.busy":"2022-03-03T03:20:09.565615Z","iopub.status.idle":"2022-03-03T03:20:09.940137Z","shell.execute_reply":"2022-03-03T03:20:09.940737Z","shell.execute_reply.started":"2022-03-03T03:13:36.092297Z"},"id":"Q5JvTDvPjolZ","papermill":{"duration":0.4674,"end_time":"2022-03-03T03:20:09.940959","exception":false,"start_time":"2022-03-03T03:20:09.473559","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["     Correlation\n","Lag             \n","0         1.0000\n","1         0.9945\n","2         0.9861\n","3         0.9774\n","4         0.9667\n","5         0.9544\n","6         0.9417\n","7         0.9259\n","8         0.9026\n","9         0.8771\n","10        0.8521\n","11        0.8265\n","12        0.8000\n","13        0.7745\n","14        0.7467\n","15        0.7136\n","16        0.6803\n","17        0.6489\n","18        0.6179\n","19        0.5874\n","20        0.5590\n","21        0.5301\n","22        0.4979\n","23        0.4666\n","24        0.4383\n","25        0.4112\n","26        0.3856\n","27        0.3624\n","28        0.3397\n","29        0.3147\n","30        0.2910\n","31        0.2703\n","32        0.2511\n","33        0.2329\n","34        0.2168\n","35        0.2014\n","36        0.1839\n","37        0.1669\n","38        0.1529\n","39        0.1399\n","40        0.1279\n","41        0.1178\n","42        0.1070\n","43        0.0935\n","44        0.0805\n","45        0.0694\n","46        0.0592\n","47        0.0495\n","48        0.0414\n","49        0.0330\n","50        0.0220\n"]},{"data":{"text/plain":["<AxesSubplot:xlabel='Lag'>"]},"execution_count":13,"metadata":{},"output_type":"execute_result"},{"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":["pd.options.display.float_format = '{:,.4f}'.format\n","l = pd.DataFrame(lag_correlation_ts(y_dataset, y_dataset, 50)) \n","print(l)\n","l.plot()"]},{"cell_type":"markdown","id":"e3685f0a","metadata":{"id":"-eZ9tcidjola","papermill":{"duration":0.070161,"end_time":"2022-03-03T03:20:10.082981","exception":false,"start_time":"2022-03-03T03:20:10.01282","status":"completed"},"tags":[]},"source":["To automate this process, let's make a universal function for time series transformation to create this DataSet.\n"]},{"cell_type":"code","execution_count":14,"id":"c30acd02","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:10.229981Z","iopub.status.busy":"2022-03-03T03:20:10.229149Z","iopub.status.idle":"2022-03-03T03:20:10.239159Z","shell.execute_reply":"2022-03-03T03:20:10.239712Z","shell.execute_reply.started":"2022-03-03T03:13:36.381999Z"},"id":"D6AFsIPsjolb","papermill":{"duration":0.085205,"end_time":"2022-03-03T03:20:10.239949","exception":false,"start_time":"2022-03-03T03:20:10.154744","status":"completed"},"tags":[]},"outputs":[],"source":["def series_to_supervised(in_data, tar_data, n_in=1, dropnan=True, target_dep=False):\n","\n","    n_vars = in_data.shape[1]\n","    cols, names = list(), list()\n","\n","    if target_dep:\n","        i_start = 1\n","    else:\n","        i_start = 0\n","    for i in range(i_start, n_in + 1):\n","        cols.append(in_data.shift(i))\n","        names += [('%s(t-%d)' % (in_data.columns[j], i)) for j in range(n_vars)]\n","\n","    if target_dep:\n","        for i in range(n_in, -1, -1):\n","            cols.append(tar_data.shift(i))\n","            names += [('%s(t-%d)' % (tar_data.name, i))]\n","    else:\n","        # put it all together\n","        cols.append(tar_data)\n","        names.append(tar_data.name)\n","    agg = pd.concat(cols, axis=1)\n","    agg.columns = names\n","\n","    # drop rows with NaN values\n","    if dropnan:\n","        agg.dropna(inplace=True)\n","\n","    return agg"]},{"cell_type":"markdown","id":"c803e6fe","metadata":{"id":"Rz7l6YB7jolc","papermill":{"duration":0.070872,"end_time":"2022-03-03T03:20:10.383862","exception":false,"start_time":"2022-03-03T03:20:10.31299","status":"completed"},"tags":[]},"source":[" When forecasting time series, the input and output fields are the same, only shifted by the lag.\n"]},{"cell_type":"code","execution_count":15,"id":"9e713942","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:10.537427Z","iopub.status.busy":"2022-03-03T03:20:10.536229Z","iopub.status.idle":"2022-03-03T03:20:10.566644Z","shell.execute_reply":"2022-03-03T03:20:10.567223Z","shell.execute_reply.started":"2022-03-03T03:13:36.391876Z"},"id":"aUQorOh_jolc","papermill":{"duration":0.110572,"end_time":"2022-03-03T03:20:10.567458","exception":false,"start_time":"2022-03-03T03:20:10.456886","status":"completed"},"tags":[]},"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>new_cases(t-0)</th>\n","      <th>new_cases(t-1)</th>\n","      <th>new_cases(t-2)</th>\n","      <th>new_cases(t-3)</th>\n","      <th>new_cases(t-4)</th>\n","      <th>new_cases(t-5)</th>\n","      <th>new_cases(t-6)</th>\n","      <th>new_cases(t-7)</th>\n","      <th>new_cases(t-8)</th>\n","      <th>new_cases(t-9)</th>\n","      <th>new_cases(t-10)</th>\n","      <th>new_cases(t-11)</th>\n","      <th>new_cases(t-12)</th>\n","      <th>new_cases(t-13)</th>\n","      <th>new_cases(t-14)</th>\n","      <th>new_cases</th>\n","    </tr>\n","    <tr>\n","      <th>date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2020-02-19</th>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-20</th>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-21</th>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-22</th>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02-23</th>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","      <td>0.0000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-25</th>\n","      <td>11,499.0000</td>\n","      <td>13,166.0000</td>\n","      <td>14,148.0000</td>\n","      <td>15,102.0000</td>\n","      <td>13,405.0000</td>\n","      <td>16,051.0000</td>\n","      <td>19,968.0000</td>\n","      <td>22,270.0000</td>\n","      <td>25,920.0000</td>\n","      <td>30,757.0000</td>\n","      <td>30,615.0000</td>\n","      <td>27,409.0000</td>\n","      <td>34,113.0000</td>\n","      <td>44,877.0000</td>\n","      <td>50,407.0000</td>\n","      <td>11,499.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-26</th>\n","      <td>10,273.0000</td>\n","      <td>11,499.0000</td>\n","      <td>13,166.0000</td>\n","      <td>14,148.0000</td>\n","      <td>15,102.0000</td>\n","      <td>13,405.0000</td>\n","      <td>16,051.0000</td>\n","      <td>19,968.0000</td>\n","      <td>22,270.0000</td>\n","      <td>25,920.0000</td>\n","      <td>30,757.0000</td>\n","      <td>30,615.0000</td>\n","      <td>27,409.0000</td>\n","      <td>34,113.0000</td>\n","      <td>44,877.0000</td>\n","      <td>10,273.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-27</th>\n","      <td>8,013.0000</td>\n","      <td>10,273.0000</td>\n","      <td>11,499.0000</td>\n","      <td>13,166.0000</td>\n","      <td>14,148.0000</td>\n","      <td>15,102.0000</td>\n","      <td>13,405.0000</td>\n","      <td>16,051.0000</td>\n","      <td>19,968.0000</td>\n","      <td>22,270.0000</td>\n","      <td>25,920.0000</td>\n","      <td>30,757.0000</td>\n","      <td>30,615.0000</td>\n","      <td>27,409.0000</td>\n","      <td>34,113.0000</td>\n","      <td>8,013.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02-28</th>\n","      <td>6,915.0000</td>\n","      <td>8,013.0000</td>\n","      <td>10,273.0000</td>\n","      <td>11,499.0000</td>\n","      <td>13,166.0000</td>\n","      <td>14,148.0000</td>\n","      <td>15,102.0000</td>\n","      <td>13,405.0000</td>\n","      <td>16,051.0000</td>\n","      <td>19,968.0000</td>\n","      <td>22,270.0000</td>\n","      <td>25,920.0000</td>\n","      <td>30,757.0000</td>\n","      <td>30,615.0000</td>\n","      <td>27,409.0000</td>\n","      <td>6,915.0000</td>\n","    </tr>\n","    <tr>\n","      <th>2022-03-01</th>\n","      <td>7,554.0000</td>\n","      <td>6,915.0000</td>\n","      <td>8,013.0000</td>\n","      <td>10,273.0000</td>\n","      <td>11,499.0000</td>\n","      <td>13,166.0000</td>\n","      <td>14,148.0000</td>\n","      <td>15,102.0000</td>\n","      <td>13,405.0000</td>\n","      <td>16,051.0000</td>\n","      <td>19,968.0000</td>\n","      <td>22,270.0000</td>\n","      <td>25,920.0000</td>\n","      <td>30,757.0000</td>\n","      <td>30,615.0000</td>\n","      <td>7,554.0000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>742 rows × 16 columns</p>\n","</div>"],"text/plain":["            new_cases(t-0)  new_cases(t-1)  new_cases(t-2)  new_cases(t-3)  \\\n","date                                                                         \n","2020-02-19          0.0000          0.0000          0.0000          0.0000   \n","2020-02-20          0.0000          0.0000          0.0000          0.0000   \n","2020-02-21          0.0000          0.0000          0.0000          0.0000   \n","2020-02-22          0.0000          0.0000          0.0000          0.0000   \n","2020-02-23          0.0000          0.0000          0.0000          0.0000   \n","...                    ...             ...             ...             ...   \n","2022-02-25     11,499.0000     13,166.0000     14,148.0000     15,102.0000   \n","2022-02-26     10,273.0000     11,499.0000     13,166.0000     14,148.0000   \n","2022-02-27      8,013.0000     10,273.0000     11,499.0000     13,166.0000   \n","2022-02-28      6,915.0000      8,013.0000     10,273.0000     11,499.0000   \n","2022-03-01      7,554.0000      6,915.0000      8,013.0000     10,273.0000   \n","\n","            new_cases(t-4)  new_cases(t-5)  new_cases(t-6)  new_cases(t-7)  \\\n","date                                                                         \n","2020-02-19          0.0000          0.0000          0.0000          0.0000   \n","2020-02-20          0.0000          0.0000          0.0000          0.0000   \n","2020-02-21          0.0000          0.0000          0.0000          0.0000   \n","2020-02-22          0.0000          0.0000          0.0000          0.0000   \n","2020-02-23          0.0000          0.0000          0.0000          0.0000   \n","...                    ...             ...             ...             ...   \n","2022-02-25     13,405.0000     16,051.0000     19,968.0000     22,270.0000   \n","2022-02-26     15,102.0000     13,405.0000     16,051.0000     19,968.0000   \n","2022-02-27     14,148.0000     15,102.0000     13,405.0000     16,051.0000   \n","2022-02-28     13,166.0000     14,148.0000     15,102.0000     13,405.0000   \n","2022-03-01     11,499.0000     13,166.0000     14,148.0000     15,102.0000   \n","\n","            new_cases(t-8)  new_cases(t-9)  new_cases(t-10)  new_cases(t-11)  \\\n","date                                                                           \n","2020-02-19          0.0000          0.0000           0.0000           0.0000   \n","2020-02-20          0.0000          0.0000           0.0000           0.0000   \n","2020-02-21          0.0000          0.0000           0.0000           0.0000   \n","2020-02-22          0.0000          0.0000           0.0000           0.0000   \n","2020-02-23          0.0000          0.0000           0.0000           0.0000   \n","...                    ...             ...              ...              ...   \n","2022-02-25     25,920.0000     30,757.0000      30,615.0000      27,409.0000   \n","2022-02-26     22,270.0000     25,920.0000      30,757.0000      30,615.0000   \n","2022-02-27     19,968.0000     22,270.0000      25,920.0000      30,757.0000   \n","2022-02-28     16,051.0000     19,968.0000      22,270.0000      25,920.0000   \n","2022-03-01     13,405.0000     16,051.0000      19,968.0000      22,270.0000   \n","\n","            new_cases(t-12)  new_cases(t-13)  new_cases(t-14)   new_cases  \n","date                                                                       \n","2020-02-19           0.0000           0.0000           0.0000      0.0000  \n","2020-02-20           0.0000           0.0000           0.0000      0.0000  \n","2020-02-21           0.0000           0.0000           0.0000      0.0000  \n","2020-02-22           0.0000           0.0000           0.0000      0.0000  \n","2020-02-23           0.0000           0.0000           0.0000      0.0000  \n","...                     ...              ...              ...         ...  \n","2022-02-25      34,113.0000      44,877.0000      50,407.0000 11,499.0000  \n","2022-02-26      27,409.0000      34,113.0000      44,877.0000 10,273.0000  \n","2022-02-27      30,615.0000      27,409.0000      34,113.0000  8,013.0000  \n","2022-02-28      30,757.0000      30,615.0000      27,409.0000  6,915.0000  \n","2022-03-01      25,920.0000      30,757.0000      30,615.0000  7,554.0000  \n","\n","[742 rows x 16 columns]"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["dataset = series_to_supervised(pd.DataFrame(y_dataset), y_dataset, 14)\n","dataset"]},{"cell_type":"markdown","id":"c04fd5bf","metadata":{"id":"uyhomtQOjold","papermill":{"duration":0.072399,"end_time":"2022-03-03T03:20:10.714442","exception":false,"start_time":"2022-03-03T03:20:10.642043","status":"completed"},"tags":[]},"source":["As you can see, the first and last columns contain the same target data.\n","Now we should create input (**X**) and output (**Y**) .\n"]},{"cell_type":"code","execution_count":16,"id":"7dde7edc","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:10.868638Z","iopub.status.busy":"2022-03-03T03:20:10.86792Z","iopub.status.idle":"2022-03-03T03:20:10.877806Z","shell.execute_reply":"2022-03-03T03:20:10.878392Z","shell.execute_reply.started":"2022-03-03T03:13:36.423837Z"},"id":"HfNkGMxYjold","papermill":{"duration":0.085512,"end_time":"2022-03-03T03:20:10.878611","exception":false,"start_time":"2022-03-03T03:20:10.793099","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Input:  Index(['new_cases(t-1)', 'new_cases(t-2)', 'new_cases(t-3)', 'new_cases(t-4)',\n","       'new_cases(t-5)', 'new_cases(t-6)', 'new_cases(t-7)', 'new_cases(t-8)',\n","       'new_cases(t-9)', 'new_cases(t-10)', 'new_cases(t-11)',\n","       'new_cases(t-12)', 'new_cases(t-13)', 'new_cases(t-14)'],\n","      dtype='object')\n","Target: new_cases\n"]}],"source":["col = dataset.columns\n","X, Y = dataset[col[1:-1]], dataset[col[-1]]\n","print(\"Input: \", X.columns)\n","print(\"Target:\", Y.name)"]},{"cell_type":"markdown","id":"2cf6878f","metadata":{"id":"Fe_XPrg2jole","papermill":{"duration":0.074191,"end_time":"2022-03-03T03:20:11.025669","exception":false,"start_time":"2022-03-03T03:20:10.951478","status":"completed"},"tags":[]},"source":["### Data normalization\n"]},{"cell_type":"markdown","id":"93709ec6","metadata":{"id":"BDsWx7rHjolf","papermill":{"duration":0.072987,"end_time":"2022-03-03T03:20:11.170655","exception":false,"start_time":"2022-03-03T03:20:11.097668","status":"completed"},"tags":[]},"source":["After that, we should normalize all the data. To do this, the **sklearn.preprocessing.MinMaxScaler** module should be used.\n","It allows us to normalize **fit_transform()** and convert back all the data easily: **inverse_transform()**.\n"]},{"cell_type":"code","execution_count":17,"id":"bb4739e1","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:11.325205Z","iopub.status.busy":"2022-03-03T03:20:11.324247Z","iopub.status.idle":"2022-03-03T03:20:12.30764Z","shell.execute_reply":"2022-03-03T03:20:12.306969Z","shell.execute_reply.started":"2022-03-03T03:13:36.433926Z"},"id":"33T04gPBjolf","papermill":{"duration":1.064083,"end_time":"2022-03-03T03:20:12.30782","exception":false,"start_time":"2022-03-03T03:20:11.243737","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.preprocessing import MinMaxScaler\n","scaler_x = MinMaxScaler(feature_range=(0, 1))\n","scaler_y = MinMaxScaler(feature_range=(0, 1))\n","\n","scaled_x = scaler_x.fit_transform(X)\n","scaled_y = scaler_y.fit_transform(Y.values.reshape(-1, 1))"]},{"cell_type":"markdown","id":"c2f76951","metadata":{"id":"1S3RMN93jolg","papermill":{"duration":0.073571,"end_time":"2022-03-03T03:20:12.453661","exception":false,"start_time":"2022-03-03T03:20:12.38009","status":"completed"},"tags":[]},"source":["After that, we are going to form training and test DataSets using **sklearn.model_selection.train_test_split()**. \n","\n","We will make them at the ratio of 73/27. Without shuffling. It means, that test samples are located in the end of **X** and **Y** DataSets.\n","\n","Input normalized DataSets: **X_train, X_test**\n","\n","Target normalized DataSets: **y_train, y_test**\n"]},{"cell_type":"code","execution_count":18,"id":"80b2aa68","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:12.605777Z","iopub.status.busy":"2022-03-03T03:20:12.604825Z","iopub.status.idle":"2022-03-03T03:20:12.669532Z","shell.execute_reply":"2022-03-03T03:20:12.670146Z","shell.execute_reply.started":"2022-03-03T03:13:36.449135Z"},"id":"5PCYCVmOjoli","papermill":{"duration":0.144402,"end_time":"2022-03-03T03:20:12.670425","exception":false,"start_time":"2022-03-03T03:20:12.526023","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(scaled_x, scaled_y, test_size=0.32, shuffle=False)"]},{"cell_type":"markdown","id":"deaed678","metadata":{"id":"tMMBy15ejolk","papermill":{"duration":0.074027,"end_time":"2022-03-03T03:20:12.816958","exception":false,"start_time":"2022-03-03T03:20:12.742931","status":"completed"},"tags":[]},"source":["All the data is normalized now. However, in order to compare to results, we need real scale data of the training and test DataSet:\n"]},{"cell_type":"code","execution_count":19,"id":"a304d252","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:12.968821Z","iopub.status.busy":"2022-03-03T03:20:12.967963Z","iopub.status.idle":"2022-03-03T03:20:12.97293Z","shell.execute_reply":"2022-03-03T03:20:12.973548Z","shell.execute_reply.started":"2022-03-03T03:13:36.46102Z"},"id":"6GNfTSkcjoll","papermill":{"duration":0.08443,"end_time":"2022-03-03T03:20:12.973829","exception":false,"start_time":"2022-03-03T03:20:12.889399","status":"completed"},"tags":[]},"outputs":[],"source":["res_train = scaler_y.inverse_transform(y_train).flatten()\n","res_test = scaler_y.inverse_transform(y_test).flatten()"]},{"cell_type":"markdown","id":"78881606","metadata":{"id":"Ir65XC3ujoln","papermill":{"duration":0.072648,"end_time":"2022-03-03T03:20:13.119386","exception":false,"start_time":"2022-03-03T03:20:13.046738","status":"completed"},"tags":[]},"source":["Target real scale DataSets: **res_train, res_test**.\n"]},{"cell_type":"markdown","id":"1782ec78","metadata":{"id":"parEDxGDjoln","papermill":{"duration":0.072081,"end_time":"2022-03-03T03:20:13.264059","exception":false,"start_time":"2022-03-03T03:20:13.191978","status":"completed"},"tags":[]},"source":["# Linear Regression\n"]},{"cell_type":"markdown","id":"b59dfbab","metadata":{"id":"5h7sFVjXjolo","papermill":{"duration":0.073851,"end_time":"2022-03-03T03:20:13.413074","exception":false,"start_time":"2022-03-03T03:20:13.339223","status":"completed"},"tags":[]},"source":["First of all, we should create a model. We will test three type of models. A Linear regression, a Multilayer Neural Network with Backpropagation and a Long Short-Term Memory Neural Network.\n","Let's create a **LinearRegression()**\n"]},{"cell_type":"code","execution_count":20,"id":"a6a13c9d","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:13.56273Z","iopub.status.busy":"2022-03-03T03:20:13.562019Z","iopub.status.idle":"2022-03-03T03:20:13.657182Z","shell.execute_reply":"2022-03-03T03:20:13.65777Z","shell.execute_reply.started":"2022-03-03T03:13:36.477861Z"},"id":"bYQ4Lq0Yjolo","papermill":{"duration":0.172637,"end_time":"2022-03-03T03:20:13.657997","exception":false,"start_time":"2022-03-03T03:20:13.48536","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.linear_model import LinearRegression\n","regressor = LinearRegression()"]},{"cell_type":"markdown","id":"69af9120","metadata":{"id":"KjZOYP67jolp","papermill":{"duration":0.073469,"end_time":"2022-03-03T03:20:13.805061","exception":false,"start_time":"2022-03-03T03:20:13.731592","status":"completed"},"tags":[]},"source":["After that, our model should be fitted on the training DataSet. Regardless of the model type, the function fit() is used to do this.\n"]},{"cell_type":"code","execution_count":21,"id":"7417a2a2","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:13.96015Z","iopub.status.busy":"2022-03-03T03:20:13.959364Z","iopub.status.idle":"2022-03-03T03:20:13.983364Z","shell.execute_reply":"2022-03-03T03:20:13.984015Z","shell.execute_reply.started":"2022-03-03T03:13:36.488126Z"},"id":"1Jo2bfiljolq","papermill":{"duration":0.103447,"end_time":"2022-03-03T03:20:13.984242","exception":false,"start_time":"2022-03-03T03:20:13.880795","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["LinearRegression()"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["regressor.fit(X_train, y_train)"]},{"cell_type":"markdown","id":"6f1c9c96","metadata":{"id":"lbkIVFfSjolq","papermill":{"duration":0.074112,"end_time":"2022-03-03T03:20:14.13138","exception":false,"start_time":"2022-03-03T03:20:14.057268","status":"completed"},"tags":[]},"source":["Then we can test it on the test DataSet and use it for prognostication.\n"]},{"cell_type":"code","execution_count":22,"id":"92950c98","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:14.282704Z","iopub.status.busy":"2022-03-03T03:20:14.281918Z","iopub.status.idle":"2022-03-03T03:20:14.286955Z","shell.execute_reply":"2022-03-03T03:20:14.287514Z","shell.execute_reply.started":"2022-03-03T03:13:36.500622Z"},"id":"SWoFdKSIjolr","papermill":{"duration":0.083459,"end_time":"2022-03-03T03:20:14.287748","exception":false,"start_time":"2022-03-03T03:20:14.204289","status":"completed"},"tags":[]},"outputs":[],"source":["y_pred_test_ln = regressor.predict(X_test)\n","y_pred_test_ln = scaler_y.inverse_transform(y_pred_test_ln).flatten()"]},{"cell_type":"markdown","id":"1129b340","metadata":{"id":"4F7iRfRljolr","papermill":{"duration":0.07336,"end_time":"2022-03-03T03:20:14.436206","exception":false,"start_time":"2022-03-03T03:20:14.362846","status":"completed"},"tags":[]},"source":["Let's analyze the results accuracy using **sklearn.metrics**.\n"]},{"cell_type":"code","execution_count":23,"id":"311dfce8","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:14.596564Z","iopub.status.busy":"2022-03-03T03:20:14.595732Z","iopub.status.idle":"2022-03-03T03:20:14.601546Z","shell.execute_reply":"2022-03-03T03:20:14.602425Z","shell.execute_reply.started":"2022-03-03T03:13:36.508548Z"},"id":"rOrh-hlajols","papermill":{"duration":0.092939,"end_time":"2022-03-03T03:20:14.60274","exception":false,"start_time":"2022-03-03T03:20:14.509801","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Correlation train 0.9968040612878395\n","Correlation test 0.9872560154184292\n","Mean Absolute Error: 51706.475155126034\n","Mean Squared Error: 8393426094.392239\n","Root Mean Squared Error: 91615.6432842789\n"]}],"source":["from sklearn import metrics\n","print(\"Correlation train\", regressor.score(X_train, y_train))\n","print(\"Correlation test\", regressor.score(X_test, y_test))\n","print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred_test_ln))\n","print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred_test_ln))\n","print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred_test_ln)))"]},{"cell_type":"markdown","id":"b961b3e1","metadata":{"id":"FyaTCujkjols","papermill":{"duration":0.074325,"end_time":"2022-03-03T03:20:14.754225","exception":false,"start_time":"2022-03-03T03:20:14.6799","status":"completed"},"tags":[]},"source":["# Back Propagation NN\n"]},{"cell_type":"markdown","id":"090f3d62","metadata":{"id":"BP7FKPRVjolt","papermill":{"duration":0.074729,"end_time":"2022-03-03T03:20:14.905239","exception":false,"start_time":"2022-03-03T03:20:14.83051","status":"completed"},"tags":[]},"source":["The modern approach to the establishment of complex functional dependencies is the use of neural networks. A classical neural network is a **multilayer neural network with back propagation**.\n","\n","To do this, we will use the **keras** framework.\n","First of all, we should create a Neural Network model as a separate function.\n","\n","A neural network is a sequence of layers. The function **Sequential()** is used to create a network.\n","\n","Let's create a network that consists of 2 hidden layers. Each of which consists of 100 neurons. **keras.layers.Dense()**.\n","\n","To avoid retraining problems, we will use additional layers **keras.layers.Dropout()**.\n","\n","The output layer will consist of one neuron, since we have only one value at the output.\n","\n","The model should be compiled for fitting and predicting: **keras.Model.compile()**.\n"]},{"cell_type":"code","execution_count":24,"id":"a1485e66","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:15.057251Z","iopub.status.busy":"2022-03-03T03:20:15.056462Z","iopub.status.idle":"2022-03-03T03:20:15.063573Z","shell.execute_reply":"2022-03-03T03:20:15.064162Z","shell.execute_reply.started":"2022-03-03T03:13:36.523948Z"},"id":"InbG_JsPjolt","papermill":{"duration":0.085139,"end_time":"2022-03-03T03:20:15.064551","exception":false,"start_time":"2022-03-03T03:20:14.979412","status":"completed"},"tags":[]},"outputs":[],"source":["def BP_model(X):\n","    \"\"\"\n","    Multilayer neural network with back propagation.\n","    X: Input DataSet\n","    return: keras NN model\n","    \"\"\"\n","    # create model\n","    model = Sequential() \n","    model.add(Dense(100, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))\n","    model.add(Dropout(0.2))\n","    model.add(Dense(100, kernel_initializer='normal', activation='relu'))\n","    model.add(Dropout(0.2))\n","    model.add(Dense(1, kernel_initializer='normal'))\n","    # Compile model\n","    model.compile(loss='mean_squared_error', optimizer='adam')\n","    return model"]},{"cell_type":"markdown","id":"93c950e9","metadata":{"id":"k27IMZkejolu","papermill":{"duration":0.072636,"end_time":"2022-03-03T03:20:15.212191","exception":false,"start_time":"2022-03-03T03:20:15.139555","status":"completed"},"tags":[]},"source":["Once the model function is built, it is necessary to create a neural network directly and specify the learning parameters: **keras.wrappers.scikit_learn.KerasRegressor()**. \n","\n","Also we should define the number of fitting **epoch and batch size**.\n"]},{"cell_type":"code","execution_count":25,"id":"963bb3a8","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:15.371083Z","iopub.status.busy":"2022-03-03T03:20:15.370066Z","iopub.status.idle":"2022-03-03T03:20:21.775522Z","shell.execute_reply":"2022-03-03T03:20:21.774709Z","shell.execute_reply.started":"2022-03-03T03:13:36.531299Z"},"id":"ebZl4TYHjolu","papermill":{"duration":6.488457,"end_time":"2022-03-03T03:20:21.775761","exception":false,"start_time":"2022-03-03T03:20:15.287304","status":"completed"},"tags":[]},"outputs":[],"source":["from keras.wrappers.scikit_learn import KerasRegressor\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import Dropout\n","epochs = 1000\n","\n","batch_size=int(y_train.shape[0]*.1)\n","\n","estimator = KerasRegressor(build_fn=BP_model, X=X_train, epochs=epochs, batch_size=batch_size, verbose=1)"]},{"cell_type":"markdown","id":"4f0df628","metadata":{"id":"LNuDkJ0Mjol2","papermill":{"duration":0.073988,"end_time":"2022-03-03T03:20:21.92414","exception":false,"start_time":"2022-03-03T03:20:21.850152","status":"completed"},"tags":[]},"source":["Now, let’s train our model for **1000** epochs.\n","It should be noted that the fitting process is very slow. Therefore, we saved our fitted model to a file.\n","To save time, we will upload the fitted model.\n","If you like, you can leave the parameter **fitting on True** to refit the model.\n","If you like, you can leave the parameter **fitting_save on True** to resave the model.\n"]},{"cell_type":"code","execution_count":26,"id":"3b6d5a85","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:20:22.078399Z","iopub.status.busy":"2022-03-03T03:20:22.077594Z","iopub.status.idle":"2022-03-03T03:21:49.036199Z","shell.execute_reply":"2022-03-03T03:21:49.035626Z","shell.execute_reply.started":"2022-03-03T03:13:36.543422Z"},"id":"haQXc1LTjol2","papermill":{"duration":87.036926,"end_time":"2022-03-03T03:21:49.036437","exception":false,"start_time":"2022-03-03T03:20:21.999511","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["\n","User settings:\n","\n","   KMP_AFFINITY=granularity=fine,noverbose,compact,1,0\n","   KMP_BLOCKTIME=0\n","   KMP_DUPLICATE_LIB_OK=True\n","   KMP_INIT_AT_FORK=FALSE\n","   KMP_SETTINGS=1\n","   KMP_WARNINGS=0\n","\n","Effective settings:\n","\n","   KMP_ABORT_DELAY=0\n","   KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n","   KMP_ALIGN_ALLOC=64\n","   KMP_ALL_THREADPRIVATE=128\n","   KMP_ATOMIC_MODE=2\n","   KMP_BLOCKTIME=0\n","   KMP_CPUINFO_FILE: value is not defined\n","   KMP_DETERMINISTIC_REDUCTION=false\n","   KMP_DEVICE_THREAD_LIMIT=2147483647\n","   KMP_DISP_NUM_BUFFERS=7\n","   KMP_DUPLICATE_LIB_OK=true\n","   KMP_ENABLE_TASK_THROTTLING=true\n","   KMP_FORCE_REDUCTION: value is not defined\n","   KMP_FOREIGN_THREADS_THREADPRIVATE=true\n","   KMP_FORKJOIN_BARRIER='2,2'\n","   KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n","   KMP_GTID_MODE=3\n","   KMP_HANDLE_SIGNALS=false\n","   KMP_HOT_TEAMS_MAX_LEVEL=1\n","   KMP_HOT_TEAMS_MODE=0\n","   KMP_INIT_AT_FORK=true\n","   KMP_LIBRARY=throughput\n","   KMP_LOCK_KIND=queuing\n","   KMP_MALLOC_POOL_INCR=1M\n","   KMP_NUM_LOCKS_IN_BLOCK=1\n","   KMP_PLAIN_BARRIER='2,2'\n","   KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n","   KMP_REDUCTION_BARRIER='1,1'\n","   KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n","   KMP_SCHEDULE='static,balanced;guided,iterative'\n","   KMP_SETTINGS=true\n","   KMP_SPIN_BACKOFF_PARAMS='4096,100'\n","   KMP_STACKOFFSET=64\n","   KMP_STACKPAD=0\n","   KMP_STACKSIZE=8M\n","   KMP_STORAGE_MAP=false\n","   KMP_TASKING=2\n","   KMP_TASKLOOP_MIN_TASKS=0\n","   KMP_TASK_STEALING_CONSTRAINT=1\n","   KMP_TEAMS_THREAD_LIMIT=4\n","   KMP_TOPOLOGY_METHOD=all\n","   KMP_USE_YIELD=1\n","   KMP_VERSION=false\n","   KMP_WARNINGS=false\n","   OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n","   OMP_ALLOCATOR=omp_default_mem_alloc\n","   OMP_CANCELLATION=false\n","   OMP_DEFAULT_DEVICE=0\n","   OMP_DISPLAY_AFFINITY=false\n","   OMP_DISPLAY_ENV=false\n","   OMP_DYNAMIC=false\n","   OMP_MAX_ACTIVE_LEVELS=1\n","   OMP_MAX_TASK_PRIORITY=0\n","   OMP_NESTED: deprecated; max-active-levels-var=1\n","   OMP_NUM_THREADS: value is not defined\n","   OMP_PLACES: value is not defined\n","   OMP_PROC_BIND='intel'\n","   OMP_SCHEDULE='static'\n","   OMP_STACKSIZE=8M\n","   OMP_TARGET_OFFLOAD=DEFAULT\n","   OMP_THREAD_LIMIT=2147483647\n","   OMP_WAIT_POLICY=PASSIVE\n","   KMP_AFFINITY='noverbose,warnings,respect,granularity=fine,compact,1,0'\n","\n","2022-03-03 03:20:22.119545: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n","2022-03-03 03:20:22.330962: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"]},{"name":"stdout","output_type":"stream","text":["Epoch 1/1000\n","11/11 [==============================] - 1s 26ms/step - loss: 0.0364 - val_loss: 0.0127\n","Epoch 2/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0117 - val_loss: 0.0146\n","Epoch 3/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0072 - val_loss: 0.0089\n","Epoch 4/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0050 - val_loss: 0.0081\n","Epoch 5/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0036 - val_loss: 0.0065\n","Epoch 6/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0034 - val_loss: 0.0058\n","Epoch 7/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0030 - val_loss: 0.0045\n","Epoch 8/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0027 - val_loss: 0.0039\n","Epoch 9/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0023 - val_loss: 0.0030\n","Epoch 10/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0047 - val_loss: 0.0034\n","Epoch 11/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0026 - val_loss: 0.0029\n","Epoch 12/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0018 - val_loss: 0.0027\n","Epoch 13/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0018 - val_loss: 0.0024\n","Epoch 14/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0022 - val_loss: 0.0023\n","Epoch 15/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0019 - val_loss: 0.0019\n","Epoch 16/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 0.0020\n","Epoch 17/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0020 - val_loss: 0.0017\n","Epoch 18/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 0.0017\n","Epoch 19/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.9823e-04 - val_loss: 0.0016\n","Epoch 20/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0015\n","Epoch 21/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0017 - val_loss: 0.0016\n","Epoch 22/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 0.0018\n","Epoch 23/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 0.0017\n","Epoch 24/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0023 - val_loss: 0.0019\n","Epoch 25/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0018 - val_loss: 0.0016\n","Epoch 26/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 0.0015\n","Epoch 27/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0015\n","Epoch 28/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0014\n","Epoch 29/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0016 - val_loss: 0.0014\n","Epoch 30/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0015\n","Epoch 31/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0015 - val_loss: 0.0013\n","Epoch 32/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0014 - val_loss: 0.0014\n","Epoch 33/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 0.0018\n","Epoch 34/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0015\n","Epoch 35/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 0.0017\n","Epoch 36/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 0.0012 - val_loss: 0.0011\n","Epoch 37/1000\n","11/11 [==============================] - 0s 13ms/step - loss: 0.0015 - val_loss: 0.0012\n","Epoch 38/1000\n","11/11 [==============================] - 0s 15ms/step - loss: 0.0012 - val_loss: 0.0012\n","Epoch 39/1000\n","11/11 [==============================] - 0s 22ms/step - loss: 0.0013 - val_loss: 0.0014\n","Epoch 40/1000\n","11/11 [==============================] - 0s 24ms/step - loss: 0.0013 - val_loss: 0.0014\n","Epoch 41/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0013\n","Epoch 42/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3255e-04 - val_loss: 0.0013\n","Epoch 43/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7939e-04 - val_loss: 0.0014\n","Epoch 44/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0012\n","Epoch 45/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0011\n","Epoch 46/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0015 - val_loss: 0.0017\n","Epoch 47/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0011\n","Epoch 48/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1554e-04 - val_loss: 0.0010\n","Epoch 49/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0011\n","Epoch 50/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0025\n","Epoch 51/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0017 - val_loss: 0.0012\n","Epoch 52/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0012\n","Epoch 53/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4244e-04 - val_loss: 0.0014\n","Epoch 54/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0019 - val_loss: 0.0012\n","Epoch 55/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0011\n","Epoch 56/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0044 - val_loss: 0.0038\n","Epoch 57/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0025 - val_loss: 0.0017\n","Epoch 58/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0016 - val_loss: 0.0014\n","Epoch 59/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 9.7440e-04\n","Epoch 60/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0014 - val_loss: 0.0010\n","Epoch 61/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 9.4521e-04\n","Epoch 62/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0019 - val_loss: 0.0021\n","Epoch 63/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0010\n","Epoch 64/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0014 - val_loss: 0.0016\n","Epoch 65/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.9544e-04 - val_loss: 9.2526e-04\n","Epoch 66/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0017\n","Epoch 67/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 9.5512e-04\n","Epoch 68/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 9.6033e-04\n","Epoch 69/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.9916e-04 - val_loss: 0.0020\n","Epoch 70/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.8445e-04 - val_loss: 9.7431e-04\n","Epoch 71/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0017 - val_loss: 0.0020\n","Epoch 72/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 0.0010\n","Epoch 73/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.7523e-04 - val_loss: 0.0014\n","Epoch 74/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3628e-04 - val_loss: 0.0016\n","Epoch 75/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0012\n","Epoch 76/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7749e-04 - val_loss: 0.0011\n","Epoch 77/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7348e-04 - val_loss: 0.0012\n","Epoch 78/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0013\n","Epoch 79/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.8719e-04 - val_loss: 0.0011\n","Epoch 80/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 0.0011\n","Epoch 81/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0013\n","Epoch 82/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.1126e-04 - val_loss: 0.0012\n","Epoch 83/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0329e-04 - val_loss: 9.2321e-04\n","Epoch 84/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 0.0021\n","Epoch 85/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0018 - val_loss: 0.0011\n","Epoch 86/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.8937e-04 - val_loss: 0.0013\n","Epoch 87/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 9.2372e-04 - val_loss: 0.0013\n","Epoch 88/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.2087e-04 - val_loss: 7.9995e-04\n","Epoch 89/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0019 - val_loss: 0.0023\n","Epoch 90/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 0.0013 - val_loss: 8.9244e-04\n","Epoch 91/1000\n","11/11 [==============================] - 0s 11ms/step - loss: 8.5776e-04 - val_loss: 0.0015\n","Epoch 92/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 0.0010\n","Epoch 93/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 8.2437e-04\n","Epoch 94/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.0016\n","Epoch 95/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 0.0010\n","Epoch 96/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.6415e-04 - val_loss: 0.0014\n","Epoch 97/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0010\n","Epoch 98/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 0.0010\n","Epoch 99/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0010\n","Epoch 100/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.2131e-04 - val_loss: 0.0011\n","Epoch 101/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0011\n","Epoch 102/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4840e-04 - val_loss: 0.0012\n","Epoch 103/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3197e-04 - val_loss: 9.8643e-04\n","Epoch 104/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.9196e-04 - val_loss: 0.0011\n","Epoch 105/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 9.5720e-04 - val_loss: 8.9814e-04\n","Epoch 106/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 0.0016\n","Epoch 107/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.8132e-04 - val_loss: 0.0013\n","Epoch 108/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4257e-04 - val_loss: 0.0011\n","Epoch 109/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9108e-04 - val_loss: 0.0011\n","Epoch 110/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2248e-04 - val_loss: 9.2896e-04\n","Epoch 111/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7199e-04 - val_loss: 0.0018\n","Epoch 112/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 9.9571e-04\n","Epoch 113/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.5500e-04 - val_loss: 0.0011\n","Epoch 114/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0584e-04 - val_loss: 8.7082e-04\n","Epoch 115/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.6548e-04 - val_loss: 0.0014\n","Epoch 116/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7349e-04 - val_loss: 9.4653e-04\n","Epoch 117/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0012\n","Epoch 118/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.6158e-04 - val_loss: 0.0013\n","Epoch 119/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 8.4093e-04\n","Epoch 120/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0015\n","Epoch 121/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4647e-04 - val_loss: 7.6509e-04\n","Epoch 122/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0014\n","Epoch 123/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1403e-04 - val_loss: 9.8342e-04\n","Epoch 124/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.6773e-04 - val_loss: 9.7113e-04\n","Epoch 125/1000\n","11/11 [==============================] - 0s 6ms/step - loss: 0.0010 - val_loss: 0.0011\n","Epoch 126/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.5385e-04 - val_loss: 7.3560e-04\n","Epoch 127/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8479e-04 - val_loss: 8.8495e-04\n","Epoch 128/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.5210e-04 - val_loss: 0.0010\n","Epoch 129/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.9921e-04 - val_loss: 7.4583e-04\n","Epoch 130/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.4652e-04 - val_loss: 0.0010\n","Epoch 131/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3516e-04 - val_loss: 9.4047e-04\n","Epoch 132/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0021\n","Epoch 133/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 9.3590e-04\n","Epoch 134/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.5455e-04 - val_loss: 0.0011\n","Epoch 135/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.6610e-04 - val_loss: 0.0010\n","Epoch 136/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.8293e-04 - val_loss: 9.9837e-04\n","Epoch 137/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8933e-04 - val_loss: 9.6518e-04\n","Epoch 138/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.3930e-04 - val_loss: 0.0012\n","Epoch 139/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8809e-04 - val_loss: 0.0011\n","Epoch 140/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.0012\n","Epoch 141/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 8.4787e-04\n","Epoch 142/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4054e-04 - val_loss: 0.0014\n","Epoch 143/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.7442e-04 - val_loss: 9.4323e-04\n","Epoch 144/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0010\n","Epoch 145/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 9.0856e-04\n","Epoch 146/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.1651e-04 - val_loss: 8.4028e-04\n","Epoch 147/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0944e-04 - val_loss: 0.0012\n","Epoch 148/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 9.4054e-04\n","Epoch 149/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 8.3921e-04\n","Epoch 150/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0021 - val_loss: 0.0030\n","Epoch 151/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0019 - val_loss: 8.2759e-04\n","Epoch 152/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.6718e-04 - val_loss: 9.7354e-04\n","Epoch 153/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0016\n","Epoch 154/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.8610e-04 - val_loss: 7.7660e-04\n","Epoch 155/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6541e-04 - val_loss: 0.0015\n","Epoch 156/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3376e-04 - val_loss: 7.7038e-04\n","Epoch 157/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8261e-04 - val_loss: 8.6274e-04\n","Epoch 158/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2153e-04 - val_loss: 0.0013\n","Epoch 159/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.9194e-04 - val_loss: 8.7663e-04\n","Epoch 160/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7103e-04 - val_loss: 0.0014\n","Epoch 161/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.9641e-04 - val_loss: 8.5039e-04\n","Epoch 162/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0020\n","Epoch 163/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.6111e-04 - val_loss: 8.2359e-04\n","Epoch 164/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 8.7643e-04\n","Epoch 165/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7022e-04 - val_loss: 8.9714e-04\n","Epoch 166/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0776e-04 - val_loss: 0.0012\n","Epoch 167/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1429e-04 - val_loss: 7.0250e-04\n","Epoch 168/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0015\n","Epoch 169/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 7.9471e-04\n","Epoch 170/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.2065e-04 - val_loss: 0.0017\n","Epoch 171/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5194e-04 - val_loss: 0.0011\n","Epoch 172/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.1997e-04 - val_loss: 0.0012\n","Epoch 173/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2071e-04 - val_loss: 8.6159e-04\n","Epoch 174/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.5970e-04 - val_loss: 8.5511e-04\n","Epoch 175/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4747e-04 - val_loss: 8.3785e-04\n","Epoch 176/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.3283e-04 - val_loss: 0.0010\n","Epoch 177/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0118e-04 - val_loss: 9.6919e-04\n","Epoch 178/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4045e-04 - val_loss: 0.0018\n","Epoch 179/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0020 - val_loss: 7.5077e-04\n","Epoch 180/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0014\n","Epoch 181/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.6139e-04 - val_loss: 9.5747e-04\n","Epoch 182/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1970e-04 - val_loss: 0.0015\n","Epoch 183/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 8.9417e-04\n","Epoch 184/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6512e-04 - val_loss: 0.0011\n","Epoch 185/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8235e-04 - val_loss: 9.3742e-04\n","Epoch 186/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9056e-04 - val_loss: 9.3191e-04\n","Epoch 187/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.7747e-04 - val_loss: 0.0010\n","Epoch 188/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.0353e-04 - val_loss: 0.0013\n","Epoch 189/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.8766e-04 - val_loss: 6.8656e-04\n","Epoch 190/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3899e-04 - val_loss: 0.0013\n","Epoch 191/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.3563e-04 - val_loss: 9.5605e-04\n","Epoch 192/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7571e-04 - val_loss: 7.5727e-04\n","Epoch 193/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0022\n","Epoch 194/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7450e-04 - val_loss: 7.7572e-04\n","Epoch 195/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.2391e-04 - val_loss: 0.0012\n","Epoch 196/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6137e-04 - val_loss: 0.0012\n","Epoch 197/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.9909e-04 - val_loss: 0.0010\n","Epoch 198/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.3939e-04 - val_loss: 0.0012\n","Epoch 199/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3600e-04 - val_loss: 9.5737e-04\n","Epoch 200/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4439e-04 - val_loss: 7.0137e-04\n","Epoch 201/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.5884e-04 - val_loss: 0.0013\n","Epoch 202/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 9.0576e-04\n","Epoch 203/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.7379e-04 - val_loss: 7.5949e-04\n","Epoch 204/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0028 - val_loss: 0.0033\n","Epoch 205/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 0.0012\n","Epoch 206/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.9969e-04 - val_loss: 0.0016\n","Epoch 207/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0011\n","Epoch 208/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6995e-04 - val_loss: 0.0013\n","Epoch 209/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 7.3406e-04\n","Epoch 210/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.6425e-04 - val_loss: 0.0016\n","Epoch 211/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 8.3635e-04\n","Epoch 212/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1684e-04 - val_loss: 0.0010\n","Epoch 213/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8783e-04 - val_loss: 9.5443e-04\n","Epoch 214/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8363e-04 - val_loss: 7.7119e-04\n","Epoch 215/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0030 - val_loss: 0.0022\n","Epoch 216/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0018 - val_loss: 0.0013\n","Epoch 217/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0018 - val_loss: 0.0020\n","Epoch 218/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 8.0631e-04\n","Epoch 219/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0013\n","Epoch 220/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3839e-04 - val_loss: 8.9545e-04\n","Epoch 221/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7379e-04 - val_loss: 0.0013\n","Epoch 222/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1849e-04 - val_loss: 8.0234e-04\n","Epoch 223/1000\n","11/11 [==============================] - 0s 13ms/step - loss: 9.7820e-04 - val_loss: 9.5798e-04\n","Epoch 224/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0142e-04 - val_loss: 8.1390e-04\n","Epoch 225/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.2008e-04 - val_loss: 0.0014\n","Epoch 226/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 9.3382e-04\n","Epoch 227/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7728e-04 - val_loss: 9.8508e-04\n","Epoch 228/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3211e-04 - val_loss: 9.1550e-04\n","Epoch 229/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8859e-04 - val_loss: 9.0726e-04\n","Epoch 230/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6511e-04 - val_loss: 0.0012\n","Epoch 231/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.0811e-04 - val_loss: 6.6284e-04\n","Epoch 232/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.5039e-04 - val_loss: 0.0017\n","Epoch 233/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 6.8288e-04\n","Epoch 234/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1292e-04 - val_loss: 7.5707e-04\n","Epoch 235/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4908e-04 - val_loss: 9.3169e-04\n","Epoch 236/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.7559e-04 - val_loss: 7.2696e-04\n","Epoch 237/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3794e-04 - val_loss: 6.7330e-04\n","Epoch 238/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2051e-04 - val_loss: 0.0013\n","Epoch 239/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.2063e-04 - val_loss: 6.5459e-04\n","Epoch 240/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4275e-04 - val_loss: 0.0012\n","Epoch 241/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3289e-04 - val_loss: 7.0932e-04\n","Epoch 242/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4938e-04 - val_loss: 0.0011\n","Epoch 243/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.6203e-04 - val_loss: 7.6349e-04\n","Epoch 244/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.9270e-04 - val_loss: 8.0277e-04\n","Epoch 245/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1898e-04 - val_loss: 0.0016\n","Epoch 246/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 6.5352e-04\n","Epoch 247/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3638e-04 - val_loss: 0.0010\n","Epoch 248/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8378e-04 - val_loss: 0.0010\n","Epoch 249/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9044e-04 - val_loss: 7.5254e-04\n","Epoch 250/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 6.8370e-04\n","Epoch 251/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.0015\n","Epoch 252/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 8.4410e-04\n","Epoch 253/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7279e-04 - val_loss: 0.0013\n","Epoch 254/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8024e-04 - val_loss: 6.7702e-04\n","Epoch 255/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.2980e-04 - val_loss: 0.0012\n","Epoch 256/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.8130e-04 - val_loss: 0.0011\n","Epoch 257/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4486e-04 - val_loss: 8.6139e-04\n","Epoch 258/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.6181e-04 - val_loss: 0.0011\n","Epoch 259/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4666e-04 - val_loss: 7.9110e-04\n","Epoch 260/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8207e-04 - val_loss: 9.6870e-04\n","Epoch 261/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8739e-04 - val_loss: 9.9790e-04\n","Epoch 262/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3315e-04 - val_loss: 9.5907e-04\n","Epoch 263/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 8.2461e-04 - val_loss: 6.5889e-04\n","Epoch 264/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.7038e-04 - val_loss: 8.7707e-04\n","Epoch 265/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8319e-04 - val_loss: 0.0010\n","Epoch 266/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.5019e-04 - val_loss: 0.0011\n","Epoch 267/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.5838e-04 - val_loss: 7.5908e-04\n","Epoch 268/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 7.7798e-04\n","Epoch 269/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4866e-04 - val_loss: 9.3659e-04\n","Epoch 270/1000\n","11/11 [==============================] - 0s 10ms/step - loss: 6.1937e-04 - val_loss: 8.6220e-04\n","Epoch 271/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.9891e-04 - val_loss: 8.4943e-04\n","Epoch 272/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7396e-04 - val_loss: 9.7300e-04\n","Epoch 273/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3374e-04 - val_loss: 0.0010\n","Epoch 274/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7655e-04 - val_loss: 6.1482e-04\n","Epoch 275/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0014\n","Epoch 276/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.4747e-04 - val_loss: 0.0014\n","Epoch 277/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0019 - val_loss: 7.3335e-04\n","Epoch 278/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 6.7371e-04\n","Epoch 279/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.0011\n","Epoch 280/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7392e-04 - val_loss: 7.2046e-04\n","Epoch 281/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1276e-04 - val_loss: 8.7617e-04\n","Epoch 282/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7319e-04 - val_loss: 9.4130e-04\n","Epoch 283/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6147e-04 - val_loss: 8.9446e-04\n","Epoch 284/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.2614e-04 - val_loss: 0.0010\n","Epoch 285/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7700e-04 - val_loss: 0.0012\n","Epoch 286/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9424e-04 - val_loss: 7.1562e-04\n","Epoch 287/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.2333e-04 - val_loss: 6.5080e-04\n","Epoch 288/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.9554e-04 - val_loss: 0.0013\n","Epoch 289/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6500e-04 - val_loss: 7.6410e-04\n","Epoch 290/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8960e-04 - val_loss: 6.7374e-04\n","Epoch 291/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0017\n","Epoch 292/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.5489e-04 - val_loss: 6.3097e-04\n","Epoch 293/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3670e-04 - val_loss: 8.6907e-04\n","Epoch 294/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 7.7644e-04\n","Epoch 295/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0014\n","Epoch 296/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 8.2032e-04\n","Epoch 297/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3878e-04 - val_loss: 0.0012\n","Epoch 298/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.5222e-04 - val_loss: 9.8728e-04\n","Epoch 299/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.7936e-04 - val_loss: 6.7220e-04\n","Epoch 300/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 7.3796e-04\n","Epoch 301/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4495e-04 - val_loss: 8.8812e-04\n","Epoch 302/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.5414e-04 - val_loss: 8.5715e-04\n","Epoch 303/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4784e-04 - val_loss: 6.7920e-04\n","Epoch 304/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2068e-04 - val_loss: 0.0011\n","Epoch 305/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4817e-04 - val_loss: 6.8217e-04\n","Epoch 306/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5497e-04 - val_loss: 8.8401e-04\n","Epoch 307/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2114e-04 - val_loss: 6.8633e-04\n","Epoch 308/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8609e-04 - val_loss: 9.4256e-04\n","Epoch 309/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3146e-04 - val_loss: 8.5065e-04\n","Epoch 310/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4042e-04 - val_loss: 7.1459e-04\n","Epoch 311/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3226e-04 - val_loss: 9.9771e-04\n","Epoch 312/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.7170e-04 - val_loss: 9.5075e-04\n","Epoch 313/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9610e-04 - val_loss: 8.9708e-04\n","Epoch 314/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.9209e-04 - val_loss: 7.9599e-04\n","Epoch 315/1000\n","11/11 [==============================] - 0s 10ms/step - loss: 6.3899e-04 - val_loss: 7.1535e-04\n","Epoch 316/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.8811e-04 - val_loss: 8.9304e-04\n","Epoch 317/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7868e-04 - val_loss: 9.3487e-04\n","Epoch 318/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4081e-04 - val_loss: 6.1112e-04\n","Epoch 319/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.5694e-04 - val_loss: 9.6605e-04\n","Epoch 320/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.9648e-04 - val_loss: 8.5218e-04\n","Epoch 321/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0017 - val_loss: 8.0764e-04\n","Epoch 322/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0012 - val_loss: 0.0012\n","Epoch 323/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 8.7978e-04\n","Epoch 324/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4906e-04 - val_loss: 6.9477e-04\n","Epoch 325/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.5229e-04 - val_loss: 7.2928e-04\n","Epoch 326/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7470e-04 - val_loss: 0.0011\n","Epoch 327/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4597e-04 - val_loss: 6.4487e-04\n","Epoch 328/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9554e-04 - val_loss: 0.0010\n","Epoch 329/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5358e-04 - val_loss: 8.4146e-04\n","Epoch 330/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.1515e-04 - val_loss: 8.2508e-04\n","Epoch 331/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 7.3354e-04\n","Epoch 332/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2232e-04 - val_loss: 8.6197e-04\n","Epoch 333/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.6552e-04 - val_loss: 6.3779e-04\n","Epoch 334/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0437e-04 - val_loss: 8.2265e-04\n","Epoch 335/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.5915e-04 - val_loss: 8.5887e-04\n","Epoch 336/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6815e-04 - val_loss: 8.6122e-04\n","Epoch 337/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.5298e-04 - val_loss: 0.0011\n","Epoch 338/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4690e-04 - val_loss: 8.0606e-04\n","Epoch 339/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.0854e-04 - val_loss: 6.9534e-04\n","Epoch 340/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 8.9254e-04\n","Epoch 341/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8127e-04 - val_loss: 7.5536e-04\n","Epoch 342/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6074e-04 - val_loss: 7.3861e-04\n","Epoch 343/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9969e-04 - val_loss: 0.0010\n","Epoch 344/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1559e-04 - val_loss: 8.9208e-04\n","Epoch 345/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4107e-04 - val_loss: 6.8172e-04\n","Epoch 346/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2237e-04 - val_loss: 0.0014\n","Epoch 347/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.8387e-04 - val_loss: 8.7664e-04\n","Epoch 348/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0022 - val_loss: 9.0001e-04\n","Epoch 349/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0015\n","Epoch 350/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.6968e-04 - val_loss: 8.5301e-04\n","Epoch 351/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4107e-04 - val_loss: 7.4485e-04\n","Epoch 352/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1148e-04 - val_loss: 0.0014\n","Epoch 353/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3938e-04 - val_loss: 6.9452e-04\n","Epoch 354/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 7.9856e-04 - val_loss: 6.7430e-04\n","Epoch 355/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.7731e-04 - val_loss: 0.0015\n","Epoch 356/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.9280e-04 - val_loss: 6.2344e-04\n","Epoch 357/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 7.0427e-04\n","Epoch 358/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8725e-04 - val_loss: 9.8892e-04\n","Epoch 359/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4195e-04 - val_loss: 6.5644e-04\n","Epoch 360/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0688e-04 - val_loss: 0.0010\n","Epoch 361/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.4706e-04 - val_loss: 7.8471e-04\n","Epoch 362/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 8.3547e-04 - val_loss: 8.0616e-04\n","Epoch 363/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.5484e-04 - val_loss: 0.0014\n","Epoch 364/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7158e-04 - val_loss: 7.3364e-04\n","Epoch 365/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7948e-04 - val_loss: 9.7201e-04\n","Epoch 366/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.6849e-04 - val_loss: 7.3722e-04\n","Epoch 367/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2509e-04 - val_loss: 8.2716e-04\n","Epoch 368/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6180e-04 - val_loss: 0.0011\n","Epoch 369/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8780e-04 - val_loss: 9.7463e-04\n","Epoch 370/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3024e-04 - val_loss: 9.6210e-04\n","Epoch 371/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.2672e-04 - val_loss: 5.8601e-04\n","Epoch 372/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5737e-04 - val_loss: 0.0014\n","Epoch 373/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0678e-04 - val_loss: 7.3750e-04\n","Epoch 374/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.5724e-04 - val_loss: 6.1890e-04\n","Epoch 375/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 8.4114e-04\n","Epoch 376/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8093e-04 - val_loss: 6.1956e-04\n","Epoch 377/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0031 - val_loss: 0.0025\n","Epoch 378/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0021 - val_loss: 7.8848e-04\n","Epoch 379/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.3232e-04 - val_loss: 9.1014e-04\n","Epoch 380/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2018e-04 - val_loss: 8.9551e-04\n","Epoch 381/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.3794e-04 - val_loss: 0.0011\n","Epoch 382/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.2912e-04 - val_loss: 0.0010\n","Epoch 383/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3638e-04 - val_loss: 8.9131e-04\n","Epoch 384/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.1477e-04 - val_loss: 0.0010\n","Epoch 385/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.1980e-04 - val_loss: 5.9711e-04\n","Epoch 386/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.5405e-04 - val_loss: 0.0013\n","Epoch 387/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.7203e-04 - val_loss: 8.6622e-04\n","Epoch 388/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.2591e-04 - val_loss: 8.0612e-04\n","Epoch 389/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8669e-04 - val_loss: 7.9959e-04\n","Epoch 390/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4495e-04 - val_loss: 9.3526e-04\n","Epoch 391/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9981e-04 - val_loss: 6.6443e-04\n","Epoch 392/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 0.0011\n","Epoch 393/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7378e-04 - val_loss: 6.3784e-04\n","Epoch 394/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0017 - val_loss: 0.0017\n","Epoch 395/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 8.5533e-04\n","Epoch 396/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.3321e-04 - val_loss: 6.8470e-04\n","Epoch 397/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.5229e-04 - val_loss: 0.0010\n","Epoch 398/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2766e-04 - val_loss: 0.0010\n","Epoch 399/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0619e-04 - val_loss: 0.0011\n","Epoch 400/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4993e-04 - val_loss: 6.3144e-04\n","Epoch 401/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6745e-04 - val_loss: 9.9709e-04\n","Epoch 402/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1583e-04 - val_loss: 8.3276e-04\n","Epoch 403/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3514e-04 - val_loss: 9.1197e-04\n","Epoch 404/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4138e-04 - val_loss: 8.8199e-04\n","Epoch 405/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6872e-04 - val_loss: 0.0012\n","Epoch 406/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9399e-04 - val_loss: 6.1516e-04\n","Epoch 407/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1338e-04 - val_loss: 0.0010\n","Epoch 408/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6175e-04 - val_loss: 7.8238e-04\n","Epoch 409/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0469e-04 - val_loss: 8.1120e-04\n","Epoch 410/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.2866e-04 - val_loss: 6.2616e-04\n","Epoch 411/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9174e-04 - val_loss: 9.2678e-04\n","Epoch 412/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0003e-04 - val_loss: 7.0501e-04\n","Epoch 413/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6412e-04 - val_loss: 7.1709e-04\n","Epoch 414/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8290e-04 - val_loss: 0.0011\n","Epoch 415/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0244e-04 - val_loss: 6.1782e-04\n","Epoch 416/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8665e-04 - val_loss: 0.0011\n","Epoch 417/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5239e-04 - val_loss: 6.9611e-04\n","Epoch 418/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.2514e-04 - val_loss: 7.5627e-04\n","Epoch 419/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3990e-04 - val_loss: 0.0011\n","Epoch 420/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4153e-04 - val_loss: 6.0054e-04\n","Epoch 421/1000\n","11/11 [==============================] - 0s 15ms/step - loss: 8.0670e-04 - val_loss: 0.0012\n","Epoch 422/1000\n","11/11 [==============================] - 0s 11ms/step - loss: 6.9304e-04 - val_loss: 8.5727e-04\n","Epoch 423/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 6.8726e-04 - val_loss: 6.6863e-04\n","Epoch 424/1000\n","11/11 [==============================] - 0s 21ms/step - loss: 8.2515e-04 - val_loss: 9.8730e-04\n","Epoch 425/1000\n","11/11 [==============================] - 0s 17ms/step - loss: 6.0369e-04 - val_loss: 7.6518e-04\n","Epoch 426/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 7.8335e-04 - val_loss: 6.1530e-04\n","Epoch 427/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0269e-04 - val_loss: 0.0011\n","Epoch 428/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9392e-04 - val_loss: 9.5219e-04\n","Epoch 429/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3754e-04 - val_loss: 6.6808e-04\n","Epoch 430/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7687e-04 - val_loss: 9.1366e-04\n","Epoch 431/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7764e-04 - val_loss: 6.3562e-04\n","Epoch 432/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.1666e-04 - val_loss: 8.0917e-04\n","Epoch 433/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4553e-04 - val_loss: 0.0011\n","Epoch 434/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3624e-04 - val_loss: 5.6442e-04\n","Epoch 435/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6084e-04 - val_loss: 7.3587e-04\n","Epoch 436/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0828e-04 - val_loss: 9.1274e-04\n","Epoch 437/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 9.3479e-04 - val_loss: 6.1894e-04\n","Epoch 438/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 8.0701e-04 - val_loss: 9.1289e-04\n","Epoch 439/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.8789e-04 - val_loss: 9.8419e-04\n","Epoch 440/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.8248e-04 - val_loss: 6.4734e-04\n","Epoch 441/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4243e-04 - val_loss: 0.0011\n","Epoch 442/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.5374e-04 - val_loss: 9.2521e-04\n","Epoch 443/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.4375e-04 - val_loss: 8.4122e-04\n","Epoch 444/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.1583e-04 - val_loss: 7.9151e-04\n","Epoch 445/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7031e-04 - val_loss: 5.9631e-04\n","Epoch 446/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.1023e-04 - val_loss: 0.0010\n","Epoch 447/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.1433e-04 - val_loss: 8.6258e-04\n","Epoch 448/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4485e-04 - val_loss: 8.9525e-04\n","Epoch 449/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.2401e-04 - val_loss: 7.6776e-04\n","Epoch 450/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.5298e-04 - val_loss: 7.1382e-04\n","Epoch 451/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.5809e-04 - val_loss: 7.4235e-04\n","Epoch 452/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8117e-04 - val_loss: 7.3932e-04\n","Epoch 453/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.1635e-04 - val_loss: 7.0986e-04\n","Epoch 454/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7210e-04 - val_loss: 6.2289e-04\n","Epoch 455/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8156e-04 - val_loss: 0.0010\n","Epoch 456/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.5857e-04 - val_loss: 6.6763e-04\n","Epoch 457/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.1614e-04 - val_loss: 8.9373e-04\n","Epoch 458/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.0842e-04 - val_loss: 8.4222e-04\n","Epoch 459/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.7656e-04 - val_loss: 6.2364e-04\n","Epoch 460/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.6583e-04 - val_loss: 6.4139e-04\n","Epoch 461/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.3333e-04 - val_loss: 9.2312e-04\n","Epoch 462/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8725e-04 - val_loss: 7.5689e-04\n","Epoch 463/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.3126e-04 - val_loss: 8.5499e-04\n","Epoch 464/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8181e-04 - val_loss: 6.0882e-04\n","Epoch 465/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9264e-04 - val_loss: 6.7829e-04\n","Epoch 466/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4327e-04 - val_loss: 0.0013\n","Epoch 467/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.6029e-04 - val_loss: 5.5987e-04\n","Epoch 468/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.1866e-04 - val_loss: 0.0012\n","Epoch 469/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.1286e-04 - val_loss: 7.3989e-04\n","Epoch 470/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3171e-04 - val_loss: 9.6761e-04\n","Epoch 471/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4646e-04 - val_loss: 6.0116e-04\n","Epoch 472/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3625e-04 - val_loss: 0.0012\n","Epoch 473/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 6.0463e-04\n","Epoch 474/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7783e-04 - val_loss: 7.2564e-04\n","Epoch 475/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 5.7072e-04 - val_loss: 6.9449e-04\n","Epoch 476/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.1377e-04 - val_loss: 9.6675e-04\n","Epoch 477/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5337e-04 - val_loss: 9.0614e-04\n","Epoch 478/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8596e-04 - val_loss: 7.5594e-04\n","Epoch 479/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.8013e-04 - val_loss: 9.2699e-04\n","Epoch 480/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.3257e-04 - val_loss: 6.2474e-04\n","Epoch 481/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8574e-04 - val_loss: 0.0012\n","Epoch 482/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.5819e-04 - val_loss: 6.1129e-04\n","Epoch 483/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9610e-04 - val_loss: 6.1784e-04\n","Epoch 484/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8400e-04 - val_loss: 7.7333e-04\n","Epoch 485/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.0017\n","Epoch 486/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 6.5198e-04\n","Epoch 487/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4139e-04 - val_loss: 9.8362e-04\n","Epoch 488/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2846e-04 - val_loss: 7.3977e-04\n","Epoch 489/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9871e-04 - val_loss: 6.7210e-04\n","Epoch 490/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.2548e-04 - val_loss: 9.0291e-04\n","Epoch 491/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8847e-04 - val_loss: 6.3395e-04\n","Epoch 492/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3884e-04 - val_loss: 6.4790e-04\n","Epoch 493/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.9880e-04 - val_loss: 0.0011\n","Epoch 494/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7310e-04 - val_loss: 0.0018\n","Epoch 495/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0016 - val_loss: 8.6783e-04\n","Epoch 496/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 9.4932e-04\n","Epoch 497/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.5761e-04 - val_loss: 0.0012\n","Epoch 498/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.8847e-04 - val_loss: 6.1502e-04\n","Epoch 499/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.8458e-04 - val_loss: 7.4058e-04\n","Epoch 500/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8124e-04 - val_loss: 0.0011\n","Epoch 501/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6846e-04 - val_loss: 6.1487e-04\n","Epoch 502/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0699e-04 - val_loss: 6.1969e-04\n","Epoch 503/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1121e-04 - val_loss: 5.8578e-04\n","Epoch 504/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6983e-04 - val_loss: 0.0010\n","Epoch 505/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3352e-04 - val_loss: 6.1686e-04\n","Epoch 506/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0518e-04 - val_loss: 9.7541e-04\n","Epoch 507/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0903e-04 - val_loss: 8.3991e-04\n","Epoch 508/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8803e-04 - val_loss: 9.5181e-04\n","Epoch 509/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8059e-04 - val_loss: 5.8152e-04\n","Epoch 510/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2654e-04 - val_loss: 9.6153e-04\n","Epoch 511/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4101e-04 - val_loss: 0.0013\n","Epoch 512/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7325e-04 - val_loss: 6.0779e-04\n","Epoch 513/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0792e-04 - val_loss: 5.7885e-04\n","Epoch 514/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3230e-04 - val_loss: 9.2150e-04\n","Epoch 515/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2226e-04 - val_loss: 7.6943e-04\n","Epoch 516/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.9603e-04 - val_loss: 6.3476e-04\n","Epoch 517/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.6830e-04 - val_loss: 6.4626e-04\n","Epoch 518/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1153e-04 - val_loss: 8.9088e-04\n","Epoch 519/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4554e-04 - val_loss: 9.4769e-04\n","Epoch 520/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.4554e-04 - val_loss: 6.5920e-04\n","Epoch 521/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0359e-04 - val_loss: 8.4410e-04\n","Epoch 522/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.6117e-04 - val_loss: 7.0611e-04\n","Epoch 523/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2164e-04 - val_loss: 8.9814e-04\n","Epoch 524/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0015\n","Epoch 525/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0874e-04 - val_loss: 6.9070e-04\n","Epoch 526/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0168e-04 - val_loss: 9.1405e-04\n","Epoch 527/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4875e-04 - val_loss: 0.0015\n","Epoch 528/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 8.5154e-04\n","Epoch 529/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.0014\n","Epoch 530/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3960e-04 - val_loss: 0.0011\n","Epoch 531/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.5283e-04 - val_loss: 6.1202e-04\n","Epoch 532/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8647e-04 - val_loss: 6.1846e-04\n","Epoch 533/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 0.0018\n","Epoch 534/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 7.1560e-04\n","Epoch 535/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7183e-04 - val_loss: 0.0019\n","Epoch 536/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4105e-04 - val_loss: 6.3060e-04\n","Epoch 537/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.5596e-04 - val_loss: 9.5847e-04\n","Epoch 538/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1521e-04 - val_loss: 7.1652e-04\n","Epoch 539/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5317e-04 - val_loss: 9.1922e-04\n","Epoch 540/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.5498e-04 - val_loss: 6.5208e-04\n","Epoch 541/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.0616e-04 - val_loss: 8.3281e-04\n","Epoch 542/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.9564e-04 - val_loss: 5.6337e-04\n","Epoch 543/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0024 - val_loss: 0.0023\n","Epoch 544/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 6.3990e-04\n","Epoch 545/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 9.1177e-04 - val_loss: 8.4521e-04\n","Epoch 546/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.8186e-04 - val_loss: 8.4238e-04\n","Epoch 547/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7326e-04 - val_loss: 8.1993e-04\n","Epoch 548/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9821e-04 - val_loss: 6.4142e-04\n","Epoch 549/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0564e-04 - val_loss: 8.1252e-04\n","Epoch 550/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1889e-04 - val_loss: 7.0402e-04\n","Epoch 551/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1142e-04 - val_loss: 8.9827e-04\n","Epoch 552/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7449e-04 - val_loss: 8.2953e-04\n","Epoch 553/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.3258e-04 - val_loss: 8.3335e-04\n","Epoch 554/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2594e-04 - val_loss: 7.6730e-04\n","Epoch 555/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 6.7265e-04\n","Epoch 556/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 0.0011\n","Epoch 557/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 6.3715e-04\n","Epoch 558/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3741e-04 - val_loss: 8.0824e-04\n","Epoch 559/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.6960e-04 - val_loss: 9.8136e-04\n","Epoch 560/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8845e-04 - val_loss: 9.2188e-04\n","Epoch 561/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2814e-04 - val_loss: 0.0011\n","Epoch 562/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0253e-04 - val_loss: 5.7844e-04\n","Epoch 563/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1475e-04 - val_loss: 7.6314e-04\n","Epoch 564/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1044e-04 - val_loss: 6.9058e-04\n","Epoch 565/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.9260e-04 - val_loss: 0.0010\n","Epoch 566/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.1646e-04 - val_loss: 6.5421e-04\n","Epoch 567/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.0200e-04 - val_loss: 6.5289e-04\n","Epoch 568/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5699e-04 - val_loss: 8.2044e-04\n","Epoch 569/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7601e-04 - val_loss: 6.3548e-04\n","Epoch 570/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3118e-04 - val_loss: 8.2604e-04\n","Epoch 571/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4260e-04 - val_loss: 7.3535e-04\n","Epoch 572/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8676e-04 - val_loss: 7.2352e-04\n","Epoch 573/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 6.5638e-04\n","Epoch 574/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4078e-04 - val_loss: 7.1869e-04\n","Epoch 575/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4086e-04 - val_loss: 0.0013\n","Epoch 576/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0776e-04 - val_loss: 6.7496e-04\n","Epoch 577/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5722e-04 - val_loss: 7.2526e-04\n","Epoch 578/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.7704e-04 - val_loss: 8.1857e-04\n","Epoch 579/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0312e-04 - val_loss: 0.0011\n","Epoch 580/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7487e-04 - val_loss: 9.4236e-04\n","Epoch 581/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3213e-04 - val_loss: 5.6237e-04\n","Epoch 582/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.0363e-04 - val_loss: 9.7629e-04\n","Epoch 583/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8226e-04 - val_loss: 5.9724e-04\n","Epoch 584/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7442e-04 - val_loss: 0.0010\n","Epoch 585/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.5218e-04 - val_loss: 6.3552e-04\n","Epoch 586/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.0281e-04 - val_loss: 5.6616e-04\n","Epoch 587/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 9.3779e-04\n","Epoch 588/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5004e-04 - val_loss: 0.0010\n","Epoch 589/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3774e-04 - val_loss: 0.0010\n","Epoch 590/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7241e-04 - val_loss: 6.5287e-04\n","Epoch 591/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6739e-04 - val_loss: 8.7230e-04\n","Epoch 592/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6561e-04 - val_loss: 5.8151e-04\n","Epoch 593/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0830e-04 - val_loss: 0.0011\n","Epoch 594/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.0594e-04 - val_loss: 6.6746e-04\n","Epoch 595/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4019e-04 - val_loss: 8.9013e-04\n","Epoch 596/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8584e-04 - val_loss: 9.0506e-04\n","Epoch 597/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8388e-04 - val_loss: 8.8541e-04\n","Epoch 598/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3420e-04 - val_loss: 9.3758e-04\n","Epoch 599/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.6149e-04 - val_loss: 6.1652e-04\n","Epoch 600/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.8509e-04 - val_loss: 0.0011\n","Epoch 601/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 4.9119e-04 - val_loss: 5.6815e-04\n","Epoch 602/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.6750e-04 - val_loss: 0.0014\n","Epoch 603/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2510e-04 - val_loss: 7.4569e-04\n","Epoch 604/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9930e-04 - val_loss: 7.3995e-04\n","Epoch 605/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.8704e-04 - val_loss: 7.7367e-04\n","Epoch 606/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0159e-04 - val_loss: 7.1729e-04\n","Epoch 607/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1353e-04 - val_loss: 7.4197e-04\n","Epoch 608/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7742e-04 - val_loss: 8.4236e-04\n","Epoch 609/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.3709e-04 - val_loss: 7.1807e-04\n","Epoch 610/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 5.8329e-04 - val_loss: 0.0011\n","Epoch 611/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6278e-04 - val_loss: 7.1809e-04\n","Epoch 612/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2040e-04 - val_loss: 5.9582e-04\n","Epoch 613/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.2410e-04 - val_loss: 0.0015\n","Epoch 614/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 7.0337e-04\n","Epoch 615/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 7.5886e-04\n","Epoch 616/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.3924e-04 - val_loss: 0.0011\n","Epoch 617/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 4.9967e-04 - val_loss: 7.5942e-04\n","Epoch 618/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8612e-04 - val_loss: 9.5146e-04\n","Epoch 619/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8240e-04 - val_loss: 7.7666e-04\n","Epoch 620/1000\n","11/11 [==============================] - 0s 10ms/step - loss: 5.7713e-04 - val_loss: 7.4507e-04\n","Epoch 621/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2896e-04 - val_loss: 6.7053e-04\n","Epoch 622/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3832e-04 - val_loss: 6.7497e-04\n","Epoch 623/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0872e-04 - val_loss: 0.0014\n","Epoch 624/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4943e-04 - val_loss: 6.1062e-04\n","Epoch 625/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.9839e-04 - val_loss: 0.0011\n","Epoch 626/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0414e-04 - val_loss: 7.9747e-04\n","Epoch 627/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8277e-04 - val_loss: 6.3262e-04\n","Epoch 628/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0033e-04 - val_loss: 0.0013\n","Epoch 629/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.4196e-04 - val_loss: 6.3021e-04\n","Epoch 630/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9500e-04 - val_loss: 9.8272e-04\n","Epoch 631/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7269e-04 - val_loss: 8.1164e-04\n","Epoch 632/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6739e-04 - val_loss: 6.8839e-04\n","Epoch 633/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4588e-04 - val_loss: 6.5889e-04\n","Epoch 634/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 4.6506e-04 - val_loss: 5.7339e-04\n","Epoch 635/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.6632e-04 - val_loss: 9.7395e-04\n","Epoch 636/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.6758e-04 - val_loss: 9.0054e-04\n","Epoch 637/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6662e-04 - val_loss: 8.7963e-04\n","Epoch 638/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.6162e-04 - val_loss: 9.7425e-04\n","Epoch 639/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 8.2782e-04\n","Epoch 640/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6854e-04 - val_loss: 0.0011\n","Epoch 641/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4668e-04 - val_loss: 7.4230e-04\n","Epoch 642/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.1457e-04 - val_loss: 8.0688e-04\n","Epoch 643/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8574e-04 - val_loss: 9.5546e-04\n","Epoch 644/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3154e-04 - val_loss: 5.8260e-04\n","Epoch 645/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0061e-04 - val_loss: 0.0011\n","Epoch 646/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2702e-04 - val_loss: 9.4109e-04\n","Epoch 647/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5780e-04 - val_loss: 6.8750e-04\n","Epoch 648/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6196e-04 - val_loss: 0.0012\n","Epoch 649/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1560e-04 - val_loss: 9.0507e-04\n","Epoch 650/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.7837e-04 - val_loss: 8.4876e-04\n","Epoch 651/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5115e-04 - val_loss: 7.3597e-04\n","Epoch 652/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2393e-04 - val_loss: 7.3932e-04\n","Epoch 653/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.0278e-04 - val_loss: 6.2151e-04\n","Epoch 654/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2443e-04 - val_loss: 0.0015\n","Epoch 655/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 6.2880e-04\n","Epoch 656/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3936e-04 - val_loss: 9.7608e-04\n","Epoch 657/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8393e-04 - val_loss: 0.0015\n","Epoch 658/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9471e-04 - val_loss: 8.6381e-04\n","Epoch 659/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 6.6889e-04\n","Epoch 660/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0010\n","Epoch 661/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 0.0010\n","Epoch 662/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9422e-04 - val_loss: 8.4263e-04\n","Epoch 663/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7467e-04 - val_loss: 0.0012\n","Epoch 664/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7452e-04 - val_loss: 6.1112e-04\n","Epoch 665/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8560e-04 - val_loss: 6.8199e-04\n","Epoch 666/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.3659e-04 - val_loss: 0.0012\n","Epoch 667/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7637e-04 - val_loss: 0.0011\n","Epoch 668/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1398e-04 - val_loss: 6.1733e-04\n","Epoch 669/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.5325e-04 - val_loss: 8.7494e-04\n","Epoch 670/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4629e-04 - val_loss: 9.3693e-04\n","Epoch 671/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2472e-04 - val_loss: 6.5109e-04\n","Epoch 672/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.1127e-04 - val_loss: 0.0013\n","Epoch 673/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0910e-04 - val_loss: 7.2208e-04\n","Epoch 674/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8313e-04 - val_loss: 5.6256e-04\n","Epoch 675/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.7756e-04 - val_loss: 0.0017\n","Epoch 676/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 7.7023e-04\n","Epoch 677/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0011\n","Epoch 678/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6793e-04 - val_loss: 0.0011\n","Epoch 679/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 7.6637e-04\n","Epoch 680/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.1841e-04 - val_loss: 9.8968e-04\n","Epoch 681/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.9939e-04 - val_loss: 6.4371e-04\n","Epoch 682/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0505e-04 - val_loss: 0.0010\n","Epoch 683/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 6.1371e-04\n","Epoch 684/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.0530e-04 - val_loss: 0.0011\n","Epoch 685/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5398e-04 - val_loss: 7.9891e-04\n","Epoch 686/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1184e-04 - val_loss: 6.0767e-04\n","Epoch 687/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7714e-04 - val_loss: 7.4487e-04\n","Epoch 688/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0684e-04 - val_loss: 7.2705e-04\n","Epoch 689/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3278e-04 - val_loss: 7.4709e-04\n","Epoch 690/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1840e-04 - val_loss: 0.0012\n","Epoch 691/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0805e-04 - val_loss: 5.7225e-04\n","Epoch 692/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7808e-04 - val_loss: 9.4997e-04\n","Epoch 693/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7982e-04 - val_loss: 7.5665e-04\n","Epoch 694/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.4393e-04 - val_loss: 7.1223e-04\n","Epoch 695/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4842e-04 - val_loss: 7.5520e-04\n","Epoch 696/1000\n","11/11 [==============================] - 0s 10ms/step - loss: 8.8833e-04 - val_loss: 8.6330e-04\n","Epoch 697/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.6090e-04 - val_loss: 6.5753e-04\n","Epoch 698/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2920e-04 - val_loss: 8.0093e-04\n","Epoch 699/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.1226e-04 - val_loss: 9.5365e-04\n","Epoch 700/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.2374e-04 - val_loss: 5.9725e-04\n","Epoch 701/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2513e-04 - val_loss: 0.0012\n","Epoch 702/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.2963e-04 - val_loss: 5.6037e-04\n","Epoch 703/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 8.0924e-04 - val_loss: 9.8589e-04\n","Epoch 704/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.1148e-04 - val_loss: 7.8524e-04\n","Epoch 705/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.1735e-04 - val_loss: 8.0294e-04\n","Epoch 706/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9770e-04 - val_loss: 0.0011\n","Epoch 707/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5128e-04 - val_loss: 9.5345e-04\n","Epoch 708/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6228e-04 - val_loss: 7.3817e-04\n","Epoch 709/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4463e-04 - val_loss: 8.7235e-04\n","Epoch 710/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9608e-04 - val_loss: 7.0385e-04\n","Epoch 711/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7067e-04 - val_loss: 8.1276e-04\n","Epoch 712/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 4.9554e-04 - val_loss: 8.5798e-04\n","Epoch 713/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8934e-04 - val_loss: 9.1366e-04\n","Epoch 714/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9577e-04 - val_loss: 7.6571e-04\n","Epoch 715/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.0994e-04 - val_loss: 7.4626e-04\n","Epoch 716/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5485e-04 - val_loss: 7.2604e-04\n","Epoch 717/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8296e-04 - val_loss: 8.9005e-04\n","Epoch 718/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.3818e-04 - val_loss: 5.7739e-04\n","Epoch 719/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7370e-04 - val_loss: 0.0011\n","Epoch 720/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6999e-04 - val_loss: 7.2954e-04\n","Epoch 721/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8984e-04 - val_loss: 0.0014\n","Epoch 722/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.7124e-04 - val_loss: 7.0915e-04\n","Epoch 723/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.9628e-04 - val_loss: 8.7427e-04\n","Epoch 724/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.2451e-04 - val_loss: 9.3175e-04\n","Epoch 725/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.9334e-04 - val_loss: 7.0661e-04\n","Epoch 726/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4790e-04 - val_loss: 7.0113e-04\n","Epoch 727/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.5866e-04 - val_loss: 7.2209e-04\n","Epoch 728/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.7983e-04 - val_loss: 0.0010\n","Epoch 729/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 6.1206e-04\n","Epoch 730/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.6839e-04 - val_loss: 0.0011\n","Epoch 731/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8419e-04 - val_loss: 6.8079e-04\n","Epoch 732/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 4.7104e-04 - val_loss: 8.8338e-04\n","Epoch 733/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0571e-04 - val_loss: 8.8240e-04\n","Epoch 734/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.6148e-04 - val_loss: 6.1355e-04\n","Epoch 735/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.7868e-04 - val_loss: 7.5448e-04\n","Epoch 736/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0015\n","Epoch 737/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.9469e-04 - val_loss: 5.8739e-04\n","Epoch 738/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0384e-04 - val_loss: 6.7278e-04\n","Epoch 739/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8959e-04 - val_loss: 0.0014\n","Epoch 740/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 5.7209e-04\n","Epoch 741/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.9715e-04 - val_loss: 7.5614e-04\n","Epoch 742/1000\n","11/11 [==============================] - 0s 11ms/step - loss: 7.2224e-04 - val_loss: 7.7306e-04\n","Epoch 743/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.5872e-04 - val_loss: 8.9347e-04\n","Epoch 744/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.2629e-04 - val_loss: 8.3313e-04\n","Epoch 745/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 6.6150e-04\n","Epoch 746/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2394e-04 - val_loss: 0.0011\n","Epoch 747/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 6.3262e-04\n","Epoch 748/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 6.0796e-04\n","Epoch 749/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0217e-04 - val_loss: 6.3930e-04\n","Epoch 750/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.6404e-04 - val_loss: 8.6182e-04\n","Epoch 751/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7762e-04 - val_loss: 0.0011\n","Epoch 752/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 7.3120e-04\n","Epoch 753/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.6926e-04 - val_loss: 9.2476e-04\n","Epoch 754/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9526e-04 - val_loss: 9.9736e-04\n","Epoch 755/1000\n","11/11 [==============================] - 0s 6ms/step - loss: 0.0011 - val_loss: 7.1012e-04\n","Epoch 756/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0010\n","Epoch 757/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7680e-04 - val_loss: 6.2140e-04\n","Epoch 758/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9708e-04 - val_loss: 0.0011\n","Epoch 759/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.5568e-04 - val_loss: 8.1869e-04\n","Epoch 760/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4304e-04 - val_loss: 7.6910e-04\n","Epoch 761/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.1480e-04 - val_loss: 7.0495e-04\n","Epoch 762/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4475e-04 - val_loss: 8.9309e-04\n","Epoch 763/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6647e-04 - val_loss: 6.4586e-04\n","Epoch 764/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 6.6694e-04\n","Epoch 765/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3259e-04 - val_loss: 8.8644e-04\n","Epoch 766/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.9720e-04 - val_loss: 7.3632e-04\n","Epoch 767/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.7274e-04 - val_loss: 8.5874e-04\n","Epoch 768/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3486e-04 - val_loss: 7.6888e-04\n","Epoch 769/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2667e-04 - val_loss: 7.9981e-04\n","Epoch 770/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.8943e-04 - val_loss: 7.4217e-04\n","Epoch 771/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.2546e-04 - val_loss: 9.2218e-04\n","Epoch 772/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4392e-04 - val_loss: 6.2515e-04\n","Epoch 773/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4485e-04 - val_loss: 0.0010\n","Epoch 774/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.3354e-04 - val_loss: 6.8264e-04\n","Epoch 775/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.2393e-04 - val_loss: 5.6590e-04\n","Epoch 776/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9573e-04 - val_loss: 9.1692e-04\n","Epoch 777/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.3587e-04 - val_loss: 0.0011\n","Epoch 778/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3565e-04 - val_loss: 8.0210e-04\n","Epoch 779/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5752e-04 - val_loss: 6.8589e-04\n","Epoch 780/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 4.9016e-04 - val_loss: 9.3137e-04\n","Epoch 781/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9354e-04 - val_loss: 9.0087e-04\n","Epoch 782/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.0667e-04 - val_loss: 7.8194e-04\n","Epoch 783/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5728e-04 - val_loss: 7.0067e-04\n","Epoch 784/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.8145e-04 - val_loss: 0.0013\n","Epoch 785/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.4524e-04 - val_loss: 9.0485e-04\n","Epoch 786/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.0217e-04 - val_loss: 6.3076e-04\n","Epoch 787/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5083e-04 - val_loss: 0.0014\n","Epoch 788/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 5.7682e-04\n","Epoch 789/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.5861e-04 - val_loss: 0.0017\n","Epoch 790/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 7.3947e-04\n","Epoch 791/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2631e-04 - val_loss: 9.0452e-04\n","Epoch 792/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.7595e-04 - val_loss: 9.0002e-04\n","Epoch 793/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.9081e-04 - val_loss: 5.7689e-04\n","Epoch 794/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8968e-04 - val_loss: 0.0013\n","Epoch 795/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.5883e-04 - val_loss: 0.0010\n","Epoch 796/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 6.1651e-04\n","Epoch 797/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 9.6964e-04 - val_loss: 0.0019\n","Epoch 798/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0010 - val_loss: 6.3210e-04\n","Epoch 799/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.9823e-04 - val_loss: 0.0011\n","Epoch 800/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4962e-04 - val_loss: 6.7291e-04\n","Epoch 801/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9319e-04 - val_loss: 6.2250e-04\n","Epoch 802/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7413e-04 - val_loss: 0.0014\n","Epoch 803/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8416e-04 - val_loss: 5.7884e-04\n","Epoch 804/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1456e-04 - val_loss: 9.2521e-04\n","Epoch 805/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2437e-04 - val_loss: 8.0239e-04\n","Epoch 806/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0011 - val_loss: 5.4554e-04\n","Epoch 807/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 0.0012\n","Epoch 808/1000\n","11/11 [==============================] - 0s 14ms/step - loss: 8.9306e-04 - val_loss: 5.9281e-04\n","Epoch 809/1000\n","11/11 [==============================] - 0s 13ms/step - loss: 6.5991e-04 - val_loss: 5.6531e-04\n","Epoch 810/1000\n","11/11 [==============================] - 0s 11ms/step - loss: 7.8134e-04 - val_loss: 0.0010\n","Epoch 811/1000\n","11/11 [==============================] - 0s 16ms/step - loss: 6.9749e-04 - val_loss: 6.7325e-04\n","Epoch 812/1000\n","11/11 [==============================] - 0s 20ms/step - loss: 8.7331e-04 - val_loss: 7.9215e-04\n","Epoch 813/1000\n","11/11 [==============================] - 0s 15ms/step - loss: 6.3252e-04 - val_loss: 7.8602e-04\n","Epoch 814/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.9312e-04 - val_loss: 6.8027e-04\n","Epoch 815/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.1820e-04 - val_loss: 9.3902e-04\n","Epoch 816/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0011 - val_loss: 0.0010\n","Epoch 817/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.3583e-04 - val_loss: 7.6201e-04\n","Epoch 818/1000\n","11/11 [==============================] - 0s 11ms/step - loss: 7.0835e-04 - val_loss: 0.0013\n","Epoch 819/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.9128e-04 - val_loss: 7.6420e-04\n","Epoch 820/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.9671e-04 - val_loss: 6.8413e-04\n","Epoch 821/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8454e-04 - val_loss: 0.0011\n","Epoch 822/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2474e-04 - val_loss: 8.0224e-04\n","Epoch 823/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6758e-04 - val_loss: 5.9474e-04\n","Epoch 824/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 8.0727e-04 - val_loss: 0.0016\n","Epoch 825/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4728e-04 - val_loss: 9.6380e-04\n","Epoch 826/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.0307e-04 - val_loss: 0.0013\n","Epoch 827/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.6689e-04 - val_loss: 5.8951e-04\n","Epoch 828/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0011 - val_loss: 9.9604e-04\n","Epoch 829/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.5015e-04 - val_loss: 7.5450e-04\n","Epoch 830/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.0771e-04 - val_loss: 7.8049e-04\n","Epoch 831/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4752e-04 - val_loss: 9.7122e-04\n","Epoch 832/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6795e-04 - val_loss: 6.1062e-04\n","Epoch 833/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2944e-04 - val_loss: 0.0013\n","Epoch 834/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.5321e-04 - val_loss: 5.8670e-04\n","Epoch 835/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.3735e-04 - val_loss: 8.2476e-04\n","Epoch 836/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.5641e-04 - val_loss: 7.3186e-04\n","Epoch 837/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6754e-04 - val_loss: 7.9723e-04\n","Epoch 838/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2397e-04 - val_loss: 6.7509e-04\n","Epoch 839/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.5858e-04 - val_loss: 5.7923e-04\n","Epoch 840/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7507e-04 - val_loss: 0.0013\n","Epoch 841/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.8473e-04 - val_loss: 8.3968e-04\n","Epoch 842/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9333e-04 - val_loss: 8.5007e-04\n","Epoch 843/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.1561e-04 - val_loss: 0.0014\n","Epoch 844/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7355e-04 - val_loss: 6.7079e-04\n","Epoch 845/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1564e-04 - val_loss: 0.0012\n","Epoch 846/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3588e-04 - val_loss: 7.2192e-04\n","Epoch 847/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4340e-04 - val_loss: 6.1219e-04\n","Epoch 848/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.2198e-04 - val_loss: 9.0347e-04\n","Epoch 849/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8373e-04 - val_loss: 7.3263e-04\n","Epoch 850/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.3282e-04 - val_loss: 9.6523e-04\n","Epoch 851/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 6.4369e-04 - val_loss: 6.8662e-04\n","Epoch 852/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6565e-04 - val_loss: 5.9271e-04\n","Epoch 853/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 0.0012\n","Epoch 854/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 8.3920e-04\n","Epoch 855/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3122e-04 - val_loss: 7.1579e-04\n","Epoch 856/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2604e-04 - val_loss: 7.7526e-04\n","Epoch 857/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0292e-04 - val_loss: 7.1371e-04\n","Epoch 858/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.6979e-04 - val_loss: 6.2473e-04\n","Epoch 859/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6657e-04 - val_loss: 0.0010\n","Epoch 860/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2377e-04 - val_loss: 6.4309e-04\n","Epoch 861/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6563e-04 - val_loss: 0.0014\n","Epoch 862/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.0751e-04 - val_loss: 7.1557e-04\n","Epoch 863/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 7.1832e-04 - val_loss: 0.0010\n","Epoch 864/1000\n","11/11 [==============================] - 0s 11ms/step - loss: 0.0012 - val_loss: 0.0010\n","Epoch 865/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0013 - val_loss: 0.0014\n","Epoch 866/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5501e-04 - val_loss: 6.3897e-04\n","Epoch 867/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4653e-04 - val_loss: 0.0011\n","Epoch 868/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9887e-04 - val_loss: 5.9705e-04\n","Epoch 869/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2338e-04 - val_loss: 7.4048e-04\n","Epoch 870/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.2097e-04 - val_loss: 9.7027e-04\n","Epoch 871/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2772e-04 - val_loss: 7.2134e-04\n","Epoch 872/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.1897e-04 - val_loss: 5.4303e-04\n","Epoch 873/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7831e-04 - val_loss: 0.0017\n","Epoch 874/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1936e-04 - val_loss: 5.8377e-04\n","Epoch 875/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4694e-04 - val_loss: 8.8831e-04\n","Epoch 876/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.1524e-04 - val_loss: 8.3031e-04\n","Epoch 877/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0421e-04 - val_loss: 8.0409e-04\n","Epoch 878/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3460e-04 - val_loss: 9.0022e-04\n","Epoch 879/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0617e-04 - val_loss: 7.9391e-04\n","Epoch 880/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1675e-04 - val_loss: 7.7947e-04\n","Epoch 881/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.6242e-04 - val_loss: 6.7043e-04\n","Epoch 882/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.0971e-04 - val_loss: 0.0010\n","Epoch 883/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8172e-04 - val_loss: 8.3421e-04\n","Epoch 884/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.8565e-04 - val_loss: 7.1569e-04\n","Epoch 885/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.6225e-04 - val_loss: 7.6135e-04\n","Epoch 886/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0230e-04 - val_loss: 5.1887e-04\n","Epoch 887/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.7124e-04 - val_loss: 0.0010\n","Epoch 888/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7035e-04 - val_loss: 6.3983e-04\n","Epoch 889/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7886e-04 - val_loss: 8.6234e-04\n","Epoch 890/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1322e-04 - val_loss: 6.8611e-04\n","Epoch 891/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.0217e-04 - val_loss: 8.6876e-04\n","Epoch 892/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.4951e-04 - val_loss: 0.0010\n","Epoch 893/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.2755e-04 - val_loss: 0.0011\n","Epoch 894/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0011 - val_loss: 6.2518e-04\n","Epoch 895/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.8057e-04 - val_loss: 0.0014\n","Epoch 896/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 9.2632e-04 - val_loss: 8.9412e-04\n","Epoch 897/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6303e-04 - val_loss: 7.6003e-04\n","Epoch 898/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.4894e-04 - val_loss: 7.3611e-04\n","Epoch 899/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3542e-04 - val_loss: 7.8612e-04\n","Epoch 900/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.6913e-04 - val_loss: 0.0010\n","Epoch 901/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.5515e-04 - val_loss: 5.6716e-04\n","Epoch 902/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 8.8534e-04\n","Epoch 903/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.7163e-04 - val_loss: 7.6210e-04\n","Epoch 904/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2876e-04 - val_loss: 8.8930e-04\n","Epoch 905/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0411e-04 - val_loss: 7.5736e-04\n","Epoch 906/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.9380e-04 - val_loss: 6.7625e-04\n","Epoch 907/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6950e-04 - val_loss: 0.0012\n","Epoch 908/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.2074e-04 - val_loss: 6.3484e-04\n","Epoch 909/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4126e-04 - val_loss: 6.0383e-04\n","Epoch 910/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.3092e-04 - val_loss: 0.0013\n","Epoch 911/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.3022e-04 - val_loss: 9.2420e-04\n","Epoch 912/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3412e-04 - val_loss: 6.2739e-04\n","Epoch 913/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6532e-04 - val_loss: 0.0013\n","Epoch 914/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8195e-04 - val_loss: 6.9560e-04\n","Epoch 915/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.2813e-04 - val_loss: 7.3306e-04\n","Epoch 916/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1361e-04 - val_loss: 6.9176e-04\n","Epoch 917/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.2193e-04 - val_loss: 0.0011\n","Epoch 918/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3528e-04 - val_loss: 0.0011\n","Epoch 919/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.8973e-04 - val_loss: 6.8564e-04\n","Epoch 920/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8700e-04 - val_loss: 8.0745e-04\n","Epoch 921/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5472e-04 - val_loss: 9.8448e-04\n","Epoch 922/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.3067e-04 - val_loss: 7.5934e-04\n","Epoch 923/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.5167e-04 - val_loss: 7.8297e-04\n","Epoch 924/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 4.3731e-04 - val_loss: 0.0010\n","Epoch 925/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3977e-04 - val_loss: 8.0999e-04\n","Epoch 926/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8300e-04 - val_loss: 6.0695e-04\n","Epoch 927/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.9316e-04 - val_loss: 0.0011\n","Epoch 928/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2901e-04 - val_loss: 9.8439e-04\n","Epoch 929/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.2763e-04 - val_loss: 5.7308e-04\n","Epoch 930/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.9833e-04 - val_loss: 0.0011\n","Epoch 931/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5640e-04 - val_loss: 0.0010\n","Epoch 932/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.1087e-04 - val_loss: 6.6911e-04\n","Epoch 933/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3583e-04 - val_loss: 9.0230e-04\n","Epoch 934/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.4056e-04 - val_loss: 6.2277e-04\n","Epoch 935/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7653e-04 - val_loss: 0.0011\n","Epoch 936/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7009e-04 - val_loss: 5.9696e-04\n","Epoch 937/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.4264e-04 - val_loss: 0.0013\n","Epoch 938/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0010 - val_loss: 7.9220e-04\n","Epoch 939/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.0185e-04 - val_loss: 8.4575e-04\n","Epoch 940/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3036e-04 - val_loss: 9.3167e-04\n","Epoch 941/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.9853e-04 - val_loss: 7.7269e-04\n","Epoch 942/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.2789e-04 - val_loss: 7.4238e-04\n","Epoch 943/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.0641e-04 - val_loss: 8.2633e-04\n","Epoch 944/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.5637e-04 - val_loss: 7.3685e-04\n","Epoch 945/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8828e-04 - val_loss: 7.1366e-04\n","Epoch 946/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.0977e-04 - val_loss: 6.5235e-04\n","Epoch 947/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0010 - val_loss: 0.0012\n","Epoch 948/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4061e-04 - val_loss: 5.8270e-04\n","Epoch 949/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.1412e-04 - val_loss: 5.5579e-04\n","Epoch 950/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.0018\n","Epoch 951/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0012 - val_loss: 6.3223e-04\n","Epoch 952/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3491e-04 - val_loss: 8.2946e-04\n","Epoch 953/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8775e-04 - val_loss: 7.0578e-04\n","Epoch 954/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.5316e-04 - val_loss: 5.5492e-04\n","Epoch 955/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.5828e-04 - val_loss: 0.0016\n","Epoch 956/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.4417e-04 - val_loss: 6.7117e-04\n","Epoch 957/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.6416e-04 - val_loss: 9.1394e-04\n","Epoch 958/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.6156e-04 - val_loss: 8.8948e-04\n","Epoch 959/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.8890e-04 - val_loss: 9.1008e-04\n","Epoch 960/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.3141e-04 - val_loss: 0.0012\n","Epoch 961/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.7679e-04 - val_loss: 5.6978e-04\n","Epoch 962/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.1476e-04 - val_loss: 0.0011\n","Epoch 963/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.5375e-04 - val_loss: 6.3205e-04\n","Epoch 964/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8507e-04 - val_loss: 9.0420e-04\n","Epoch 965/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.9231e-04 - val_loss: 7.5507e-04\n","Epoch 966/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.7187e-04 - val_loss: 0.0012\n","Epoch 967/1000\n","11/11 [==============================] - 0s 9ms/step - loss: 5.8468e-04 - val_loss: 6.8862e-04\n","Epoch 968/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.9121e-04 - val_loss: 6.8140e-04\n","Epoch 969/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.2175e-04 - val_loss: 0.0018\n","Epoch 970/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.4511e-04 - val_loss: 6.7314e-04\n","Epoch 971/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.4113e-04 - val_loss: 8.4063e-04\n","Epoch 972/1000\n","11/11 [==============================] - 0s 10ms/step - loss: 6.4336e-04 - val_loss: 6.3235e-04\n","Epoch 973/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.9059e-04 - val_loss: 7.8621e-04\n","Epoch 974/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1840e-04 - val_loss: 0.0011\n","Epoch 975/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.9077e-04 - val_loss: 5.3767e-04\n","Epoch 976/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.7105e-04 - val_loss: 0.0011\n","Epoch 977/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.1966e-04 - val_loss: 9.0981e-04\n","Epoch 978/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.8933e-04 - val_loss: 6.5916e-04\n","Epoch 979/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 4.5049e-04 - val_loss: 0.0013\n","Epoch 980/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0998e-04 - val_loss: 5.5456e-04\n","Epoch 981/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.0319e-04 - val_loss: 7.0573e-04\n","Epoch 982/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0016 - val_loss: 0.0013\n","Epoch 983/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.0814e-04 - val_loss: 6.4810e-04\n","Epoch 984/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 8.7939e-04 - val_loss: 0.0011\n","Epoch 985/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 6.4318e-04 - val_loss: 8.1042e-04\n","Epoch 986/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 9.1048e-04 - val_loss: 8.2965e-04\n","Epoch 987/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.0765e-04 - val_loss: 9.4577e-04\n","Epoch 988/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3394e-04 - val_loss: 0.0010\n","Epoch 989/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 8.3534e-04 - val_loss: 6.0873e-04\n","Epoch 990/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 5.3906e-04 - val_loss: 5.9061e-04\n","Epoch 991/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 7.4412e-04 - val_loss: 6.6720e-04\n","Epoch 992/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 4.7356e-04 - val_loss: 7.3487e-04\n","Epoch 993/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.7865e-04 - val_loss: 5.9972e-04\n","Epoch 994/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.8705e-04 - val_loss: 0.0011\n","Epoch 995/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 5.6945e-04\n","Epoch 996/1000\n","11/11 [==============================] - 0s 12ms/step - loss: 5.1747e-04 - val_loss: 9.5918e-04\n","Epoch 997/1000\n","11/11 [==============================] - 0s 8ms/step - loss: 6.3542e-04 - val_loss: 9.9721e-04\n","Epoch 998/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.6717e-04 - val_loss: 6.2511e-04\n","Epoch 999/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 7.4831e-04 - val_loss: 9.4808e-04\n","Epoch 1000/1000\n","11/11 [==============================] - 0s 7ms/step - loss: 5.3356e-04 - val_loss: 5.9216e-04\n","Saved model to disk\n","Loaded model from disk\n"]}],"source":["fitting = True\n","fitting_save = True\n","\n","import pickle\n","\n","if fitting:\n","    history=estimator.fit(X_train,y_train, validation_data=(X_test,y_test)) # Fitting model\n","    if fitting_save:\n","        # Save model\n","        estimator.model.save('BP_saved_model.h5')\n","        print(\"Saved model to disk\")\n","        with open('history.pickle', 'wb') as f:\n","            pickle.dump(history.history, f)\n","# load model \n","from keras.models import load_model\n","estimator = KerasRegressor(build_fn=BP_model, X=X_train, epochs=epochs, batch_size=batch_size, verbose=1)\n","estimator.model = load_model('BP_saved_model.h5')    \n","with open('history.pickle', 'rb') as f:\n","    history = pickle.load(f)\n","print(\"Loaded model from disk\")"]},{"cell_type":"code","execution_count":27,"id":"aea4bff6","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:21:50.562138Z","iopub.status.busy":"2022-03-03T03:21:50.560987Z","iopub.status.idle":"2022-03-03T03:21:50.828691Z","shell.execute_reply":"2022-03-03T03:21:50.829266Z","shell.execute_reply.started":"2022-03-03T03:14:59.027226Z"},"id":"NI1esVXrjol4","papermill":{"duration":1.032466,"end_time":"2022-03-03T03:21:50.829506","exception":false,"start_time":"2022-03-03T03:21:49.79704","status":"completed"},"tags":[]},"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":["plt.figure()\n","plt.plot(history['loss'], label='train')\n","plt.plot(history['val_loss'], label='test')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend()\n","plt.show()"]},{"cell_type":"markdown","id":"7c2bbbdb","metadata":{"id":"tzi65ADDjol4","papermill":{"duration":0.762271,"end_time":"2022-03-03T03:21:52.360971","exception":false,"start_time":"2022-03-03T03:21:51.5987","status":"completed"},"tags":[]},"source":["As you can see, a Neural Network is well-fitted and no overfitting is observed.\n","Let's calculate a prediction of the training (**res_train_ANN**) an test (**res_test_ANN**) sets.\n"]},{"cell_type":"markdown","id":"99c01e18","metadata":{"id":"-OolwVvhjol4","papermill":{"duration":0.766315,"end_time":"2022-03-03T03:21:53.892825","exception":false,"start_time":"2022-03-03T03:21:53.12651","status":"completed"},"tags":[]},"source":["Let's calculate a forecast and make an inverse normalization to real scale.\n"]},{"cell_type":"code","execution_count":28,"id":"1afae3d4","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:21:55.434673Z","iopub.status.busy":"2022-03-03T03:21:55.433909Z","iopub.status.idle":"2022-03-03T03:21:55.73568Z","shell.execute_reply":"2022-03-03T03:21:55.734894Z","shell.execute_reply.started":"2022-03-03T03:14:59.229285Z"},"id":"-LbcwXiijol5","papermill":{"duration":1.075381,"end_time":"2022-03-03T03:21:55.735859","exception":false,"start_time":"2022-03-03T03:21:54.660478","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["11/11 [==============================] - 0s 2ms/step\n","5/5 [==============================] - 0s 2ms/step\n"]}],"source":["res_tr=estimator.predict(X_train)\n","res_ts=estimator.predict(X_test)\n","res_train_ANN=scaler_y.inverse_transform(res_tr.reshape(-1, 1)).flatten()\n","res_test_ANN=scaler_y.inverse_transform(res_ts.reshape(-1, 1)).flatten()"]},{"cell_type":"markdown","id":"4462fd5c","metadata":{"id":"GO--0BBwjol5","papermill":{"duration":0.844991,"end_time":"2022-03-03T03:21:57.353492","exception":false,"start_time":"2022-03-03T03:21:56.508501","status":"completed"},"tags":[]},"source":["Let's compare the accuracy of a Linear Regression and a Neural Network.\n"]},{"cell_type":"code","execution_count":29,"id":"dd5b29a6","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:21:58.893687Z","iopub.status.busy":"2022-03-03T03:21:58.89229Z","iopub.status.idle":"2022-03-03T03:21:58.902601Z","shell.execute_reply":"2022-03-03T03:21:58.903409Z","shell.execute_reply.started":"2022-03-03T03:14:59.410483Z"},"id":"0loru7LVjol6","papermill":{"duration":0.787783,"end_time":"2022-03-03T03:21:58.903667","exception":false,"start_time":"2022-03-03T03:21:58.115884","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Correlation train 0.998448607178335\n","Correlation train 0.9911922894983044\n","Mean Absolute Error: 51597.71995671751\n","Mean Squared Error: 8402095380.421753\n","Root Mean Squared Error: 91662.94442369693\n"]}],"source":["print(\"Correlation train\", np.corrcoef(res_train, res_train_ANN)[0,1])\n","print(\"Correlation train\", np.corrcoef(res_test, res_test_ANN)[0,1])\n","print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, res_test_ANN))\n","print('Mean Squared Error:', metrics.mean_squared_error(y_test, res_test_ANN))\n","print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, res_test_ANN)))"]},{"cell_type":"markdown","id":"306582af","metadata":{"id":"knCFLwJKjol7","papermill":{"duration":0.766004,"end_time":"2022-03-03T03:22:00.469011","exception":false,"start_time":"2022-03-03T03:21:59.703007","status":"completed"},"tags":[]},"source":["We got slightly better results for a Neural Network than for a Linear Regression.\n"]},{"cell_type":"markdown","id":"97e76aaa","metadata":{"id":"sh5LVW2ajol7","papermill":{"duration":0.773023,"end_time":"2022-03-03T03:22:02.007246","exception":false,"start_time":"2022-03-03T03:22:01.234223","status":"completed"},"tags":[]},"source":["# Long Short-Term Memory - LSTM\n"]},{"cell_type":"markdown","id":"77c04679","metadata":{"id":"iwRiFqZ6jol8","papermill":{"duration":0.76761,"end_time":"2022-03-03T03:22:03.545202","exception":false,"start_time":"2022-03-03T03:22:02.777592","status":"completed"},"tags":[]},"source":["Unlike standard feedforward neural networks, **LSTM** has feedback connections. It can process not only single data points, but also entire sequences of data (such as speech, video or time series).\n","\n","In case of time series, the neural network has one input and one output. However, there should be a vector of time series values for the previous time period on the input.\n"]},{"cell_type":"markdown","id":"23424f0c","metadata":{"id":"AhN3dMeLjol9","papermill":{"duration":0.770471,"end_time":"2022-03-03T03:22:05.078375","exception":false,"start_time":"2022-03-03T03:22:04.307904","status":"completed"},"tags":[]},"source":["To do this, we should transform input DataSets into 3D shape.\n"]},{"cell_type":"code","execution_count":30,"id":"1e145894","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:22:06.715521Z","iopub.status.busy":"2022-03-03T03:22:06.714712Z","iopub.status.idle":"2022-03-03T03:22:06.716251Z","shell.execute_reply":"2022-03-03T03:22:06.716903Z","shell.execute_reply.started":"2022-03-03T03:14:59.424598Z"},"id":"0qIDV-K9jol9","papermill":{"duration":0.807278,"end_time":"2022-03-03T03:22:06.717149","exception":false,"start_time":"2022-03-03T03:22:05.909871","status":"completed"},"tags":[]},"outputs":[],"source":["train_x_LSTM = X_train.reshape((X_train.shape[0], 1, 14))\n","test_x_LSTM = X_test.reshape((X_test.shape[0], 1, 14))"]},{"cell_type":"markdown","id":"9150f832","metadata":{"id":"dWhD97wRjol9","papermill":{"duration":0.84742,"end_time":"2022-03-03T03:22:08.335801","exception":false,"start_time":"2022-03-03T03:22:07.488381","status":"completed"},"tags":[]},"source":["Let's create an LSTM Neural Network consisting of one **LSTM** layer and one BP layer like in the previous case.\n","In this case our NN will consist of 7 LSTM and 7 BP neurons only.\n"]},{"cell_type":"code","execution_count":31,"id":"2bd098bc","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:22:09.873062Z","iopub.status.busy":"2022-03-03T03:22:09.872234Z","iopub.status.idle":"2022-03-03T03:22:10.225875Z","shell.execute_reply":"2022-03-03T03:22:10.225086Z","shell.execute_reply.started":"2022-03-03T03:14:59.430922Z"},"id":"76Ui9g3ajol-","papermill":{"duration":1.123101,"end_time":"2022-03-03T03:22:10.226053","exception":false,"start_time":"2022-03-03T03:22:09.102952","status":"completed"},"tags":[]},"outputs":[],"source":["from keras.layers import LSTM\n","\n","batch_size=int(y_train.shape[0]*.1)\n","model = Sequential()\n","model.add(LSTM(7, input_shape=(train_x_LSTM.shape[1], train_x_LSTM.shape[2])))\n","model.add(Dropout(0.2))\n","model.add(Dense(7, kernel_initializer='normal', activation='relu'))\n","model.add(Dropout(0.2))\n","model.add(Dense(y_train.shape[1])) #activation='sigmoid'\n","model.compile(loss='mean_squared_error', optimizer='adam')\n","\n"]},{"cell_type":"markdown","id":"b2ff791c","metadata":{"id":"IhsB8Thgjol_","papermill":{"duration":0.766265,"end_time":"2022-03-03T03:22:11.803762","exception":false,"start_time":"2022-03-03T03:22:11.037497","status":"completed"},"tags":[]},"source":["All subsequent steps of learning, storing, reading and predicting are similar to the ones we used with the previous neural network. You can see that only 420 epochs are enough for an LSTM.\n"]},{"cell_type":"code","execution_count":32,"id":"d010cd5b","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:22:13.344779Z","iopub.status.busy":"2022-03-03T03:22:13.343948Z","iopub.status.idle":"2022-03-03T03:22:56.862844Z","shell.execute_reply":"2022-03-03T03:22:56.862146Z","shell.execute_reply.started":"2022-03-03T03:14:59.681293Z"},"id":"MA8vKjjtjol_","papermill":{"duration":44.294678,"end_time":"2022-03-03T03:22:56.863022","exception":false,"start_time":"2022-03-03T03:22:12.568344","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/420\n","11/11 [==============================] - 3s 64ms/step - loss: 0.0586 - val_loss: 0.0408\n","Epoch 2/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0537 - val_loss: 0.0372\n","Epoch 3/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0491 - val_loss: 0.0341\n","Epoch 4/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0448 - val_loss: 0.0315\n","Epoch 5/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0433 - val_loss: 0.0294\n","Epoch 6/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0395 - val_loss: 0.0277\n","Epoch 7/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0393 - val_loss: 0.0263\n","Epoch 8/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0361 - val_loss: 0.0250\n","Epoch 9/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0347 - val_loss: 0.0239\n","Epoch 10/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0314 - val_loss: 0.0229\n","Epoch 11/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0318 - val_loss: 0.0219\n","Epoch 12/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0314 - val_loss: 0.0210\n","Epoch 13/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0279 - val_loss: 0.0201\n","Epoch 14/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0251 - val_loss: 0.0191\n","Epoch 15/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0264 - val_loss: 0.0182\n","Epoch 16/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0234 - val_loss: 0.0172\n","Epoch 17/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0241 - val_loss: 0.0162\n","Epoch 18/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0214 - val_loss: 0.0153\n","Epoch 19/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0174 - val_loss: 0.0144\n","Epoch 20/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0191 - val_loss: 0.0135\n","Epoch 21/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0158 - val_loss: 0.0127\n","Epoch 22/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0126 - val_loss: 0.0119\n","Epoch 23/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0157 - val_loss: 0.0112\n","Epoch 24/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0127 - val_loss: 0.0106\n","Epoch 25/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0150 - val_loss: 0.0101\n","Epoch 26/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0156 - val_loss: 0.0097\n","Epoch 27/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0119 - val_loss: 0.0093\n","Epoch 28/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0118 - val_loss: 0.0090\n","Epoch 29/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0094 - val_loss: 0.0088\n","Epoch 30/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0122 - val_loss: 0.0086\n","Epoch 31/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0128 - val_loss: 0.0084\n","Epoch 32/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0124 - val_loss: 0.0083\n","Epoch 33/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0119 - val_loss: 0.0081\n","Epoch 34/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0107 - val_loss: 0.0080\n","Epoch 35/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0109 - val_loss: 0.0079\n","Epoch 36/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0084 - val_loss: 0.0078\n","Epoch 37/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0094 - val_loss: 0.0077\n","Epoch 38/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0096 - val_loss: 0.0076\n","Epoch 39/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0078 - val_loss: 0.0075\n","Epoch 40/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0096 - val_loss: 0.0074\n","Epoch 41/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0100 - val_loss: 0.0074\n","Epoch 42/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0089 - val_loss: 0.0073\n","Epoch 43/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0110 - val_loss: 0.0072\n","Epoch 44/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0106 - val_loss: 0.0071\n","Epoch 45/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0123 - val_loss: 0.0070\n","Epoch 46/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0078 - val_loss: 0.0070\n","Epoch 47/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0088 - val_loss: 0.0069\n","Epoch 48/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0127 - val_loss: 0.0068\n","Epoch 49/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0080 - val_loss: 0.0067\n","Epoch 50/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0100 - val_loss: 0.0066\n","Epoch 51/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0105 - val_loss: 0.0065\n","Epoch 52/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0105 - val_loss: 0.0065\n","Epoch 53/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0103 - val_loss: 0.0064\n","Epoch 54/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0100 - val_loss: 0.0063\n","Epoch 55/420\n","11/11 [==============================] - 0s 11ms/step - loss: 0.0089 - val_loss: 0.0062\n","Epoch 56/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0061\n","Epoch 57/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0113 - val_loss: 0.0060\n","Epoch 58/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0101 - val_loss: 0.0060\n","Epoch 59/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0087 - val_loss: 0.0059\n","Epoch 60/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0104 - val_loss: 0.0058\n","Epoch 61/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0078 - val_loss: 0.0057\n","Epoch 62/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0092 - val_loss: 0.0056\n","Epoch 63/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0078 - val_loss: 0.0055\n","Epoch 64/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0054\n","Epoch 65/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0084 - val_loss: 0.0054\n","Epoch 66/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0094 - val_loss: 0.0053\n","Epoch 67/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0102 - val_loss: 0.0052\n","Epoch 68/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0078 - val_loss: 0.0052\n","Epoch 69/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0077 - val_loss: 0.0051\n","Epoch 70/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0080 - val_loss: 0.0051\n","Epoch 71/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0070 - val_loss: 0.0050\n","Epoch 72/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0086 - val_loss: 0.0049\n","Epoch 73/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0069 - val_loss: 0.0049\n","Epoch 74/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0096 - val_loss: 0.0048\n","Epoch 75/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0047\n","Epoch 76/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0066 - val_loss: 0.0047\n","Epoch 77/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0080 - val_loss: 0.0046\n","Epoch 78/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0080 - val_loss: 0.0045\n","Epoch 79/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0084 - val_loss: 0.0044\n","Epoch 80/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0044\n","Epoch 81/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0063 - val_loss: 0.0043\n","Epoch 82/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0042\n","Epoch 83/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0079 - val_loss: 0.0042\n","Epoch 84/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0088 - val_loss: 0.0041\n","Epoch 85/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0083 - val_loss: 0.0040\n","Epoch 86/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0039\n","Epoch 87/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0092 - val_loss: 0.0039\n","Epoch 88/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0069 - val_loss: 0.0038\n","Epoch 89/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0064 - val_loss: 0.0038\n","Epoch 90/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0095 - val_loss: 0.0037\n","Epoch 91/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0077 - val_loss: 0.0036\n","Epoch 92/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0036\n","Epoch 93/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0083 - val_loss: 0.0036\n","Epoch 94/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0035\n","Epoch 95/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0085 - val_loss: 0.0035\n","Epoch 96/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0080 - val_loss: 0.0034\n","Epoch 97/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0068 - val_loss: 0.0034\n","Epoch 98/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0075 - val_loss: 0.0033\n","Epoch 99/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0082 - val_loss: 0.0033\n","Epoch 100/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0032\n","Epoch 101/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0096 - val_loss: 0.0032\n","Epoch 102/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0048 - val_loss: 0.0032\n","Epoch 103/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0079 - val_loss: 0.0031\n","Epoch 104/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0031\n","Epoch 105/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0079 - val_loss: 0.0030\n","Epoch 106/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0089 - val_loss: 0.0030\n","Epoch 107/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0029\n","Epoch 108/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0108 - val_loss: 0.0029\n","Epoch 109/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0072 - val_loss: 0.0029\n","Epoch 110/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0029\n","Epoch 111/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0057 - val_loss: 0.0028\n","Epoch 112/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0058 - val_loss: 0.0028\n","Epoch 113/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0059 - val_loss: 0.0028\n","Epoch 114/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0063 - val_loss: 0.0027\n","Epoch 115/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0082 - val_loss: 0.0027\n","Epoch 116/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0082 - val_loss: 0.0027\n","Epoch 117/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0074 - val_loss: 0.0027\n","Epoch 118/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0026\n","Epoch 119/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0080 - val_loss: 0.0026\n","Epoch 120/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0026\n","Epoch 121/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0076 - val_loss: 0.0026\n","Epoch 122/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0080 - val_loss: 0.0026\n","Epoch 123/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0063 - val_loss: 0.0026\n","Epoch 124/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0062 - val_loss: 0.0026\n","Epoch 125/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0026\n","Epoch 126/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0025\n","Epoch 127/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0025\n","Epoch 128/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0057 - val_loss: 0.0025\n","Epoch 129/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0025\n","Epoch 130/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0053 - val_loss: 0.0024\n","Epoch 131/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0024\n","Epoch 132/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0079 - val_loss: 0.0024\n","Epoch 133/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0024\n","Epoch 134/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0024\n","Epoch 135/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0023\n","Epoch 136/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0023\n","Epoch 137/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0079 - val_loss: 0.0023\n","Epoch 138/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0023\n","Epoch 139/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0064 - val_loss: 0.0023\n","Epoch 140/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0090 - val_loss: 0.0023\n","Epoch 141/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0065 - val_loss: 0.0022\n","Epoch 142/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0072 - val_loss: 0.0022\n","Epoch 143/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0077 - val_loss: 0.0022\n","Epoch 144/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0022\n","Epoch 145/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0069 - val_loss: 0.0021\n","Epoch 146/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0051 - val_loss: 0.0021\n","Epoch 147/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0021\n","Epoch 148/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0021\n","Epoch 149/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0093 - val_loss: 0.0021\n","Epoch 150/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0065 - val_loss: 0.0021\n","Epoch 151/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0022\n","Epoch 152/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0021\n","Epoch 153/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0074 - val_loss: 0.0021\n","Epoch 154/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0074 - val_loss: 0.0021\n","Epoch 155/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0059 - val_loss: 0.0020\n","Epoch 156/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0071 - val_loss: 0.0020\n","Epoch 157/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0020\n","Epoch 158/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0020\n","Epoch 159/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0020\n","Epoch 160/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0054 - val_loss: 0.0021\n","Epoch 161/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0102 - val_loss: 0.0021\n","Epoch 162/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0055 - val_loss: 0.0020\n","Epoch 163/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0078 - val_loss: 0.0020\n","Epoch 164/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0019\n","Epoch 165/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0075 - val_loss: 0.0019\n","Epoch 166/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0071 - val_loss: 0.0019\n","Epoch 167/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0071 - val_loss: 0.0019\n","Epoch 168/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0064 - val_loss: 0.0019\n","Epoch 169/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0060 - val_loss: 0.0019\n","Epoch 170/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0068 - val_loss: 0.0019\n","Epoch 171/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0080 - val_loss: 0.0019\n","Epoch 172/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0019\n","Epoch 173/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0052 - val_loss: 0.0019\n","Epoch 174/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0056 - val_loss: 0.0018\n","Epoch 175/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0066 - val_loss: 0.0019\n","Epoch 176/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0062 - val_loss: 0.0019\n","Epoch 177/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0046 - val_loss: 0.0019\n","Epoch 178/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0076 - val_loss: 0.0019\n","Epoch 179/420\n","11/11 [==============================] - 0s 12ms/step - loss: 0.0059 - val_loss: 0.0019\n","Epoch 180/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0072 - val_loss: 0.0019\n","Epoch 181/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0072 - val_loss: 0.0019\n","Epoch 182/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0019\n","Epoch 183/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0064 - val_loss: 0.0020\n","Epoch 184/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0059 - val_loss: 0.0020\n","Epoch 185/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0068 - val_loss: 0.0020\n","Epoch 186/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0067 - val_loss: 0.0019\n","Epoch 187/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0020\n","Epoch 188/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0041 - val_loss: 0.0019\n","Epoch 189/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0081 - val_loss: 0.0019\n","Epoch 190/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0078 - val_loss: 0.0019\n","Epoch 191/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0089 - val_loss: 0.0019\n","Epoch 192/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0060 - val_loss: 0.0019\n","Epoch 193/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0063 - val_loss: 0.0019\n","Epoch 194/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0060 - val_loss: 0.0020\n","Epoch 195/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0052 - val_loss: 0.0019\n","Epoch 196/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0061 - val_loss: 0.0019\n","Epoch 197/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0087 - val_loss: 0.0019\n","Epoch 198/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0052 - val_loss: 0.0019\n","Epoch 199/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0018\n","Epoch 200/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0083 - val_loss: 0.0018\n","Epoch 201/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0068 - val_loss: 0.0018\n","Epoch 202/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0072 - val_loss: 0.0018\n","Epoch 203/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0065 - val_loss: 0.0018\n","Epoch 204/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0062 - val_loss: 0.0019\n","Epoch 205/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0044 - val_loss: 0.0019\n","Epoch 206/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0073 - val_loss: 0.0019\n","Epoch 207/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0065 - val_loss: 0.0019\n","Epoch 208/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0065 - val_loss: 0.0019\n","Epoch 209/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0033 - val_loss: 0.0019\n","Epoch 210/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0056 - val_loss: 0.0019\n","Epoch 211/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0070 - val_loss: 0.0019\n","Epoch 212/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0076 - val_loss: 0.0018\n","Epoch 213/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0064 - val_loss: 0.0018\n","Epoch 214/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0054 - val_loss: 0.0018\n","Epoch 215/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0063 - val_loss: 0.0017\n","Epoch 216/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0050 - val_loss: 0.0018\n","Epoch 217/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0093 - val_loss: 0.0020\n","Epoch 218/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0018\n","Epoch 219/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0069 - val_loss: 0.0018\n","Epoch 220/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0064 - val_loss: 0.0018\n","Epoch 221/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0072 - val_loss: 0.0017\n","Epoch 222/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0054 - val_loss: 0.0017\n","Epoch 223/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0017\n","Epoch 224/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0017\n","Epoch 225/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0059 - val_loss: 0.0017\n","Epoch 226/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0062 - val_loss: 0.0018\n","Epoch 227/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0017\n","Epoch 228/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0055 - val_loss: 0.0017\n","Epoch 229/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0017\n","Epoch 230/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0085 - val_loss: 0.0017\n","Epoch 231/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0071 - val_loss: 0.0018\n","Epoch 232/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0017\n","Epoch 233/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0017\n","Epoch 234/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0017\n","Epoch 235/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0017\n","Epoch 236/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0017\n","Epoch 237/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0017\n","Epoch 238/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0017\n","Epoch 239/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0068 - val_loss: 0.0017\n","Epoch 240/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0017\n","Epoch 241/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0056 - val_loss: 0.0018\n","Epoch 242/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0018\n","Epoch 243/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0018\n","Epoch 244/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0016\n","Epoch 245/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0055 - val_loss: 0.0018\n","Epoch 246/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0018\n","Epoch 247/420\n","11/11 [==============================] - 0s 15ms/step - loss: 0.0068 - val_loss: 0.0017\n","Epoch 248/420\n","11/11 [==============================] - 0s 12ms/step - loss: 0.0080 - val_loss: 0.0017\n","Epoch 249/420\n","11/11 [==============================] - 0s 14ms/step - loss: 0.0056 - val_loss: 0.0017\n","Epoch 250/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0074 - val_loss: 0.0017\n","Epoch 251/420\n","11/11 [==============================] - 0s 12ms/step - loss: 0.0092 - val_loss: 0.0017\n","Epoch 252/420\n","11/11 [==============================] - 0s 12ms/step - loss: 0.0069 - val_loss: 0.0017\n","Epoch 253/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0058 - val_loss: 0.0017\n","Epoch 254/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0017\n","Epoch 255/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0068 - val_loss: 0.0017\n","Epoch 256/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0017\n","Epoch 257/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0051 - val_loss: 0.0016\n","Epoch 258/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0016\n","Epoch 259/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0052 - val_loss: 0.0016\n","Epoch 260/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0072 - val_loss: 0.0016\n","Epoch 261/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0016\n","Epoch 262/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0016\n","Epoch 263/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0016\n","Epoch 264/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0063 - val_loss: 0.0016\n","Epoch 265/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0068 - val_loss: 0.0016\n","Epoch 266/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0055 - val_loss: 0.0016\n","Epoch 267/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0016\n","Epoch 268/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0072 - val_loss: 0.0017\n","Epoch 269/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0055 - val_loss: 0.0017\n","Epoch 270/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0017\n","Epoch 271/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0016\n","Epoch 272/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0016\n","Epoch 273/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0015\n","Epoch 274/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0069 - val_loss: 0.0015\n","Epoch 275/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0058 - val_loss: 0.0016\n","Epoch 276/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0061 - val_loss: 0.0017\n","Epoch 277/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0051 - val_loss: 0.0017\n","Epoch 278/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0017\n","Epoch 279/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0074 - val_loss: 0.0016\n","Epoch 280/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0054 - val_loss: 0.0016\n","Epoch 281/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0016\n","Epoch 282/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0016\n","Epoch 283/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0016\n","Epoch 284/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0057 - val_loss: 0.0016\n","Epoch 285/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0057 - val_loss: 0.0016\n","Epoch 286/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0016\n","Epoch 287/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0016\n","Epoch 288/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0057 - val_loss: 0.0016\n","Epoch 289/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0055 - val_loss: 0.0016\n","Epoch 290/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0016\n","Epoch 291/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0048 - val_loss: 0.0017\n","Epoch 292/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0062 - val_loss: 0.0016\n","Epoch 293/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0068 - val_loss: 0.0016\n","Epoch 294/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0044 - val_loss: 0.0016\n","Epoch 295/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0055 - val_loss: 0.0015\n","Epoch 296/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0052 - val_loss: 0.0015\n","Epoch 297/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0015\n","Epoch 298/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0079 - val_loss: 0.0017\n","Epoch 299/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0095 - val_loss: 0.0017\n","Epoch 300/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0063 - val_loss: 0.0017\n","Epoch 301/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0016\n","Epoch 302/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0066 - val_loss: 0.0017\n","Epoch 303/420\n","11/11 [==============================] - 0s 13ms/step - loss: 0.0060 - val_loss: 0.0017\n","Epoch 304/420\n","11/11 [==============================] - 0s 11ms/step - loss: 0.0049 - val_loss: 0.0018\n","Epoch 305/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0062 - val_loss: 0.0017\n","Epoch 306/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0051 - val_loss: 0.0017\n","Epoch 307/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0063 - val_loss: 0.0016\n","Epoch 308/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0061 - val_loss: 0.0016\n","Epoch 309/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0056 - val_loss: 0.0015\n","Epoch 310/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0055 - val_loss: 0.0015\n","Epoch 311/420\n","11/11 [==============================] - 0s 11ms/step - loss: 0.0056 - val_loss: 0.0015\n","Epoch 312/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0061 - val_loss: 0.0015\n","Epoch 313/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0054 - val_loss: 0.0015\n","Epoch 314/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0037 - val_loss: 0.0015\n","Epoch 315/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0095 - val_loss: 0.0015\n","Epoch 316/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0079 - val_loss: 0.0015\n","Epoch 317/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0015\n","Epoch 318/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0053 - val_loss: 0.0015\n","Epoch 319/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0068 - val_loss: 0.0015\n","Epoch 320/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0041 - val_loss: 0.0016\n","Epoch 321/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0068 - val_loss: 0.0016\n","Epoch 322/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0047 - val_loss: 0.0016\n","Epoch 323/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0072 - val_loss: 0.0016\n","Epoch 324/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0016\n","Epoch 325/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0093 - val_loss: 0.0016\n","Epoch 326/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0069 - val_loss: 0.0015\n","Epoch 327/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0015\n","Epoch 328/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0061 - val_loss: 0.0015\n","Epoch 329/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0071 - val_loss: 0.0014\n","Epoch 330/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0040 - val_loss: 0.0015\n","Epoch 331/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0015\n","Epoch 332/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0044 - val_loss: 0.0016\n","Epoch 333/420\n","11/11 [==============================] - 0s 10ms/step - loss: 0.0050 - val_loss: 0.0016\n","Epoch 334/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0053 - val_loss: 0.0016\n","Epoch 335/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0069 - val_loss: 0.0015\n","Epoch 336/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0045 - val_loss: 0.0015\n","Epoch 337/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0099 - val_loss: 0.0014\n","Epoch 338/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0052 - val_loss: 0.0014\n","Epoch 339/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0015\n","Epoch 340/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0054 - val_loss: 0.0016\n","Epoch 341/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0068 - val_loss: 0.0016\n","Epoch 342/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0042 - val_loss: 0.0015\n","Epoch 343/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0070 - val_loss: 0.0015\n","Epoch 344/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0065 - val_loss: 0.0015\n","Epoch 345/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0062 - val_loss: 0.0015\n","Epoch 346/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0015\n","Epoch 347/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0015\n","Epoch 348/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0072 - val_loss: 0.0014\n","Epoch 349/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0084 - val_loss: 0.0014\n","Epoch 350/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0014\n","Epoch 351/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0046 - val_loss: 0.0015\n","Epoch 352/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0050 - val_loss: 0.0014\n","Epoch 353/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0014\n","Epoch 354/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0042 - val_loss: 0.0014\n","Epoch 355/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0054 - val_loss: 0.0014\n","Epoch 356/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0057 - val_loss: 0.0015\n","Epoch 357/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0016\n","Epoch 358/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0047 - val_loss: 0.0016\n","Epoch 359/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0015\n","Epoch 360/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0015\n","Epoch 361/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0015\n","Epoch 362/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0082 - val_loss: 0.0015\n","Epoch 363/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0081 - val_loss: 0.0015\n","Epoch 364/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0065 - val_loss: 0.0015\n","Epoch 365/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0068 - val_loss: 0.0015\n","Epoch 366/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0014\n","Epoch 367/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0039 - val_loss: 0.0015\n","Epoch 368/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0015\n","Epoch 369/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0065 - val_loss: 0.0015\n","Epoch 370/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0066 - val_loss: 0.0016\n","Epoch 371/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0016\n","Epoch 372/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0081 - val_loss: 0.0016\n","Epoch 373/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0016\n","Epoch 374/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0016\n","Epoch 375/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0060 - val_loss: 0.0015\n","Epoch 376/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0064 - val_loss: 0.0015\n","Epoch 377/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0069 - val_loss: 0.0015\n","Epoch 378/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0014\n","Epoch 379/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0014\n","Epoch 380/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0054 - val_loss: 0.0014\n","Epoch 381/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0042 - val_loss: 0.0014\n","Epoch 382/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0014\n","Epoch 383/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0014\n","Epoch 384/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0065 - val_loss: 0.0014\n","Epoch 385/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0071 - val_loss: 0.0015\n","Epoch 386/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0015\n","Epoch 387/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0066 - val_loss: 0.0015\n","Epoch 388/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0064 - val_loss: 0.0016\n","Epoch 389/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0016\n","Epoch 390/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0054 - val_loss: 0.0015\n","Epoch 391/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0015\n","Epoch 392/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0049 - val_loss: 0.0015\n","Epoch 393/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0054 - val_loss: 0.0015\n","Epoch 394/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0039 - val_loss: 0.0014\n","Epoch 395/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0066 - val_loss: 0.0015\n","Epoch 396/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0014\n","Epoch 397/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0014\n","Epoch 398/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0084 - val_loss: 0.0014\n","Epoch 399/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0053 - val_loss: 0.0014\n","Epoch 400/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0067 - val_loss: 0.0014\n","Epoch 401/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0057 - val_loss: 0.0017\n","Epoch 402/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0053 - val_loss: 0.0017\n","Epoch 403/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0052 - val_loss: 0.0016\n","Epoch 404/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0073 - val_loss: 0.0014\n","Epoch 405/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0088 - val_loss: 0.0015\n","Epoch 406/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0046 - val_loss: 0.0015\n","Epoch 407/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0050 - val_loss: 0.0015\n","Epoch 408/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0074 - val_loss: 0.0015\n","Epoch 409/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0053 - val_loss: 0.0015\n","Epoch 410/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0071 - val_loss: 0.0014\n","Epoch 411/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0041 - val_loss: 0.0015\n","Epoch 412/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0084 - val_loss: 0.0013\n","Epoch 413/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0062 - val_loss: 0.0014\n","Epoch 414/420\n","11/11 [==============================] - 0s 7ms/step - loss: 0.0064 - val_loss: 0.0015\n","Epoch 415/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0058 - val_loss: 0.0016\n","Epoch 416/420\n","11/11 [==============================] - 0s 9ms/step - loss: 0.0057 - val_loss: 0.0015\n","Epoch 417/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0056 - val_loss: 0.0015\n","Epoch 418/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0073 - val_loss: 0.0014\n","Epoch 419/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0059 - val_loss: 0.0015\n","Epoch 420/420\n","11/11 [==============================] - 0s 8ms/step - loss: 0.0045 - val_loss: 0.0015\n","Saved model to disk\n","Loaded model from disk\n"]}],"source":["fitting = True\n","fitting_save = True\n","epochs = 420\n","\n","if fitting:\n","    history = model.fit(train_x_LSTM, y_train, epochs=epochs, batch_size=batch_size, validation_data=(test_x_LSTM, y_test), verbose=1, shuffle=False)    \n","    if fitting_save:\n","    # serialize model to JSON\n","        model_json = model.to_json()\n","        with open(\"LSTM_model.json\", \"w\") as json_file:\n","            json_file.write(model_json)\n","        # serialize weights to HDF5\n","        model.save_weights(\"LSTM_model.h5\")\n","        print(\"Saved model to disk\")\n","        with open('history_LSTM.pickle', 'wb') as f:\n","            pickle.dump(history.history, f)\n","# load model  \n","from keras.models import model_from_json\n","json_file = open('LSTM_model.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","model = model_from_json(loaded_model_json)\n","# load weights into new model\n","model.load_weights(\"LSTM_model.h5\")        \n","with open('history_LSTM.pickle', 'rb') as f:\n","    history = pickle.load(f)\n","print(\"Loaded model from disk\")"]},{"cell_type":"markdown","id":"d0137b6f","metadata":{"id":"c57sZj-3jomA","papermill":{"duration":1.112036,"end_time":"2022-03-03T03:22:59.092097","exception":false,"start_time":"2022-03-03T03:22:57.980061","status":"completed"},"tags":[]},"source":["Let's plot the dynamic of loss and val loss like in the previous case.\n"]},{"cell_type":"code","execution_count":33,"id":"0a0117bf","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:23:01.256622Z","iopub.status.busy":"2022-03-03T03:23:01.242846Z","iopub.status.idle":"2022-03-03T03:23:01.506628Z","shell.execute_reply":"2022-03-03T03:23:01.505968Z","shell.execute_reply.started":"2022-03-03T03:15:30.160572Z"},"id":"xtYXH_J9jomB","papermill":{"duration":1.33744,"end_time":"2022-03-03T03:23:01.5068","exception":false,"start_time":"2022-03-03T03:23:00.16936","status":"completed"},"tags":[]},"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":["plt.figure()\n","plt.plot(history['loss'], label='train')\n","plt.plot(history['val_loss'], label='test')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","\n","plt.legend()\n","plt.show()"]},{"cell_type":"markdown","id":"bc8d4af2","metadata":{"id":"DMLKo9WvjomC","papermill":{"duration":1.073831,"end_time":"2022-03-03T03:23:03.737426","exception":false,"start_time":"2022-03-03T03:23:02.663595","status":"completed"},"tags":[]},"source":["Let's calculate our forecast.\n"]},{"cell_type":"code","execution_count":34,"id":"7f258741","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:23:05.932684Z","iopub.status.busy":"2022-03-03T03:23:05.931816Z","iopub.status.idle":"2022-03-03T03:23:06.556038Z","shell.execute_reply":"2022-03-03T03:23:06.556713Z","shell.execute_reply.started":"2022-03-03T03:15:30.355527Z"},"id":"8_MeiODNjomD","papermill":{"duration":1.74339,"end_time":"2022-03-03T03:23:06.556935","exception":false,"start_time":"2022-03-03T03:23:04.813545","status":"completed"},"tags":[]},"outputs":[],"source":["res_tr_LSTM = model.predict(train_x_LSTM)\n","res_ts_LSTM = model.predict(test_x_LSTM)\n","res_train_LSTM=scaler_y.inverse_transform(res_tr_LSTM).flatten()\n","res_test_LSTM=scaler_y.inverse_transform(res_ts_LSTM).flatten()"]},{"cell_type":"markdown","id":"7bd0c37f","metadata":{"id":"-IFuYbGajomE","papermill":{"duration":1.073448,"end_time":"2022-03-03T03:23:08.699529","exception":false,"start_time":"2022-03-03T03:23:07.626081","status":"completed"},"tags":[]},"source":["And accuracy:\n"]},{"cell_type":"code","execution_count":35,"id":"58911989","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:23:10.843382Z","iopub.status.busy":"2022-03-03T03:23:10.842551Z","iopub.status.idle":"2022-03-03T03:23:10.854878Z","shell.execute_reply":"2022-03-03T03:23:10.854113Z","shell.execute_reply.started":"2022-03-03T03:15:30.878332Z"},"id":"D4XZ8Jj4jomE","papermill":{"duration":1.092895,"end_time":"2022-03-03T03:23:10.85505","exception":false,"start_time":"2022-03-03T03:23:09.762155","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Correlation train 0.9928495964501599\n","Correlation train 0.9766580400384832\n","Mean Absolute Error: 52680.85382168928\n","Mean Squared Error: 8396806269.492304\n","Root Mean Squared Error: 91634.08901436356\n"]}],"source":["print(\"Correlation train\", np.corrcoef(res_train, res_train_LSTM)[0,1])\n","print(\"Correlation train\", np.corrcoef(res_test, res_test_LSTM)[0,1])\n","print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, res_test_LSTM))\n","print('Mean Squared Error:', metrics.mean_squared_error(y_test, res_test_LSTM))\n","print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, res_test_LSTM)))"]},{"cell_type":"markdown","id":"69fd8569","metadata":{"id":"qb_yfGj7jomF","papermill":{"duration":1.061724,"end_time":"2022-03-03T03:23:13.059602","exception":false,"start_time":"2022-03-03T03:23:11.997878","status":"completed"},"tags":[]},"source":["As you can see, the forecast results of the test data set are much better than ones of the previous models. Let's visualize these 3 results:\n"]},{"cell_type":"code","execution_count":36,"id":"9c3ec0f5","metadata":{"execution":{"iopub.execute_input":"2022-03-03T03:23:15.19493Z","iopub.status.busy":"2022-03-03T03:23:15.194146Z","iopub.status.idle":"2022-03-03T03:23:16.909826Z","shell.execute_reply":"2022-03-03T03:23:16.909004Z","shell.execute_reply.started":"2022-03-03T03:15:30.89167Z"},"id":"n_Th26YijomG","papermill":{"duration":2.78719,"end_time":"2022-03-03T03:23:16.910018","exception":false,"start_time":"2022-03-03T03:23:14.122828","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["res_pred_test_ln = pd.Series(y_pred_test_ln, name = 'Predicted test Linear Model')\n","res_pred_test_ANN = pd.Series(res_test_ANN, name = 'Predicted test ANN')\n","res_pred_test_LSTM = pd.Series(res_test_LSTM, name = 'Predicted test LSTM')\n","\n","df_2 = pd.DataFrame({'Actual test': res_test, 'Linear Model': res_pred_test_ln, 'ANN Model': res_pred_test_ANN,  'LSTM Model': res_pred_test_LSTM,})\n","df_2.index = dataset.index[len(dataset)-len(res_test):]\n","df_2.plot()\n","df_2 = pd.DataFrame({'Actual test': res_test, 'Linear Model': res_pred_test_ln,})\n","df_2.index = dataset.index[len(dataset)-len(res_test):]\n","df_2.plot()\n","df_2 = pd.DataFrame({'Actual test': res_test, 'LSTM Model': res_pred_test_LSTM,})\n","df_2.index = dataset.index[len(dataset)-len(res_test):]\n","df_2.plot()\n","df_2 = pd.DataFrame({'Actual test': res_test,'ANN Model': res_pred_test_ANN,})\n","df_2.index = dataset.index[len(dataset)-len(res_test):]\n","df_2.plot()\n","plt.show()"]},{"cell_type":"markdown","id":"251ad0ce","metadata":{"id":"64RewTXJjomG","papermill":{"duration":1.072502,"end_time":"2022-03-03T03:23:19.136571","exception":false,"start_time":"2022-03-03T03:23:18.064069","status":"completed"},"tags":[]},"source":["As you can see, an LSTM model makes a perfect forecast. A Linear Regression is the fastest forecasting model.\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"},"papermill":{"default_parameters":{},"duration":214.755087,"end_time":"2022-03-03T03:23:23.020195","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-03-03T03:19:48.265108","version":"2.3.3"}},"nbformat":4,"nbformat_minor":5}