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Predictive Maintenance for a Water Pump.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "Predictive Maintenance for a Water Pump.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "toc_visible": true, | |
| "mount_file_id": "1s60Zsr7AR1oqxTWHJBeFkTnQye69YJ65", | |
| "authorship_tag": "ABX9TyPwDsNA7UNNfI2OKY3/5kJb", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/KevinPatel04/bea0977f6827a876427886bfcc727f96/predictive-maintenance-for-a-water-pump.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9ameaIOTw1ZM" | |
| }, | |
| "source": [ | |
| "<center>\n", | |
| "\n", | |
| "# Predictive Maintenance for a Water Pump\n", | |
| "\n", | |
| "</center>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "diVJoMcvxLja" | |
| }, | |
| "source": [ | |
| "\n", | |
| "\n", | |
| "<center>\n", | |
| "\n", | |
| "Image src: https://www.eetimes.com/jumping-into-industry-4-0-with-predictive-maintenance-solutions/\n", | |
| "\n", | |
| "</center>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "kQb9hVToSpNo" | |
| }, | |
| "source": [ | |
| "**Aim:**\n", | |
| "\n", | |
| "During this demo, I will show how we can build and train a neural network for predicting whether a water pump will fail in a future time window or not. The prediction for pump failure is about 98% accurate for the following settings." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "1MeWmNk4Gjfe" | |
| }, | |
| "source": [ | |
| "### **Understanding Dataset**\n", | |
| "\n", | |
| "#### *Load the CSV Data*\n", | |
| "\n", | |
| "Dataset: https://www.kaggle.com/nphantawee/pump-sensor-data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 411 | |
| }, | |
| "id": "spulYw1etPth", | |
| "outputId": "0577f43b-822a-4d6a-f05e-bd889a5962da" | |
| }, | |
| "source": [ | |
| "import pandas as pd\n", | |
| "df = pd.read_csv('/content/drive/MyDrive/IoT-Analytics/sensor.csv')\n", | |
| "df.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>Unnamed: 0</th>\n", | |
| " <th>timestamp</th>\n", | |
| " <th>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_15</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " <th>machine_status</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>0</td>\n", | |
| " <td>2018-04-01 00:00:00</td>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.3750</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.0556</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2018-04-01 00:01:00</td>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.3750</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.0556</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2</td>\n", | |
| " <td>2018-04-01 00:02:00</td>\n", | |
| " <td>2.444734</td>\n", | |
| " <td>47.35243</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.397570</td>\n", | |
| " <td>638.8889</td>\n", | |
| " <td>73.54598</td>\n", | |
| " <td>13.32465</td>\n", | |
| " <td>16.03733</td>\n", | |
| " <td>15.61777</td>\n", | |
| " <td>15.01013</td>\n", | |
| " <td>37.86777</td>\n", | |
| " <td>48.17723</td>\n", | |
| " <td>32.08894</td>\n", | |
| " <td>1.708474</td>\n", | |
| " <td>420.8480</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>462.7798</td>\n", | |
| " <td>459.6364</td>\n", | |
| " <td>2.500062</td>\n", | |
| " <td>666.2234</td>\n", | |
| " <td>399.9418</td>\n", | |
| " <td>880.4237</td>\n", | |
| " <td>501.3617</td>\n", | |
| " <td>982.7342</td>\n", | |
| " <td>631.1326</td>\n", | |
| " <td>740.8031</td>\n", | |
| " <td>849.8997</td>\n", | |
| " <td>454.2390</td>\n", | |
| " <td>778.5734</td>\n", | |
| " <td>715.6266</td>\n", | |
| " <td>661.5740</td>\n", | |
| " <td>721.8750</td>\n", | |
| " <td>694.7721</td>\n", | |
| " <td>441.2635</td>\n", | |
| " <td>169.9820</td>\n", | |
| " <td>343.1955</td>\n", | |
| " <td>200.9694</td>\n", | |
| " <td>93.90508</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>31.25000</td>\n", | |
| " <td>69.53125</td>\n", | |
| " <td>30.46875</td>\n", | |
| " <td>31.770830</td>\n", | |
| " <td>41.66666</td>\n", | |
| " <td>39.351852</td>\n", | |
| " <td>65.39352</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194443</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>67.12963</td>\n", | |
| " <td>241.3194</td>\n", | |
| " <td>203.7037</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>3</td>\n", | |
| " <td>2018-04-01 00:03:00</td>\n", | |
| " <td>2.460474</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.1684</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>628.1250</td>\n", | |
| " <td>76.98898</td>\n", | |
| " <td>13.31742</td>\n", | |
| " <td>16.24711</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>38.57977</td>\n", | |
| " <td>48.65607</td>\n", | |
| " <td>31.67221</td>\n", | |
| " <td>1.579427</td>\n", | |
| " <td>420.7494</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>462.8980</td>\n", | |
| " <td>460.8858</td>\n", | |
| " <td>2.509521</td>\n", | |
| " <td>666.0114</td>\n", | |
| " <td>399.1046</td>\n", | |
| " <td>878.8917</td>\n", | |
| " <td>499.0430</td>\n", | |
| " <td>977.7520</td>\n", | |
| " <td>625.4076</td>\n", | |
| " <td>739.2722</td>\n", | |
| " <td>847.7579</td>\n", | |
| " <td>474.8731</td>\n", | |
| " <td>779.5091</td>\n", | |
| " <td>690.4011</td>\n", | |
| " <td>686.1111</td>\n", | |
| " <td>754.6875</td>\n", | |
| " <td>683.3831</td>\n", | |
| " <td>446.2493</td>\n", | |
| " <td>166.4987</td>\n", | |
| " <td>343.9586</td>\n", | |
| " <td>193.1689</td>\n", | |
| " <td>101.04060</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>72.13541</td>\n", | |
| " <td>30.46875</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>40.88541</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>64.81481</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>66.84028</td>\n", | |
| " <td>240.4514</td>\n", | |
| " <td>203.1250</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>4</td>\n", | |
| " <td>2018-04-01 00:04:00</td>\n", | |
| " <td>2.445718</td>\n", | |
| " <td>47.13541</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>636.4583</td>\n", | |
| " <td>76.58897</td>\n", | |
| " <td>13.35359</td>\n", | |
| " <td>16.21094</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>39.48939</td>\n", | |
| " <td>49.06298</td>\n", | |
| " <td>31.95202</td>\n", | |
| " <td>1.683831</td>\n", | |
| " <td>419.8926</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>461.4906</td>\n", | |
| " <td>468.2206</td>\n", | |
| " <td>2.604785</td>\n", | |
| " <td>663.2111</td>\n", | |
| " <td>400.5426</td>\n", | |
| " <td>882.5874</td>\n", | |
| " <td>498.5383</td>\n", | |
| " <td>979.5755</td>\n", | |
| " <td>627.1830</td>\n", | |
| " <td>737.6033</td>\n", | |
| " <td>846.9182</td>\n", | |
| " <td>408.8159</td>\n", | |
| " <td>785.2307</td>\n", | |
| " <td>704.6937</td>\n", | |
| " <td>631.4814</td>\n", | |
| " <td>766.1458</td>\n", | |
| " <td>702.4431</td>\n", | |
| " <td>433.9081</td>\n", | |
| " <td>164.7498</td>\n", | |
| " <td>339.9630</td>\n", | |
| " <td>193.8770</td>\n", | |
| " <td>101.70380</td>\n", | |
| " <td>42.70833</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>76.82291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>65.10416</td>\n", | |
| " <td>51.79398</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>158.2755</td>\n", | |
| " <td>66.55093</td>\n", | |
| " <td>242.1875</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Unnamed: 0 timestamp ... sensor_51 machine_status\n", | |
| "0 0 2018-04-01 00:00:00 ... 201.3889 NORMAL\n", | |
| "1 1 2018-04-01 00:01:00 ... 201.3889 NORMAL\n", | |
| "2 2 2018-04-01 00:02:00 ... 203.7037 NORMAL\n", | |
| "3 3 2018-04-01 00:03:00 ... 203.1250 NORMAL\n", | |
| "4 4 2018-04-01 00:04:00 ... 201.3889 NORMAL\n", | |
| "\n", | |
| "[5 rows x 55 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4AgblrV7eCf5" | |
| }, | |
| "source": [ | |
| "#### *Data Pre-processing*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 394 | |
| }, | |
| "id": "AMdNmTvNePru", | |
| "outputId": "4b5e9504-7fb8-4040-aa4c-9b2f26e35b39" | |
| }, | |
| "source": [ | |
| "df.drop(columns=[\"Unnamed: 0\"],inplace=True)\n", | |
| "df.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>timestamp</th>\n", | |
| " <th>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_15</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " <th>machine_status</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2018-04-01 00:00:00</td>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.3750</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.0556</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2018-04-01 00:01:00</td>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.3750</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.0556</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2018-04-01 00:02:00</td>\n", | |
| " <td>2.444734</td>\n", | |
| " <td>47.35243</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.397570</td>\n", | |
| " <td>638.8889</td>\n", | |
| " <td>73.54598</td>\n", | |
| " <td>13.32465</td>\n", | |
| " <td>16.03733</td>\n", | |
| " <td>15.61777</td>\n", | |
| " <td>15.01013</td>\n", | |
| " <td>37.86777</td>\n", | |
| " <td>48.17723</td>\n", | |
| " <td>32.08894</td>\n", | |
| " <td>1.708474</td>\n", | |
| " <td>420.8480</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>462.7798</td>\n", | |
| " <td>459.6364</td>\n", | |
| " <td>2.500062</td>\n", | |
| " <td>666.2234</td>\n", | |
| " <td>399.9418</td>\n", | |
| " <td>880.4237</td>\n", | |
| " <td>501.3617</td>\n", | |
| " <td>982.7342</td>\n", | |
| " <td>631.1326</td>\n", | |
| " <td>740.8031</td>\n", | |
| " <td>849.8997</td>\n", | |
| " <td>454.2390</td>\n", | |
| " <td>778.5734</td>\n", | |
| " <td>715.6266</td>\n", | |
| " <td>661.5740</td>\n", | |
| " <td>721.8750</td>\n", | |
| " <td>694.7721</td>\n", | |
| " <td>441.2635</td>\n", | |
| " <td>169.9820</td>\n", | |
| " <td>343.1955</td>\n", | |
| " <td>200.9694</td>\n", | |
| " <td>93.90508</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>31.25000</td>\n", | |
| " <td>69.53125</td>\n", | |
| " <td>30.46875</td>\n", | |
| " <td>31.770830</td>\n", | |
| " <td>41.66666</td>\n", | |
| " <td>39.351852</td>\n", | |
| " <td>65.39352</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194443</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>67.12963</td>\n", | |
| " <td>241.3194</td>\n", | |
| " <td>203.7037</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2018-04-01 00:03:00</td>\n", | |
| " <td>2.460474</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.1684</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>628.1250</td>\n", | |
| " <td>76.98898</td>\n", | |
| " <td>13.31742</td>\n", | |
| " <td>16.24711</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>38.57977</td>\n", | |
| " <td>48.65607</td>\n", | |
| " <td>31.67221</td>\n", | |
| " <td>1.579427</td>\n", | |
| " <td>420.7494</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>462.8980</td>\n", | |
| " <td>460.8858</td>\n", | |
| " <td>2.509521</td>\n", | |
| " <td>666.0114</td>\n", | |
| " <td>399.1046</td>\n", | |
| " <td>878.8917</td>\n", | |
| " <td>499.0430</td>\n", | |
| " <td>977.7520</td>\n", | |
| " <td>625.4076</td>\n", | |
| " <td>739.2722</td>\n", | |
| " <td>847.7579</td>\n", | |
| " <td>474.8731</td>\n", | |
| " <td>779.5091</td>\n", | |
| " <td>690.4011</td>\n", | |
| " <td>686.1111</td>\n", | |
| " <td>754.6875</td>\n", | |
| " <td>683.3831</td>\n", | |
| " <td>446.2493</td>\n", | |
| " <td>166.4987</td>\n", | |
| " <td>343.9586</td>\n", | |
| " <td>193.1689</td>\n", | |
| " <td>101.04060</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>72.13541</td>\n", | |
| " <td>30.46875</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>40.88541</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>64.81481</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>66.84028</td>\n", | |
| " <td>240.4514</td>\n", | |
| " <td>203.1250</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2018-04-01 00:04:00</td>\n", | |
| " <td>2.445718</td>\n", | |
| " <td>47.13541</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>636.4583</td>\n", | |
| " <td>76.58897</td>\n", | |
| " <td>13.35359</td>\n", | |
| " <td>16.21094</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>39.48939</td>\n", | |
| " <td>49.06298</td>\n", | |
| " <td>31.95202</td>\n", | |
| " <td>1.683831</td>\n", | |
| " <td>419.8926</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>461.4906</td>\n", | |
| " <td>468.2206</td>\n", | |
| " <td>2.604785</td>\n", | |
| " <td>663.2111</td>\n", | |
| " <td>400.5426</td>\n", | |
| " <td>882.5874</td>\n", | |
| " <td>498.5383</td>\n", | |
| " <td>979.5755</td>\n", | |
| " <td>627.1830</td>\n", | |
| " <td>737.6033</td>\n", | |
| " <td>846.9182</td>\n", | |
| " <td>408.8159</td>\n", | |
| " <td>785.2307</td>\n", | |
| " <td>704.6937</td>\n", | |
| " <td>631.4814</td>\n", | |
| " <td>766.1458</td>\n", | |
| " <td>702.4431</td>\n", | |
| " <td>433.9081</td>\n", | |
| " <td>164.7498</td>\n", | |
| " <td>339.9630</td>\n", | |
| " <td>193.8770</td>\n", | |
| " <td>101.70380</td>\n", | |
| " <td>42.70833</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>76.82291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>65.10416</td>\n", | |
| " <td>51.79398</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>158.2755</td>\n", | |
| " <td>66.55093</td>\n", | |
| " <td>242.1875</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " timestamp sensor_00 ... sensor_51 machine_status\n", | |
| "0 2018-04-01 00:00:00 2.465394 ... 201.3889 NORMAL\n", | |
| "1 2018-04-01 00:01:00 2.465394 ... 201.3889 NORMAL\n", | |
| "2 2018-04-01 00:02:00 2.444734 ... 203.7037 NORMAL\n", | |
| "3 2018-04-01 00:03:00 2.460474 ... 203.1250 NORMAL\n", | |
| "4 2018-04-01 00:04:00 2.445718 ... 201.3889 NORMAL\n", | |
| "\n", | |
| "[5 rows x 54 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "F3foYvRQjyPl", | |
| "outputId": "d6010fe4-1c77-42b5-c6c6-1b3cc22b5305" | |
| }, | |
| "source": [ | |
| "total_n_rows = df.shape[0]\n", | |
| "total_n_rows" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "220320" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "SsI8ZB6Md8YV", | |
| "outputId": "79932be3-e936-41c2-f1c3-a700958d17d0" | |
| }, | |
| "source": [ | |
| "df.isna().sum()/total_n_rows*100" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "timestamp 0.000000\n", | |
| "sensor_00 4.633261\n", | |
| "sensor_01 0.167484\n", | |
| "sensor_02 0.008624\n", | |
| "sensor_03 0.008624\n", | |
| "sensor_04 0.008624\n", | |
| "sensor_05 0.008624\n", | |
| "sensor_06 2.177741\n", | |
| "sensor_07 2.474129\n", | |
| "sensor_08 2.317992\n", | |
| "sensor_09 2.085603\n", | |
| "sensor_10 0.008624\n", | |
| "sensor_11 0.008624\n", | |
| "sensor_12 0.008624\n", | |
| "sensor_13 0.008624\n", | |
| "sensor_14 0.009532\n", | |
| "sensor_15 100.000000\n", | |
| "sensor_16 0.014070\n", | |
| "sensor_17 0.020879\n", | |
| "sensor_18 0.020879\n", | |
| "sensor_19 0.007262\n", | |
| "sensor_20 0.007262\n", | |
| "sensor_21 0.007262\n", | |
| "sensor_22 0.018609\n", | |
| "sensor_23 0.007262\n", | |
| "sensor_24 0.007262\n", | |
| "sensor_25 0.016340\n", | |
| "sensor_26 0.009078\n", | |
| "sensor_27 0.007262\n", | |
| "sensor_28 0.007262\n", | |
| "sensor_29 0.032680\n", | |
| "sensor_30 0.118464\n", | |
| "sensor_31 0.007262\n", | |
| "sensor_32 0.030864\n", | |
| "sensor_33 0.007262\n", | |
| "sensor_34 0.007262\n", | |
| "sensor_35 0.007262\n", | |
| "sensor_36 0.007262\n", | |
| "sensor_37 0.007262\n", | |
| "sensor_38 0.012255\n", | |
| "sensor_39 0.012255\n", | |
| "sensor_40 0.012255\n", | |
| "sensor_41 0.012255\n", | |
| "sensor_42 0.012255\n", | |
| "sensor_43 0.012255\n", | |
| "sensor_44 0.012255\n", | |
| "sensor_45 0.012255\n", | |
| "sensor_46 0.012255\n", | |
| "sensor_47 0.012255\n", | |
| "sensor_48 0.012255\n", | |
| "sensor_49 0.012255\n", | |
| "sensor_50 34.956881\n", | |
| "sensor_51 6.982117\n", | |
| "machine_status 0.000000\n", | |
| "dtype: float64" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 13 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "EaU_N1yRkQFJ" | |
| }, | |
| "source": [ | |
| "df.drop(columns=['sensor_15'],inplace=True)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "OPWUs30LieHz", | |
| "outputId": "b5e5dabc-2460-4f8a-f575-9605118cdc4a" | |
| }, | |
| "source": [ | |
| "for col in df.columns:\n", | |
| " if col.startswith('sensor_'):\n", | |
| " df[col] = df[col].fillna(df[col].mean())\n", | |
| "print(\"Number of Missing Values: \",df.isna().sum().sum())" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Number of Missing Values: 0\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 317 | |
| }, | |
| "id": "kQTuTaUGkZ8r", | |
| "outputId": "2950b7e7-e253-49a9-b56a-5e0be4bae142" | |
| }, | |
| "source": [ | |
| "df.describe()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>2.372221</td>\n", | |
| " <td>47.591611</td>\n", | |
| " <td>50.867392</td>\n", | |
| " <td>43.752481</td>\n", | |
| " <td>590.673936</td>\n", | |
| " <td>73.396414</td>\n", | |
| " <td>13.501537</td>\n", | |
| " <td>15.843152</td>\n", | |
| " <td>15.200721</td>\n", | |
| " <td>14.799210</td>\n", | |
| " <td>41.470339</td>\n", | |
| " <td>41.918319</td>\n", | |
| " <td>29.136975</td>\n", | |
| " <td>7.078858</td>\n", | |
| " <td>376.860041</td>\n", | |
| " <td>416.472892</td>\n", | |
| " <td>421.127517</td>\n", | |
| " <td>2.303785</td>\n", | |
| " <td>590.829775</td>\n", | |
| " <td>360.805165</td>\n", | |
| " <td>796.225942</td>\n", | |
| " <td>459.792815</td>\n", | |
| " <td>922.609264</td>\n", | |
| " <td>556.235397</td>\n", | |
| " <td>649.144799</td>\n", | |
| " <td>786.411781</td>\n", | |
| " <td>501.506589</td>\n", | |
| " <td>851.690339</td>\n", | |
| " <td>576.195305</td>\n", | |
| " <td>614.596442</td>\n", | |
| " <td>863.323100</td>\n", | |
| " <td>804.283915</td>\n", | |
| " <td>486.405980</td>\n", | |
| " <td>234.971776</td>\n", | |
| " <td>427.129817</td>\n", | |
| " <td>593.033876</td>\n", | |
| " <td>60.787360</td>\n", | |
| " <td>49.655946</td>\n", | |
| " <td>36.610444</td>\n", | |
| " <td>68.844530</td>\n", | |
| " <td>35.365126</td>\n", | |
| " <td>35.453455</td>\n", | |
| " <td>43.879591</td>\n", | |
| " <td>42.656877</td>\n", | |
| " <td>43.094984</td>\n", | |
| " <td>48.018585</td>\n", | |
| " <td>44.340903</td>\n", | |
| " <td>150.889044</td>\n", | |
| " <td>57.119968</td>\n", | |
| " <td>183.049260</td>\n", | |
| " <td>202.699667</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>0.402564</td>\n", | |
| " <td>3.293904</td>\n", | |
| " <td>3.666662</td>\n", | |
| " <td>2.418782</td>\n", | |
| " <td>144.017702</td>\n", | |
| " <td>17.297501</td>\n", | |
| " <td>2.140046</td>\n", | |
| " <td>2.173755</td>\n", | |
| " <td>2.013639</td>\n", | |
| " <td>2.070033</td>\n", | |
| " <td>12.092997</td>\n", | |
| " <td>13.055862</td>\n", | |
| " <td>10.113499</td>\n", | |
| " <td>6.901457</td>\n", | |
| " <td>113.200986</td>\n", | |
| " <td>126.063772</td>\n", | |
| " <td>129.142691</td>\n", | |
| " <td>0.765803</td>\n", | |
| " <td>199.338581</td>\n", | |
| " <td>101.970415</td>\n", | |
| " <td>226.671085</td>\n", | |
| " <td>154.513958</td>\n", | |
| " <td>291.824683</td>\n", | |
| " <td>182.291359</td>\n", | |
| " <td>220.847121</td>\n", | |
| " <td>246.652412</td>\n", | |
| " <td>169.817006</td>\n", | |
| " <td>313.062664</td>\n", | |
| " <td>225.727198</td>\n", | |
| " <td>195.610904</td>\n", | |
| " <td>283.534464</td>\n", | |
| " <td>260.562141</td>\n", | |
| " <td>150.746362</td>\n", | |
| " <td>88.372856</td>\n", | |
| " <td>141.767371</td>\n", | |
| " <td>289.375003</td>\n", | |
| " <td>37.603518</td>\n", | |
| " <td>10.539752</td>\n", | |
| " <td>15.612766</td>\n", | |
| " <td>21.369829</td>\n", | |
| " <td>7.898181</td>\n", | |
| " <td>10.258892</td>\n", | |
| " <td>11.043727</td>\n", | |
| " <td>11.575646</td>\n", | |
| " <td>12.836733</td>\n", | |
| " <td>15.640325</td>\n", | |
| " <td>10.441797</td>\n", | |
| " <td>82.239917</td>\n", | |
| " <td>19.142425</td>\n", | |
| " <td>52.630590</td>\n", | |
| " <td>105.693568</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>33.159720</td>\n", | |
| " <td>31.640620</td>\n", | |
| " <td>2.798032</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.014468</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.028935</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>32.409550</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>95.527660</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>43.154790</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>4.319347</td>\n", | |
| " <td>0.636574</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>23.958330</td>\n", | |
| " <td>0.240716</td>\n", | |
| " <td>6.460602</td>\n", | |
| " <td>54.882370</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.260970</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>24.479166</td>\n", | |
| " <td>19.270830</td>\n", | |
| " <td>23.437500</td>\n", | |
| " <td>20.833330</td>\n", | |
| " <td>22.135416</td>\n", | |
| " <td>24.479166</td>\n", | |
| " <td>25.752316</td>\n", | |
| " <td>26.331018</td>\n", | |
| " <td>26.331018</td>\n", | |
| " <td>27.199070</td>\n", | |
| " <td>26.331018</td>\n", | |
| " <td>26.620370</td>\n", | |
| " <td>27.488426</td>\n", | |
| " <td>27.777779</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>2.419155</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>50.390620</td>\n", | |
| " <td>42.838539</td>\n", | |
| " <td>626.620400</td>\n", | |
| " <td>69.977213</td>\n", | |
| " <td>13.346350</td>\n", | |
| " <td>15.856480</td>\n", | |
| " <td>15.183740</td>\n", | |
| " <td>15.010130</td>\n", | |
| " <td>40.705417</td>\n", | |
| " <td>38.857022</td>\n", | |
| " <td>28.687178</td>\n", | |
| " <td>1.538652</td>\n", | |
| " <td>418.100925</td>\n", | |
| " <td>459.447800</td>\n", | |
| " <td>454.131950</td>\n", | |
| " <td>2.447450</td>\n", | |
| " <td>662.766800</td>\n", | |
| " <td>398.020575</td>\n", | |
| " <td>875.461300</td>\n", | |
| " <td>478.942500</td>\n", | |
| " <td>950.919700</td>\n", | |
| " <td>601.149500</td>\n", | |
| " <td>693.932600</td>\n", | |
| " <td>790.343525</td>\n", | |
| " <td>448.299675</td>\n", | |
| " <td>782.685650</td>\n", | |
| " <td>518.964700</td>\n", | |
| " <td>627.777800</td>\n", | |
| " <td>839.062400</td>\n", | |
| " <td>760.703950</td>\n", | |
| " <td>489.753000</td>\n", | |
| " <td>172.486475</td>\n", | |
| " <td>353.182075</td>\n", | |
| " <td>288.559000</td>\n", | |
| " <td>28.803398</td>\n", | |
| " <td>45.572910</td>\n", | |
| " <td>32.552080</td>\n", | |
| " <td>57.812500</td>\n", | |
| " <td>32.552080</td>\n", | |
| " <td>32.812500</td>\n", | |
| " <td>39.583330</td>\n", | |
| " <td>36.747684</td>\n", | |
| " <td>36.747684</td>\n", | |
| " <td>40.509258</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>83.912030</td>\n", | |
| " <td>47.743060</td>\n", | |
| " <td>182.581000</td>\n", | |
| " <td>180.555600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>2.455556</td>\n", | |
| " <td>48.133678</td>\n", | |
| " <td>51.649300</td>\n", | |
| " <td>44.227428</td>\n", | |
| " <td>632.638916</td>\n", | |
| " <td>75.576145</td>\n", | |
| " <td>13.628470</td>\n", | |
| " <td>16.167530</td>\n", | |
| " <td>15.451390</td>\n", | |
| " <td>15.082470</td>\n", | |
| " <td>44.290480</td>\n", | |
| " <td>45.362290</td>\n", | |
| " <td>32.515630</td>\n", | |
| " <td>2.930587</td>\n", | |
| " <td>420.106000</td>\n", | |
| " <td>462.855850</td>\n", | |
| " <td>462.017950</td>\n", | |
| " <td>2.533686</td>\n", | |
| " <td>665.672050</td>\n", | |
| " <td>399.366900</td>\n", | |
| " <td>879.697300</td>\n", | |
| " <td>531.854100</td>\n", | |
| " <td>981.924500</td>\n", | |
| " <td>625.872650</td>\n", | |
| " <td>740.199250</td>\n", | |
| " <td>861.831750</td>\n", | |
| " <td>494.475250</td>\n", | |
| " <td>967.231500</td>\n", | |
| " <td>564.894700</td>\n", | |
| " <td>668.981400</td>\n", | |
| " <td>917.708300</td>\n", | |
| " <td>878.807600</td>\n", | |
| " <td>512.267800</td>\n", | |
| " <td>226.367700</td>\n", | |
| " <td>473.340800</td>\n", | |
| " <td>709.637350</td>\n", | |
| " <td>64.291375</td>\n", | |
| " <td>49.479160</td>\n", | |
| " <td>35.416660</td>\n", | |
| " <td>66.406250</td>\n", | |
| " <td>34.895832</td>\n", | |
| " <td>35.156250</td>\n", | |
| " <td>42.968750</td>\n", | |
| " <td>40.509260</td>\n", | |
| " <td>40.219910</td>\n", | |
| " <td>44.849540</td>\n", | |
| " <td>42.534720</td>\n", | |
| " <td>138.020800</td>\n", | |
| " <td>52.662040</td>\n", | |
| " <td>183.049260</td>\n", | |
| " <td>199.942100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>2.499826</td>\n", | |
| " <td>49.479160</td>\n", | |
| " <td>52.777770</td>\n", | |
| " <td>45.312500</td>\n", | |
| " <td>637.615723</td>\n", | |
| " <td>80.911770</td>\n", | |
| " <td>14.539930</td>\n", | |
| " <td>16.427950</td>\n", | |
| " <td>15.697340</td>\n", | |
| " <td>15.118630</td>\n", | |
| " <td>47.463485</td>\n", | |
| " <td>49.656238</td>\n", | |
| " <td>34.939455</td>\n", | |
| " <td>12.859338</td>\n", | |
| " <td>420.997000</td>\n", | |
| " <td>464.302600</td>\n", | |
| " <td>466.855700</td>\n", | |
| " <td>2.587667</td>\n", | |
| " <td>667.146625</td>\n", | |
| " <td>400.088300</td>\n", | |
| " <td>882.129800</td>\n", | |
| " <td>534.254400</td>\n", | |
| " <td>1090.807250</td>\n", | |
| " <td>628.607500</td>\n", | |
| " <td>750.356125</td>\n", | |
| " <td>919.098450</td>\n", | |
| " <td>536.272050</td>\n", | |
| " <td>1043.972000</td>\n", | |
| " <td>743.947000</td>\n", | |
| " <td>697.222200</td>\n", | |
| " <td>981.249900</td>\n", | |
| " <td>943.858175</td>\n", | |
| " <td>555.156900</td>\n", | |
| " <td>316.839525</td>\n", | |
| " <td>528.889800</td>\n", | |
| " <td>837.327975</td>\n", | |
| " <td>90.820915</td>\n", | |
| " <td>53.645830</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>77.864580</td>\n", | |
| " <td>37.760410</td>\n", | |
| " <td>36.979164</td>\n", | |
| " <td>46.614580</td>\n", | |
| " <td>45.138890</td>\n", | |
| " <td>44.849540</td>\n", | |
| " <td>51.215280</td>\n", | |
| " <td>46.585650</td>\n", | |
| " <td>208.333300</td>\n", | |
| " <td>60.763890</td>\n", | |
| " <td>204.571800</td>\n", | |
| " <td>214.699100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>2.549016</td>\n", | |
| " <td>56.727430</td>\n", | |
| " <td>56.032990</td>\n", | |
| " <td>48.220490</td>\n", | |
| " <td>800.000000</td>\n", | |
| " <td>99.999880</td>\n", | |
| " <td>22.251160</td>\n", | |
| " <td>23.596640</td>\n", | |
| " <td>24.348960</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>76.106860</td>\n", | |
| " <td>60.000000</td>\n", | |
| " <td>45.000000</td>\n", | |
| " <td>31.187550</td>\n", | |
| " <td>500.000000</td>\n", | |
| " <td>739.741500</td>\n", | |
| " <td>599.999939</td>\n", | |
| " <td>4.873250</td>\n", | |
| " <td>878.917900</td>\n", | |
| " <td>448.907900</td>\n", | |
| " <td>1107.526000</td>\n", | |
| " <td>594.061100</td>\n", | |
| " <td>1227.564000</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " <td>839.575000</td>\n", | |
| " <td>1214.420000</td>\n", | |
| " <td>2000.000000</td>\n", | |
| " <td>1841.146000</td>\n", | |
| " <td>1466.281000</td>\n", | |
| " <td>1600.000000</td>\n", | |
| " <td>1800.000000</td>\n", | |
| " <td>1839.211000</td>\n", | |
| " <td>1578.600000</td>\n", | |
| " <td>425.549800</td>\n", | |
| " <td>694.479126</td>\n", | |
| " <td>984.060700</td>\n", | |
| " <td>174.901200</td>\n", | |
| " <td>417.708300</td>\n", | |
| " <td>547.916600</td>\n", | |
| " <td>512.760400</td>\n", | |
| " <td>420.312500</td>\n", | |
| " <td>374.218800</td>\n", | |
| " <td>408.593700</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " <td>320.312500</td>\n", | |
| " <td>370.370400</td>\n", | |
| " <td>303.530100</td>\n", | |
| " <td>561.632000</td>\n", | |
| " <td>464.409700</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " sensor_00 sensor_01 ... sensor_50 sensor_51\n", | |
| "count 220320.000000 220320.000000 ... 220320.000000 220320.000000\n", | |
| "mean 2.372221 47.591611 ... 183.049260 202.699667\n", | |
| "std 0.402564 3.293904 ... 52.630590 105.693568\n", | |
| "min 0.000000 0.000000 ... 27.488426 27.777779\n", | |
| "25% 2.419155 46.310760 ... 182.581000 180.555600\n", | |
| "50% 2.455556 48.133678 ... 183.049260 199.942100\n", | |
| "75% 2.499826 49.479160 ... 204.571800 214.699100\n", | |
| "max 2.549016 56.727430 ... 1000.000000 1000.000000\n", | |
| "\n", | |
| "[8 rows x 51 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 16 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "CuWN5fG90cmi" | |
| }, | |
| "source": [ | |
| "### **Visualize the data**\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "eLbdA6NKGd8n" | |
| }, | |
| "source": [ | |
| "#### *Sensor Values*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 296 | |
| }, | |
| "id": "Q0mnCiZfoHdu", | |
| "outputId": "624af473-7838-4947-ec44-927f3c42f8ce" | |
| }, | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "sensor_values = df['sensor_25']\n", | |
| "n = len(sensor_values)\n", | |
| "sensor_values = sensor_values[:n]\n", | |
| "\n", | |
| "min_val = min(sensor_values)\n", | |
| "max_val = max(sensor_values)\n", | |
| "\n", | |
| "plt.plot( sensor_values, color=\"blue\", linestyle=\"-\",\n", | |
| " marker=\".\", linewidth=0.5 )\n", | |
| "plt.hlines(min_val, 0, n, color=\"green\")\n", | |
| "plt.hlines(max_val, 0, n, color=\"red\")\n", | |
| "plt.xlabel(\"Minute\", fontsize=10)\n", | |
| "plt.ylabel(\"Sensor value\", fontsize=10)\n", | |
| "plt.title(\"The first \"+str(n)+\" sensor values of Sensor 25\", fontsize=14)\n", | |
| "plt.grid()\n", | |
| "#plt.savefig(\"sensor_25.png\")\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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OhZkz9br5veuu+PJj6bhUtacgIheKyCIRWSgivxeRbiKyvYjME5GlIvJHEeniHNvVSS919g+uZt6ikMnAYYfBT34Co0bBtGlx56g4vvii+HPWrAlPW+LBCIKgtMVSKaomFERkEPB9YB+l1FeARuBE4DrgBqXUTsDHwJnOKWcCHzvbb3COi4VMBq65BmbMgLVrob0dNmyA887T+ywWi6WWmDqpFvVPtdVHTUB3EVkP9ABWAocC33X23wlcCdwGfNNZB/gLcIuIiFJKVTmPOWQycNBB0NaWv2/9ei0o6k2NZLFY6pdMBg45BL78Erp0gTlzqlsHVa2noJR6G/gZ8CZaGHwCPAesVkptcA5bAQxy1gcBbznnbnCO71ut/AVxySX+AsFw++1ZaV1L6V0phg6NOweWzkQ9fiNJY8YMaG0FpfTvjBnVvV/Vegoishm69b89sBr4M/D1Clx3PDAeoH///qRLnFi1Zs0a33MXLEgBXTAzesF0VLLpQw/dwLnnvs4NNwxBKaGxsZ0bb3yR3Xb7tOh8XHTRV3jppT4MH76a//u/hcX/kRDmzIFDDjkQ/Zrb2W67z7n11ucC56KtWbOGW255nvPO2xP9f9u45ZYXSaeL/18dhaByUmsOP3wXHn10IKY8Hn74KtLpJbHlJ8pzWbSoN+edNwJdlhS33DK/pG+kXqhWWXnyyT2APpjn+OSTq0mnX6z4fTailKrKAnwbmO5Kj0OriT4AmpxtKeARZ/0RIOWsNznHSdg99t57b1Uqc+bM8d2+9dZKaZlc3HLMMcXnYfTo3GuMHl3y3wkFoh03Z84cdfXV2fw0Nip19dXVyVO1mDtX53nu3MpcL6icxAEo1dys1Mknx52TaM/F+y1tvXX18xUn1SorI0fmPseRI8u/JvCsCqhXq2l99Cawn4j0EBEBDgMWA3OA451jTgXuddbvc9I4+//pZL6mrFhR2nn33FP8ObNmhafjoKUFGpxS0aVLfU1eM9Zil1+uf8NUFvWq1hg9un5MUVeuDE9bojFkSHi60lRzTGEeesD4eeAl517TgIuBH4jIUvSYwXTnlOlAX2f7D4BLqpU3SzCpFOy+u16fPbu+BtXTaVi3To8JffllsMumTAYOPTRceEybBmPGwP33D6xmlmOhVgJx003D00kmSY2G114LT1eaqlofKaWuAK7wbH4dGOlz7Dq0yskSMz166N8kCoSwyYQtLdDcrAWCiJ7964cRHqCPnTEje03QLsNNz2/WrJ3ZZRcYPz65ExkzGW0gsWCB9tx86aU6v9OmwfTpsNVWcMQR8OGH+plccIH+31275gv+iy+Gv/4VvvUtuK5Mo3DvPJlS5s3EgelxGmsf84ziev9bbRWerjSdfkbztGlw991w3HH6QyqHTCZZlUWpfPll3Dnwx/2xNjbCGWfAnntmK7sPP4SvfQ3+/nfdW/j+92H4cH2u+2M2lX9jo15+/Ws9D6WxUQuT1tbc+954o77OYYdpYdKtW/G9qFIrFPd5kO+mJJOB/ffPplevhgkT9OLGT725dq0+t7FRC4h16/ScHHBiaVCeYGhqCk8nlXRal4H29myP86WX9DyltrZ8YWreUe/evauibj3iiNz3d8QRlb+Hmzp5TdVh2rTsx1MJfX463TGEwvr1lbtWJVtX6bSuyEB/nLffHn58ayscfni2hSqizzGCoq1N9yxMryHIFPmtt+CEE7L3XrtWX3fQINhsM906f/112Hdf2G03WLQI5s2DHXbQx48YATffnG15nn++/i8mfeaZ/g0Sd/kM4pIKKFnb2vxb8ZMnlycU9t8/97tyC69qUmyZy2SyZp7jxunzjPBtb9fv849/1A0H0O9/4kSYMkWnTWOhoWEEzc3lNy69PPRQfrrS98ghaAS6HpZyrY/8rH+am0uzPoLiLV6892puLvnvhFKM9ZFSSg0bFv2cMObOzf7H5ubyLYIGDCj93ZS+tMdwz+Dl6KNzn0m179erl14aG5Xq0UMpEb00NW1Qm26qVO/eudZQbuuvzTfPvdbmm5f3/qMwd65S3bvr/Hbv7l/mRo7U/6ESzyf3OrllZbvtKvOfOpL1UeIZMSI/fcstpV+v2JbwhReGp+OiUuqjyZOzvY7167MqCT8KDeyNHQurVlUmX8URwd94Dam1g8I1a/RiehOmatqwoYFPPoFPP9V+mMaO1e+upQUuu0z/fvRR7rU+/rg6eXSXHdObDDI22HdfePpp/R8qQe51csvK8uW6xzFmTHn3qLX1UadUH2UyMHPmtjzzTO72sEor6nWLEQzucK4iyQl5Wyn1kVePHWS269WLz52bfY5jxmj9bdgs887EO+9k1+N10JhbAc6cCZtskm1QBDUsjj1W/370Ebz/PuyyS+4g+Asv6P177qnVJEuWaB3+qlXw+ef6GPdA+pQp8PLLTo4ELrooe6+2Nn1M375ZdcvTT1fo7xfBrFkweLBWVZYygF9r6yPf7kO9LKWoj+bO9e/qVWIpdqJXNi/BXd1KANGOM+qjrbaKfk6h+3oXL5Mm5R/T0KD3edV7dsk+Q3fZScoyalT8eQhapk4NLpO1XiZNKu47OuaY3PNLmSjrhRD1ke/GellKEQp9+hT/Er06vaClFB0i6EqwWgLB3CMKRij06xf9nEL39S5uTj45+FkOHRr/x5vURSmlRoyIPx/eZZNN4s9DoecWdx5AqS23LO47qvTYnH4OdkxhI6tXF3d8QwPstVe0Y5cvLz4/oItKkqik9ZEXo/LQKrzg44xKwOLP/Plx5yCfepmHEDcNRda6qRQ89phef+yx6ls4djqhUCwnnaTN1Lp0yW5rbq7Mtc2gqlKF3TKUSzGCp5pCYcIELRiq7enRohGp3fyAUiL9dUa6dUvGTOkgrFAowCabaMmcTsM55+jFSO1ycVtGhLllqDXVnrw2YULhOQaW0jHu0adO1Xb269dnGwXuxk2lqWZjohIUik9eK5YtK64RaCZtQvUbj9BJrY+KYfFi/ZtKVb7b5p79WG3nc0pF/yjMJJ1yGTkyHmuPjoyZEBeGsU6ZOFFP1HOX23Q620szs8FXr4brry/fwstM7uu8KKKaMJtGYJQ6xUx0LPa8Uul0QmHTTeGTT6Ifb2a7emlsLP8jSqWy1ynV+ZyxzTZuHryzOE2rYu5cOPDA6NethMqh2j5aymXLLbWpoFdwifir20T0YlxB+NGtm14++UTrjrt1073NVat0esst4d139fWHDNEf+Vtv6Ws2NIRfG6IFSTJC3a8CCWrcHHOMFharVsG990ZRN0avADsjpp5paNBLW1vuMy2mEdi3b/YaNfFcHDQCXQ9LeSap0RZjyuY/gu9v4VAMxqrAz6KgUGyAuXOV6to1e++GhlzT1rlzlerSRe/r0qWw1YKxPgJ9nXKZOrW6Vhx6Zq2eKXv00Uqdc45+BmZ/t27+53TvnvuujFWHOeb88/PPa2jImhyfc07u9qFDtYVaobLS3q7fgcmjeVemTJp1rwmieznqqOz1Ci1du+a+86jlc+pUPSM47NoHHPBeLJY71VjOOUebiY4e7W9W29zsbzrtXb7xjRU53yvocuHnJWGPPfRz9n7f3m9+6lR9vpk5HVbGigFrkup9INGXIIKESzGEzVPwTtf3K0Duysks7sA43v1bbBGeH7dQ2HTT4v6LH375q9QycmT2eZx4olK/+52+5/77KzVuXG5l6xacBxyQ3W6A7Me4ySZKHXlk/v28wraQKwUv5n5BQYzc+bn66lzh5l4KCQX3eV579mLKp3lGTU3+9znrrH8X9b4aGwsLmrgW7/82DSmzGOHarZt+vg0N+phjjtH7zP+65Zbn8p5focU0Ukx5NeWqqUk3NrzuOHbaqTKCwQqFvAeSvwS1av0wLy/q8UGERTm7+urcAuFe32ILvfhVHO5Kyq9SDvOb4hYK/foV91/8MPMdKrl0754/+efww5X6znf0/z7iCKUefDD3HbkrNvOBu9+Ve71PH6V23jn/vt6Kv9gIb+Yec+fqd+3u1bl7CuaYoPJVSCi4KzR3T8F7j6h5DvIFdsstz5VU8Zqe0NCh+toNDfr9nHyy3jZgQH65HzEiv6w3NOhzjjlGr4vo651zTm4DqlihYPLoLgONjdleqMmvqZhNOYDcyGs77VT5su9dBgyI/i7z322wUOh0YwpZFG6d6PDh+TrdIHti98CPl2JcXYQNNK9erV/9xty61t9/P/iaU6Zk7z9uXL6VT9SB30qY3X7wQfnX8OK1hc9kdCxqpeC++3SAIJHcd9TWlh0nWL/e38rrmmv081cK+vWDV1/N7mtoyH+npRoepFJ6bGfQIO2KGXItS8zY0uzZ/l5FH3wQevcOvv4ZZ2irI6X02IL5r373iIK73Ll54omAYBUFSKXgb3/T60HeTKdNy3VTfeutenvQ2Fml4xykUjrC3euv6/9vLLZEsmXpww+zxxoWLcq6zl66tPx8FGLVquDxr7IIkhb1sFSyp9C1a37LOshtRVhLrk+f4vLSq5fybcH17Vt6C8K0oorp/SiVrz4qd+ZkNVpHXq6+Ott9b2xUatAgpU47Tf938466dMmqkLw9BaPjN6qgHj2UOv303Hs2NZX3HMyzMHzrW0r9+c/++XeXuVKej59qK+wehfIcrPJZHzlPxbp1UKoysba3265w3hob/c897zyldt9d1wle1Y6fKlGXrQ054wm1WkSKfzZY9ZH3gfgv55wTfWA2rGtaDKbyj5rHalSshjlz5uT9r3I+ykrne/To/Hu4P9YuXfQHYlQzRiiaj3qHHZS66aZs3pTKrzC7dFHqH//IvW+PHqU/A/ezMBx3XFYoBFU2Ju+jRwePLwS9W2+FWsoYiMlzsFBoi5Qft1vtWhMlf35+hMy4glvfb7Z7BZW7/DQ0tOWMEdVyKf7ZWPVRQUS03bax5S9k018pO2E/FVWcsx0POig3PWpUciYlXXll/jajakmn4c03terBRMzydvG3204HwXHT0qLVAybgTXt7bSY5KZWff6P+8AZ/6to1PxqcH8bLrle15XePYvCacRdj1n3XXcXdq9YMGJC/LZ3OmpC6zXr9VIbu8tPUpKpvLloD7Ixmh4MO0pWI28a70q4Y/GIGeIWCe/ZirVm0qHfe3ItKTWRzc845ubEsolbCQTO+UyntTnncOF2BNjZGt+c2FeZVV+nfhgb4/e9zj1m3rrKC2vt/Tf5NhTN9eu7+KAIB9JyIILz3iIpScMghudu86Xpmzz3zt5mKPko5cpef669/sUNEXuzEPQWFe6D52mv1r/lglYJf/UpXNEEveujQ6I7bMhk49ND8gOmmEp42TQulN9+srpuJqVOD9/385ztV78YubrtN/5o4ClHdXhSq5KO2iE0Fb4wC3C3AtjZ48UX30Yr2dqGlpbIzSU1PwY9S379fq7dclMq/bjXuExcmfoObYntWpvyk059WI4s1p1P2FHr0yE137ZpbKRg2bAgPvGNcYHjxiyBmrGHcwcAzmayKY8IE+MlP4I47quvALCy26xtvVN+jmd9/++UvC583cmS0CrlQi3jRomA/MpmMfud+0c0q0XM05aJQz+jNN0u7frUi03mtnXr3hsMPf7fgeV27Vic/UdnJp43Tq1e0c0vtWcXBpEmVvV6nFAqffw5NTbr279Ej68oinc5vwS1ZUvz1f/xjrY7KZPTyn/8JDz+c3d/YqD/8GTNy79feroXSvvsWf89KoFRllel+hfXxx7Pr6XQ0dyFbbAHz5lUmTy++mO9Hxp0f48aioQG23roy9zSC5/LLtSB6/XVtlhmkkorigtobShaq43tIqXw33fPnw2WXLWH06Oy2xsb8cw8+uPL5KQZ3FDbQPZxZs7LuSrp00ZqAanDOOdW5rpfGxuIjuRWiUwoFgNNOW86wYfDoo9ltfuqJUls7bW1wwgnaJv3223VlaOZAtLdrddEdd/if5644K8nIkeH7t9vu87xt5bT2rrsODjhA2+SPGpUbZhNydbdhvaPjjis9D1722CNYX+zOT9euuhJvblaI6HSpFYgRPG1tenzg+efhT38K9ni5666Fr+kXT2Hw4NLyVwi/WOYAjzyi3+nVV+u5BF7BEMV5XzUxvXDQQv7739flb8cdYZ994OabqwUODigAACAASURBVNcT8BurKETXrrlaDD9B6+WAA4q/T0GCzJLqYSnVJHXuXKWamrRJndfkzGvqVWjWYK1Nz4pZJk3KRsMaMqTwc5kzZ05OxLOuXUt6vDlccUX4fmPmZ+YVeM0vy4005TbXO+QQpWbPDreB9+675ZbnyraXN+XKuC8w/zFozsDcudlZvSJK3X9/vvlhVPPKcjDXdc+8F9Fp9+xdwxZb5OYnSpmrJua5e11JmJnJlQ6B634mYa5K3ItIruuK1auz3+w55+SbBLuv2dhYev6xJqm5aJMzrSpxm5z56YzdrY16YvRo3VJ/+ml47jn4v/+Ldt7ixVnTTz8T0ErjHuQdPjw7a9UMAIYN9BfCO6Dsd8+w/ADsttunZZsZmutddZX+bxMnZk1gg67d3KyPMTPLb74Zzj8fjjoq+D4ffVRePoMweXT3rrwWUZlM/kz7dwsPO1QV89wPOQT+9391+pprsj32arqhbmnRLf8vv9Q9xG98Q6uQ3ebdDQ36mBkzsnl45hmtBmxo0GNtjY363AEDsj3V/ffX6qlyvo0wOqVQaGmBhgZFW5vQ1BRu1VINk8xq4J3ubrruRj8eB2PHwt136yn/UezVKxmzwhuYZPbsyly3HC69VP8a4Rdk2ZJOZyuP9eu1isZUwh9/HHz9UgeoC+EWaibPXvNgP3PhKGqwWnDQQdn/oL99vV5NN9RuC6Yrr4Q//EGPZ40bp7+HSZP0vBKvu47/+I/8OmfkyGzZMRgLvmrQaccUDO6K1E8P6N6fNJqbswNaXpPOX/9aFzIjFGr9P8aO1TGY163Tv7UePPcLTJIUolhImfellH6PN9+s0089FWzB1L17ZfPpvUdYnt2VrcFMpoubf/0r22tMpWDYMDj77NJjmETFvGf32MCyZfr35pvzGwVen2pmMNwtuNy932rRKYWCtjLSJb6tLVthPPRQbFmKhPeja2+HbbfV61Om5O4zztDa28MHrKZNgzFj9G8lmTkzN/3005W/Rxhm0BhqFJgkAn6myn54w70+91y29djertUHfkJ+4sTy8+jG3GPMGP0blvdUKj9PbiOOODD5nTOnNmEsC2FmSoN/Q8U7aW7ChFzBVauwnJ1SKLS0QHNze54FSinmp7XEa83hVn15J9GZfWHqI+NOYdYs/Xv//QMrneUcLrywqpfPwXTfIfthffIJ/O53ta8cvCaphe7fr19+utDclSFDwuegFMvYsdn1WbP0b6G8e4VCoShy1cZUukrlzg1avFjr62spKJTS36NpoPk1VNyzo9NprSIK6klUs/fbKYVCKqWnpBvXBubBb7FFvPkqhHHbC/r39NODu79mX1tbsFC4++7c9OOPV/cBRLG/ryTm2aRS+uOfP1+r1Wrdakyns+EUo3zM++2Xm95118LmxG+8UU4O8/HrNSdNDVcIU+m61TCm9+yeRFptzDebSsGJJ+r1INVVmGqxGPcb5dAphQJoqxLvw3/mmfjyE4XW1myh6NZND1oFVW7GUiGsp+C1/x81KiRQQ51jPv5aVgYGY4kS9WMeNy6r+mpq0hXI3Lnh51TaIOKII3LTfvrtpOO2PjKVsBn7aGiI5/+U42zR66erWuMhndL6KIhqzAitJCtX6sHls8/OVvpe53nNzbDLLtkCY8YU/HTQRt0wYYL2ibTzziuBXaqWf/cM2FrjNhGsdWVQii+ddFqPHZx1llZ11NpQwFiLPfSQnujV0lI4715fYEOHVjOH0XFbH6VS+vtoaYFTTqmdGwtjMGCcLRYb7MhQSQu9IDptT6FShDmYqyTG5LStTQ8um4rD6BgbG/XM0paWXP8ubW3hA81uPfRFF+1e1cHgRx6p3rULUatWVtj9i/Gl446e19yc7e3VMt933aXn6TzySLS8G19gDQ1aIAT5Boubnj3htNNq9yzNt1tooDkpWKFQJuPHw847V/ceAwZodZFX/eCnYxSB117Tk6TGjtWFMco8hQkT4NlnN2PCBG3t0hGpJydnhlQK/vIXOPJInd5883jzE4W2tmQIBKNafeut3O1K1SZmhsHcq9BAc1KwQqEC7L57cX5xGhqiB/MBbTUzZUp+K9ev9btggW7dffSRNgtdtqyYyWs6Mw8/nGvbbYmfQuWkUs77Ogpu880ZM/LLci2FgiGVgpNO0utx9FajYoWCi1J13iKw1VZ6fbPNCh+vlG6Z9+ypg80XwkQR82vlelu/XvfJn31W/IzmDRuSY9vd2clk4Pjj4YEHdNrMaPZWavvsU9t8JR23atU9FwngvffgvPNqO2/GjAkZ4Z1UgQBWKOTwyCP53fMoguLDD7PWIVFmlSqlZ0/36gV77x18nEhlrCSCBpoL5THJes/OgnF5Yd6f8cWV5Jn2USfpVRP35MXGxuz3M22aVifNm6cbZrUQDG4BHkcPpViqKhREpI+I/EVEXhGRl0UkJSKbi8g/ROQ153cz51gRkZtEZKmILBCRvaqZtyDMR2cGywoNjmYyWtXyr3/pdJSPtaFB36dQATHjAVOmlNeyKMX3UbkmiN7B7ShugC356ImW2XfYt6//cdVyhlcMxU7SqybuyYtux3HeuTnetKX6PYUbgYeVUrsCewAvA5cAs5VSQ4DZThrgCGCIs4wHqujyKRjTcmhv1+Z1hVoSZjKMEQYrVwYf29iY9YxoKlvv3AgRHXvAoFT5nlpLEQpu2+5S8Pq9SYofnHrDDDSb9xDU6Hj11drlKQjjZTjqJL1qY57ZNttkt3nn5lQyVkcU3n5b/8bdkwqjakJBRDYFRgHTAZRSXyqlVgPfBO50DrsTOMZZ/yYww3H3/RTQR0Sq63fBh2JbEqab6lfxmm2NjVotddxx2lrJVLYi/oVj2LDseZWwUihFKLhtu0vhoIPC05biePpp/RsUge7z/PhINSWTyW1AFfI+HBfjx+ugT/vtp83JK+kaJAwzT+G3v9Xpgw9OrmCoZk9he+B94Nci8oKI/EpEegL9lVKmPb0K6O+sDwLcxmMrnG01pdiWhOmm+g30NTZqL6a33qoHCP/yF7jzzux+kfyK14w3QGk29WHCqZxrFIt3Rqw3bQnH7Q1z7txch3h+fPZZbfIVxCWX5OZt6NDkDqb27av9CtVKIBg18eTJ2We0fn14/Pc4qeaM5iZgL+B8pdQ8EbmRrKoI0DGkRKSoITMRGY9WL9G/f3/SJfZR16xZ43vukiUD0bN6lZN+lXQ6RCfkcOCBA3nmmZ3RQY0EENra2tmwYZmjItqe9nahtbWdO+5YRmvrm3z5ZYrW1ueBlHM/QaSdBx5YCQyid+/naW39tKhu+JZbjmTVqu5OHhQ9e27giy/WsnDhm2y++QcBZ7Vs/L8A2223hmXLPiSdXhb9xh6eeWZbdLtA5+OZZ95g552r5PA/lJaSywgEl5NqsmhRb374wz2ARg45pI3TT3+DhoYdaGtrQEQ5Hn5NOcNZV6TTVYrj6oP3uTz//P5AM+Z9L126nnS6gG+OmtDC/fd/zBZbvMFuu30KwDvv7MfJJ7fy9a+v4hvfKPxtRyWorLS1Hci//pVh9uyRQBfMM5o9+0vS6QR2F4JCspW7AAOAZa70QcCDwBJgoLNtILDEWZ8KnOQ6fuNxQUup4TiV8g8nqJRSo0fnhr8bPTra9W67TYfuPOYYHcaysTE3BGD37rnblFJq0CCl3npL38fs79JFn2+2FRNuzx3G0YTu69FDqa23VuqnPw0+zxsi8NBDC4fRLMSAAbnXLBTWtFq4w3GWQlA5qSZXX50Nw9jYqNS4cdm0+/16wzrWEu9z6d07Nz+9e9c2P374heOcOjU3nyYMZiUIKiu9e+swmz175t67Z8/K3btYCAnHWTX1kVJqFfCWiBhnOocBi4H7gFOdbacC9zrr9wHjHCuk/YBPVFbNVDNKGYjKZLQv+1WrtLXSTTflqn6CXCy0tmaDp5x6qvZpdMYZWVVBsYN1Ok5E7rYvvoAVK+Cyy8J1mNpJmKJLFz1ZzjsLtFi88yW8aUsw7pmv5te4RwgaaN5uu6pnKxRveE5vOg78XGefd17uMd50tVAKDjggd5s3nRSq7RDvfGCmiHQBXgdOR49j/ElEzgSWAyc4x/4dOBJYCnzhHFtzxo/XsXAPPBC+851oesd0OrciNxPN3HgdWWUyepzhZz/T6WnTtFXSlCl6cLkUx23GA6TRW3r1z0HBWQC23x42bPiC5ct78txz2tXFWWclVy/c0TFCoK0t2iCyCbYUF14vrUkIY+vnOtsdIxny09XA7ebCxKZw5y9pVFUoKKXmA35zLQ/zOVYB36tmfqLS3Az33pvrWC4MY4FUTEXuduVsfo1AKcajpptUCnbbDV56Kfo5htdfB6V65Gw74YTyewyW4rnkklyh8PDDhc+J2yeSd85NEiZpuV1n/+//xt/AaWnRVlkbNuhJrp1SKHQWinWNDFlB0tqajXlgBEo57nFLbfnoHkTul7xiRWnXspTHggW56Sg9hbgnr3XrBmvW5KbjxqhLBw/ODaT1vitsSK0Cayml83DKKTrQU5J9H1mhUCGKrcjdgqRvX91DKLZn4Ic3lKOl/ujeHVavLu6c92OOj7TXXvD447npOMlkdA8BtBm4UYXee69Woxruvdf//ErS1gY33KDNss1EuqQKBLBCIVaqETCjvG6728yxPBobsyoQk7ZEY8iQ8Jnxfhg/P3Fx7bV6Jv6GDVpFcu218eZnxozsYHdbm06b723QID1eePzx1a+cMxndg7r6aj1+aLykJhnrEK+DUa5LjEphYtEGpS3BmBntxWA8gsZFKpXtKTz+ePwt4TDrt27d9KTSWuTRjB0a1x/Ll1f/nuVihUIHI0x9VCjOb6V6CaAD/YSlLcG4YzRH7QHUSjcehjvOR5JZt067uKiFmwm3BVRTU9aLclJdXIAVCh0Ot1CYNEn/HnpoYesL7dqicv6Yja+eoLQlGBNqFbLzWApRSu+iI/Pkk/7pTAbeeUfr+GvtybWtTce8hvi9yIYRPSaXSI/CR1mSxI036t/CPQRdqRx99Ds525Lss7+jYwR4VHVgMZH/OgPe52bSZoKnMQGvtgcT9wS6DRs6SIxmEdlfRBYDrzjpPUTk1qrnLEbquTJ0myaagbb162HRomjn33ADPPEEXHFF5fNmKZ4otuwjRiRDZeN24hc3JhKiN23imFfKA3EhvFZkxtlkvcdovgEYA3wIoJR6Ee0Su0OThMk3peBnmtjcrCe1hbFoETzwwFZMmhRdgFiqT5TKfvDgqmejIG4T0EMOiV8w/OlP/ulUSguIiRNrM1dg/vzctJlkWG7grGoSSX2klPLOa23zPdASO1tumV03g5S/+hXsvHP4eWby2vr1Wb1nOSRxhmtHZcCAuHOQawLa2poNuBMX3ln97nQtrY9GjMhNm578xInxC84gogiFt0Rkf0CJSLOI/AgdQc2SQNzhGidOjC8fXisoO6mudDbbLHy/ib9hyTJ9em7arQ6tpXrYG3HQuLVpba3jMQXgHLRPokHA28AIEuKjyJKPe0zBBPE466yo4Rr119KzZ/n52H778LQlOiefHL6/Ej27cnGb0YL2pRUnH3+cm161Ci6+WK+vWwe3315bk9TGRm2SamhvD463HTcFhYJS6gOl1MlKqf5KqS2VUmOVUgmZImXx4mdptG4dLFwY5Wyt45k5s/x8LF0anrZEI5PR6j/DDjvkH/POO/nbak0qlTtwOmsWjB0bW3b4wCee1OTJ+nmuXFk7k9RUCnr31k4Ozzoru72hITkTTb1EsT76tYjc4V1qkTlL8axb579dR3+LzqOPlpcPr4O2uB221SvpdK6TwyYfxzRnnlmz7ITidgsNlWlclEqQQ75am6SC7kFdcIHuTZn317VrfVsfPYCOmPYgMBvoDawJPcOSOIrtzid1EKwYkmQiWSotLdp6zGBUDu4gPMOH1zxbiWfIEP/txiTVzDCuZcWcSukgWpBsL6lR1Ed3u5aZ6KA4fjESLB2IoADx9UImo9UDkOzZo4VIpeCvf82mzSCpO95CUgLAe11yxOmkLyy+hAmIGUcZNxHykioQoDQ3F0OALQseZUkUXbsWd7y7dVoKxsVGULrapNNZJ3FJnj1aLH7uQqIZEVQfr0uOOJ+5n5nuwIG5prLr19fOdLaeJsRGGVP4TEQ+Nb/A/cDF1c+apRSCTD+jWTpkS+7IkeXlw22KJ5JvmldtTBAjSPbs0Si4/fj4tW7jdpsNuif2PY9NYikRACuFn5nuKafEEzu83uboRFEfbaKU6u363VkpdXctMmcpjmnT/K0uQAf4iIpI8T0LLy0terCvoUH/1rpSNkGMINn62yi4A7w3+HyxxQbkqQbuOOWGu2OsJfwsez79NL8HkYSJf0kjMMiOiITGTlJKPV/57FjKwTjA89LUVFxg96am8l0npFJ6AtH998P3vx9PpVwvrpwL8dWvZtdHjoSnnsrdb9wxx0lLS35gpeOOiy07vj3jp56C//zP3G21mvhn1Ef10GsIi7x2fcg+BRxa4bxYyiSoK7xhAyxeHOUKusQ2NWXDBlqShd/s5rhDXxqMUBCBiy6C8ePjy4vfhL4lS+Cqq3K3XXVV9fNZD4LATaBQUEodUsuMWMonaI4CFOfkbu1aeMvr7apIMhltP//llzoObr2rcJKCXwUTd3xm0Ooj00toaKj9GJIXvwl9a9fCihW527zpalMPAiKS9ZGIfEVEThCRcWapdsYsxRMWB7nYQb9ly8rKykbrn1pOEuqoFLKQ8Tpdi4OkDewHzVOwFCaK9dEVwM3OcggwGfiPKufLUgLl60e14rOhofwxBVNJNDQko5KoV6ZNgx/+MJt+8838Y+JulUPyBvaDek/eGeF+M8SrQT2NKUTpKRwPHAasUkqdDuwBbFrVXFlKIsxxWrTCqA869tjyxxRSKfjlL+HEE5NRSdQrXgsev3Gjm26qTV4KkaSBfb+Y1b16wdFH527zpquB+9ubN0//TptW/fuWShQ5uVYp1S4iG0SkN/AeYIchE0iYg6299y58vohCKeGoo2D58vLzs8ce2jImCZVEvXLccbk+hQYMyDc7roWtfb3xwgv522bN0mrUe+7JbivGVLtUvvxSC+62Nvjb3/S2CRP0b5yD8UFE6Sk8KyJ9gF8CzwHPA3XqNKBjEzZBbdMIfbsBA9YhoivzSlBPsziTyvDhuWNFvXrlH1MPKola43XAuPnmunHibjjVwlNpJqPdeE+enO+O5Oqrq3vvUinYU1BKneus3i4iDwO9lVILqpstSyn85Cf+27t0gVERAqiuXKldS37vezBmTPn5UcpWWOUyY0au7b+ftUwS5ikkHTPBzz221dhY/bGudFp/A20+sSr9xoeSQJSB5vtE5Lsi0lMptcwKhOQSNLj2m99EtcbQNfhTT+U6YSuVF1/U16lXZ3RJwKsaMiEv3fjpzzs73vkc7e0wbFiuFd769dV3xWFm9vtZBia1Jx1FfXQ9cCCwWET+IiLHi0iAt3JLEok+uSlbSsv9WDIZrS/9/e/r20tp3HjdMPjFCfjOd2qTl0IkyVW5Xyjal1/OD9PpTVcaY5V11VX5qr8kWI35EcX30WOOCmkHYCradfZ71c6YpXj8/OIA/PGPtc0HZIPD2HkK5eENczloUP4xSahckuaq/N//9t/uFapBwXgqSSoFl16aazAgAn//e/XvXQpRJ691B45Dx2v+KnBnNTNlKY2ggeYrrsjakNcKExzGzlMoj1QKrr02m/Zzc5EUh3hJclUeFGNi2LDwdDVJpbKeZJ98MrlWeQUHmkXkT8BI4GHgFuAxpVSdh2DpfPj54Q/Dr/IphlRKB0efPRvOPTe5H0DSyWTgxz/Opr0B6QHmz69dfoIwkxXXrk12I8A7wbNWDvEMZlJokr+HKD2F6cCOSqlzlFJzrEBILkOHBu+LFh8hayp07LFlZ4elS2Hhwnj96tcrRv0yY0ZujGa/WNdxeiM1JG1GcxC1Nkn1YtzHxK1eCyPKmMIjSikfgypL0gia0fw//wOHRvJpmx1oLrf7P20a/PSnuhU7YUKyZ3AmDbd+/o47cseKTJjJqVNhhx30b1ImQCVpRnPQ+JqJ0dzYqGOG1LJHk8lkv4MkjLsEUUo4zg5PWxv87GfJfWlBBLV6/vzn4q/1+uvl5aXWVh4dCbd+fsOG3Ghrb7+tf8eP15HEkiIQkkbQ+Foqpff98Ie179G4AxElYdwliFChIJpO5dIik9G24FddlWxp7kfQh/DSS3D55bXNy1ZbhactwZiANaBbtW57duPSvJ7KZRz4zecwdO0K559f+x5NS0vWAV+Sx11ChYJSSgEJNZyqDul0NlhIkqW5H37+XgzlusIulp13Dk9bwjEzwRsatBWXl3oql3HwjW/kbzOqt9ZWuOWW2gvWVAq++U29PmVKMtRsfkRRHz0vIl8tfFjHwFhRNDYmW5r7EeYYrRb22G68FjFJsJCpF9xqhvZ2OOmk/GOSXC6vuSb+nsxdd+XO7wD9TWcyWs36s5/VXhOQyeiAU6An18X9jIKIIhT2BTIi8m8RWSAiL4lIZFcXItIoIi+IyANOensRmSciS0XkjyLSxdne1UkvdfYPLuUPlYt7BmKSrSj8COsNlDLBqZzBYa9FTBIsZOoFt5qhqUm7HzeYHlcSy6UpLz/5STJUr9/+dm569GgtcJWKRxPQIcYUHMYAO6JjMn8DONr5jcoFwMuu9HXADUqpnYCPgTOd7WcCHzvbb3COiwUzAzGJH14YQbM4AQYOLP56F11Uel6GD89agDQ06LQlOqbyML+GHXesfV6ikMlkJ2a1t2sVTdyV3m675adND0tEC9xa9rg6xJgCgFJqOdAHLQi+AfRxthVERLYGjgJ+5aQFLVz+4hxyJ3CMs/5NsjOl/wIc5hxviUjY4Fop7rA//bT0vEyenLWaaW8PnmFqyWfy5KxXzba25ATRCSOdzrWSqoUH0kJ4DS+86Vo7pEul9EROSLYWIsqM5guAswHjN/MuEZmmlLo5wvWnAJOATZx0X2C1Usq0f1YAxpvLIOAtAKXUBhH5xDk+J6SIiIwHxgP079+fdInNkTVr1pR8bnI5EGgkOwlNOeuKmTPbOOWUJwLOa3F+3TJYAYp0+vGScvLCC/sAPTfe/4UXPiedfraka5VHS1nvOY5y8sorewK9Mc9uwYIv0M8SPvroQ6Av6XSaZcsGk04vq2neDN7n0rt3b5qb96C1tZHGxnbOP/81WltXxtpbeOCBIZjqRaSdZ55ZxjPPAGyPUsKXX7Zzxx3LaG2tjA/rKGVlw4ZBwBBaW9Ox96QCUUqFLsACoKcr3RNYEOG8o4FbnfUW4AGgH7DUdcw2wEJnfSGwtWvfv4F+YffYe++9VanMmTOn5HOTSu/eSun2j/8SRCnnFKKS1yqHcu8bRzmZOjX3uTU3Z9f33Tf7n664ouZZ24jfc5k7V+dt662VmjSp9nny5sX93Lp21dsmTcp9tpXMZ5SycuON8X0LboBnVUC9GmVMQQD3jOY2cpuUQRwA/IeILAP+gFYb3Qj0ERHTQ9kacKbj8LYjJHD2bwrUeBJ6fZNU/+yW4hg/Xs9UBjjmmFy1jJ+bi6RgwlyuWKFVYBdfHF9evO5Bhg7V6hqvKq4eVHO1JopQ+DUwT0SuFJH/Bp5C+0MKRSl1qVJqa6XUYOBE4J9KqZOBOcDxzmGnAo6RFvc5aZz9/3QkmiUixg7bUv+YmcqTJuWaVib5Hd98c3g6TubP19ZR69blbvemLdEGmn8OnA58hG65n66UmlLGPS8GfiAiS9FjBkbATAf6Ott/AFxSxj06JUkJtmKpHKlUNtg7JFsoJKnCHTcuPxTs3Xfnb6u1Kct99+nfOHtRhYgSjnNHYJFS6ibgJeAgESnK6l0plVZKHe2sv66UGqmU2kkp9W2lVKuzfZ2T3snZX6b3nc5H2FyErl1rlw/ID0qe1CDl9UA0D7fx43W3Xq779XJIpeC7383dNmIEfNUzDdebriYXX5z1JBu3ei2MKOqju4E2EdkJuB2t9/9dVXNlKQm3HbSXIL9IuVROW2eC7IjU3hulJR7OOis8XWvc8xREdKPpmGNyj/Gmq4k37nkl4qBXgyhCoV1pE9JvAbcopS4CSpgKZak2qRT06+e/b5NN/LcXYsyY0vNjsCNDnQP3vBZTCceJaYg0NGg3Ly0t2QF8gzddTdwD337ppBBFKKwXkZOAcWizUgAfF12WuBkzJtj/kTdouD/5CtZ//KO0vJgp/Urp38TaZNcBxUbNi4NMRsd+MCRhxq6ZHHbkkdnJYss902696WoS572LIYpQOB1IAT9VSr0hItsDv61utiyl4A4M7mXNmtKuWWorv2/f7Lnt7VHVVxYvmUxuFLykmqSm09lZ2CJw+unJnbFrCafgjGal1GLg+670G8Tol8hSGkuW1PZ+H36YjQUQR9jDjkI6neu+JKlCwR2jubGx9rGP/TAO+R58UPcUzCCvJZwo1kcHiMg/RORVEXldRN4QEWsZZAnFVBINDXaguRz69s2dvOYXWyEJpFI6RgDo/CbBNbRRWSqV9Uo6aFDuMd60JUJPAT1/4ELgOXJnNlssgaRScP31etLQGWdYVUKpeAMnvf9+PPmIwkMP6d/29mwlHOd7dw80mzGOlhbYf3+9XaS0ULUdnShC4ROl1ENVz4mlqpjwjsVSjo38brvpxQqE0vGqPJIqFKZNy7q5AF0Rx907NOXuqKNyXeFvsgnstZeex2DLZj5RhMIcEfk/tJfUjdpNpdTzVcuVpST69IHVq/33lTLQO3IkzJtXen4WLoQXX9TqI/vxlcZrr8Wdg2jcfXduulu3+N+5UV8tWKDjlKdSettnn8ETT2irruHD489n0ogiFPZ1fvdxbVNoB3eWBLH77vB4gKfrbbYp/nqffVZ6XjIZ+P73tT73jjv0R2g/vsoxbRr89rew1VZZP0lJ4rPPtIn0DNYnGwAAGQhJREFUI4/Ec/9MBkaN0uvLl8OECXrdGDy4I6/ZcplLFOujQ2qREUv5nHxysFDw6qaj8PLLhY8J4oQTck1STzgB3nqr9OtZcjGVnPmNWzD4zUN59NGaZ2Mj7tCXhiuvzPZo4oi8Vi9EsT7qLyLTReQhJz1MRM4sdJ6l9oSZfbotWGrBihXhaUvluOaauHOgW91eal3m3PhV9u++m103kyot+USZvPYb4BFgKyf9KjCxWhmylI5t9XRObA8sGt6wsG1tNkysH1GEQj+l1J+AdtChMrGmqRZLYmhLwNeYtPkTQW5VnnoqPG2JJhQ+F5G+OC40RWQ/4JOq5spSEta/kCUujjoq7hzkEtRrTlLMh6QSRSj8AB0VbUcReRKYAZxf1VxZSqKlpfT5CH5ssUXp5558cnjaUjnckdniYtKk/G3dutU+H4Ygi6Lddw9PV5OhQ8PTSSFK5LXngYOB/YEJwG5KqQXVzpileFIp2Hvvyl3v3nsLHxPEXXdpk8BNN9UC4a67KpcvSy5HHhl3DnTZGz06d9txx8WTF4NfJXzttdl0Q0Nuutqcckp4OikECgUR+aqIDICN4wh7Az8FrheRBAcF7NzstVflrlWu/faFF8KcOVYgVAOjw29u9m+lx4GZk7D55sloCCxenF3fbrvctDFJrSXGHxhA9+7JNQwJ6ylMBb4EEJFRwLVo1dEnwLTqZ81SCtttF3cOsihV+xi4HY2g5/ftb+vfE05I3uSrDz+MXyBAdkazCLz3nk67neS1tdV2HC6VglNPhf79tfPApL03Q5hQaFRKGUe93wGmKaXuVkpdDuxU/axZ6h0rFMonKGLe75yAuDNnJjfWb9z4eUk1rfPGxtoHAspk4M479XyJJHiRDSJUKIiI6WAdBvzTta/GHS9LPWKFQvmMGFH4mBtvrH4+6hE/L6mplPYRdvHF2WhstcI9y9oIqSQSJhR+DzwmIvcCa4F/AYjITliT1MTy5ptx5yDLkiUwfXpyW0T1wLBhhY9xB+GxZDEV/tFH5wqA5ma44ILaq29aWrLjGEkIVxpEoFBQSv0U+CF6RvOBSm0MzNiANUlNLI89VrlrlVOZZzJwxRVw88268FvBUBpJiGBmqQypFFx+OXzlK7XvpRRDqEmqUuoppdTflFKfu7a9at1mJ5cPPqjctQ47rPTKfMYM3VU2+twZMyqXr85ElDCmDVFmG3VCTNm97z44+OBkNEx22QV23TW5AgGiTV6z1BGVVCWUo/f0+tf3pi3RaGkpPC5Tilv0zoDbr9H69dn0Rp1HDLz6KrzySjIEVBBWKHQgpk2DTwJGe0qZ6VyO3tMbISypEcOSTiql41KE8eMf1yYv9cY77/in16yB/farvdVWJqPddy9cmGyVqhUKHYjp04P3FetyoFev8vSe3tattUIqnSeeyN/2ox/p30mT4o+lkFTOPDM/ffHF2t/RG2/onkMtBYNRqUKyVapWKHQgwir+9ev9twe1VpqaytN7fu1r4WlLNMaOheeey99uzFBvvjm5Lc4k8te/hqctVih0KMLMF/2CoEBwa6VcFwCPPKLj33brpn3ixBWWsd65/37/7UbIt7Ym1949bkxUOnf6W9/K3eZNV5Nx47LuSbp21ekkYoVCB6KShWy//cq/xiWXwPz5ViCUQ//+4fvb26Fv39rkpSNw3XW6Qt5+e616u+662t07ldJm2rvtpn2CJdUCyQqFDkQphax378rnw2BnNJdPFEuZmTOrn4+ORK9eMG9ebQVCPWGFQgcibNAsSLUUpJ6oRNB1KxTKxx1XOAgbPaw44iqTmQz8z//AokXlzQGqNlYodCB+8YvgfUGO1T7+2H97JeY7TJ6sg5iMGVP+tTorUXp/QUYElmTREXwfWeqMzz8P3rdwof/2Vauqk5cxY+Cll7T536xZVjCUSo8ehY+pZLS9zkBck9fq3veRpWPRtWtxxytVXvd21qzwtCUaS5YUPqZfv+rno94oVHbjUCGlUvBf/6UHmuvW95Gl4/DZZ8Wfk2S9Z2dhl10KH7PzztXPR73hp5ox5qBxsuuu+p0mVSCAFQqdhvb24s9Zuza5es/OQpRQm153DhZ/1YzR51vCsUKhk1Cq3tkKhXhJpWCHHcKPiWKh1Nnwa4nH6QivnqiaUBCRbURkjogsFpFFInKBs31zEfmHiLzm/G7mbBcRuUlElorIAhGpYAj6zkGYC+W2ttKuWaq5Y8+e4WlLdA4+OHx/klURScQKh3Cq2VPYAPxQKTUM2A/4nogMAy4BZiulhgCznTTAEcAQZxkP3FbFvHVIqiEUCrVSg/j5z8PTlugMHx6+P6lWLEnGzp8JpmqxlpVSK4GVzvpnIvIyMAj4JtDiHHYnkAYudrbPcCK8PSUifURkoHMdSwSamyurN21ogFtvLe3c8eO1d8/ly+Hkk60nz3IImktisG4uLJWkakLBjYgMBvYE5gH9XRX9KsB4dxkEvOU6bYWzLUcoiMh4dE+C/v37ky5R6b1mzZqSz00qO+20By+91AfwNoMUoEinH/c5axT5HUZ9/E03zae19dOSxxX69x/I0Ud/xJZbtsY4NtFS1nuOt5zovG+yyWbAHp59Cv2eFQ888A477/xaTXMW/FzKe96VxV22s9/Ahg0H8MQT89h008qOPEcpKwsX9uP99/uTTi+q6L0rilKqqgvQC3gO+JaTXu3Z/7Hz+wA6FrTZPhvYJ+zae++9tyqVOXPmlHxuUpk7VymtMfVf/Cj2+GKYNk2pt94q/zrlUO7/iLOcmLw//HD4exo1qvZ5C3oulSg3lSKoTG+2mVIffFD5+0UpK3ffrdSxx1b+3sUCPKsC6tWqWh+JSDNwNzBTKWU8l78rIgOd/QOB95ztbwPuwIJbO9ssVSRoUluldK5Wd1s+Tz4Zvn/58trkoyNhy2Uw1bQ+EmA68LJSyj3MeB9wqrN+KnCva/s4xwppP+ATZccTiuKSSwof42Xfff23lzujGfTszW23DY/zYCnMZpuF76+Wq5KOyoYNcMMNdmJmENXsKRwAnAIcKiLzneVI4FrgayLyGnC4kwb4O/A6sBT4JXBuFfPWIXn99eB9PXv6fwRhFXY5M5qHDdOVVXs7vPyyFQzlUGiguRLOCzsLmYye3f/TnyY7TnKcVE0oKKWeUEqJUmp3pdQIZ/m7UupDpdRhSqkhSqnDlVIfOccrpdT3lFI7KqWGK6WerVbeOirf/W7wvi++8K/kwwLzlOPJ8eWXw9O1wPzXev/wDzggu+5ndmwd4uUT9M5NpEGlkh0nOU7sjOZOgvkIvJX8Sy8Fn5NkT46FyGTgoIP0+kEH1bdg+OpXs+u77pq/f8sta5eXeiExBlA+JH3ynBUKHYjf/a7wMd5K3gSA9yPJnhwLcckl2Ql7bW2ljbckhWeeya4vXpy/f8iQ2uWlXghqzJiesUg8cZLrYYDbCoUORCEXym1tcM89udv8KhlDvQoEyB9fCRtvSTp/+EP4/s03r00+6olUCgYMyN223XZ6e69e8JOfJDtOcpxYodCBCAuyY5g2rfr5gHzdd5gLjmrgHV8JG29JOu+9F77fK+gtmr/+NTf9+9/r37Y2Pds+THVaLZYsgVdfTbY60wqFDsRrESa1rl5d/XxAvqvuUlx3l8Mxx2QHYBsbdbpesWMGpeEdRJ4xQzeK1q7VvYQJE2rXSAItCK64QvfOkxyrxAoFS4fEO9CY5IHHQnznO3HnoOMwfXp4uprYGM2WumarreLOQXm0tGjrqcbG+raiArjjjrhzUJ/4DSp365Z7jDddTWyMZkvN2cPrM60M6t3zZiqlraeuuqq+raguvhj+/Oe4c1GfmHd+7rnZQWXvJMpaTqpMpeC//xuGDk12mbRCoQNxWwUjUCR9MCwKqRRcemlyP74ozJwZdw7qn9NPz5aBuE1Sd91Vx9ROcpm0QqEDUUlritbWZA+GdRa22CLuHNQ/7rkBqZR2+WJNUoOxQqEDcffdlb1eOYNhvXqFpy3R2G+/uHPQ8Whqgh/8wAqEIKxQ6EBUulVZzmBYU1N42hKN3r3jzoGls2GFQgfCO09BBI46Knfb3Lm56VGj/K/V1FTeYNhnn4WnLdGYPz/uHFgqjfV9ZKkZXjPSbbeFvffWguDQQ/Wvt5L/9FP/a3XtWl732hu8Z9NNS79WZ+a44+LOQf1ixsMWLIg3H26s7yNLTZk0KetOorFRDxQXYulS/+3lqHumTdOuut1cc03p1+uMmApt+PDCx/boUd281COZTLb8n346jBmT3Zf0lnrcWKHQwXALBYAVK/THMWeOvzWR8STqxetMrBj8BryjVG4WjbtCO+ywwq3LwYOrnqW6I53W7iwMs2bB2LHZdD202OPCCoUORDqd6y76ySfhb3/TH4dS2szUa03k51enuTk3sEslmDy5stfryKTT2vIL9G+hlu0FF1Q9S3WHn4GEcRy4YQP8/OfW3DoIKxQ6EO5p9CLaI6M7lGN7e/5MZa9fncZGuPBCvX7NNf4fTiYTvA90q8yL9eQZHfd7DFLjGUuzSZNg/PiaZKuuSKV048bNhg26zH7xhQ7Haefh+GMNBTsQqRR873swZQp07+5v8fPCC7npPn1y021tulUvolVRXbrkWiFlMrrSamvL32epHKZ3ENRLeP99/TtlivYAm4R34A5/moT8rF+fm3b3lNvasvNwkpDXJGF7Ch2MlSv1b5AJ6KpVuencbna2BlJKfzhelZNRbbg/Kktl8aoBw0hKnGHvOEhSW+CmvMfpKDHpA91WKHQwnnoqfL93ADm3lZQ/+tbeDnfdlfU7X+ijSmplUE+0tOTGgqgHvOMgSW0spFLaWuuyy+Lp5dbDALcVCh2MQpVIUA9CWy0p13qWxYt1QJJjj836Vzr77PyPyt1atJSHW33knfPhZc89q5+fQnQkV+WdHSsUOhjr1oXv93rdNC17d2S0oChp99yjhQPAnXfm70+nC9/fUpgZM7L68PXrtQovjFtvrX6eCpFEV+VDh+anzUDzVVdpwWV7tvlYodDB+PDDwseYLmx+yz5639ZPRbB6dfL1pR2RRYvizoEmaa7KFy/WgqChQf8uXpwdf1EqnvGYV17R7miSLIysUOhAZDKFW5VuvBN8isFPRZBUPXK9MW5c1pzSa1bphwnxaMln8WI9WL94cdw50d/n5ZdrwZDkwXgrFDoQl1xS3PE33VTc8e7QhX4qgrDQht5xCks4pjcXZWCyHgYvk4J3/KWW4zFuq7IkD8bbeQodiGIcf40Zk2+eWgj3eIHXTLWlBR5/PPjcSkaF6+i4K48ovYDu3auanQ6FtxzedlvtJv+Zwfgvv0z2YLwVCh2III+nfvjNOi6Gyy/XliYiuuIqZPVkZ91Gx115iAQP/BtsTyE6Xvfy3nQ1MYPxphGVlLEXL1YodCAKVR6VpK0td2JVoUlWlui4K4++fXXg+bDn+/nnNcta3TNkSG6MiiFDanv/VCq5wsBghYLFkkC8lYcxBbaUx623woEH6gZUQ0MyzHmThh3+60CUr0aw9qRJxKreKkcqBU88AVdfrX+T3mqPA9tT6ED07Alr1pRzBauctnR86kGFEye2p9CBOPfccq+g6N5dCxeLxdI5sUKhA3HddTByZDlXUMyerQOQWCyWzolVH3Uw5s3T5ozbbKNnK7e2wkcfRT//pZfyYy5YLJbOg+0pdECamrSvl5UrixMIIEyYkAyXAJZcJk2KOweWzoIVCh2MTEb3EB58sJSz9UCz9XSaPP70p7hzYOksWKHQwSjP66M2ST3zzIpkxVJB7AQ1S61IlFAQka+LyBIRWSoiRbp3s2Qy8KtflXcNGwg+mSQhkI6lc5AYoSAijcAvgCOAYcBJIjIs3lzVF+l0vgO1Yie0vfpqxbJjsVjqkCRZH40EliqlXgcQkT8A3wSqMuw5cf5E+izrU41Lx8ZjP34UyPVMp1Q7eqygkPzXqqN77lFsc+TvgO9SmclsClC0/ObQClyr9qxevToR5eSxWebdet9JPM83Kc8lSdT6maRPS1flukkSCoOAt1zpFcC+3oNEZDwwHqB///6kS3RK3tbWxurVq0s6N7mYit9UHCpg3Y/sce89c2CE44ujXp91cspJuFCvdR6T81ySQ62fSal1XyFEJSR+oogcD3xdKXWWkz4F2FcpdV7QOfvss4969tlnS7pfOp2mJakOzUukMr6PhEmTYPLkCmTIfeVkFLOiSUo5KfRua/18k/JckkQ9PRMReU4ptY/fvsSMKQBvA9u40ls72ywR8asYlCqmwlBMnapnRs+dW918WYoj7Bna52upJElSHz0DDBGR7dHC4ES0YttSBEEVRJSKI51+fGNLJ5WylU3SsO/DUgsSIxSUUhtE5DzgEfSI2h1KqUUxZ8tisVg6FYkRCgBKqb8Df487HxaLxdJZSdKYgsVisVhixgoFi8VisWzECgWLxWKxbMQKBYvFYrFsJDGT10pBRN4Hlpd4ej/ggwpmpyNgn0k+9pn4Y59LPvX0TLZTSm3ht6OuhUI5iMizQTP6Oiv2meRjn4k/9rnk01GeiVUfWSwWi2UjVihYLBaLZSOdWShMizsDCcQ+k3zsM/HHPpd8OsQz6bRjChaLxWLJpzP3FCwWi8XiwQoFi8VisWykUwoFEfm6iCwRkaUicknc+ak0IrJMRF4Skfki8qyzbXMR+YeIvOb8buZsFxG5yXkWC0RkL9d1TnWOf01ETnVt39u5/lLn3MqFaKsgInKHiLwnIgtd26r+HILukQQCnsmVIvK2U17mi8iRrn2XOv9viYiMcW33/YZEZHsRmeds/6OIdHG2d3XSS539g2vzjwsjItuIyBwRWSwii0TkAmd75ywrSqlOtaDdcv8b2AHoArwIDIs7XxX+j8uAfp5tk4FLnPVLgOuc9SOBh9CxN/cD5jnbNwded343c9Y3c/Y97RwrzrlHxP2fA57DKGAvYGEtn0PQPZKwBDyTK4Ef+Rw7zPk+ugLbO99NY9g3BPwJONFZvx34T2f9XOB2Z/1E4I9xPwvX/xwI7OWsbwK86vz3TllWYn8hMRSAFPCIK30pcGnc+arwf1xGvlBYAgx01gcCS5z1qcBJ3uOAk4Cpru1TnW0DgVdc23OOS9oCDPZUgFV/DkH3SMri80yuxF8o5Hwb6FgnqaBvyKnwPgCanO0bjzPnOutNznES97MIeD73Al/rrGWlM6qPBgFvudIrnG0dCQXMEpHnRGS8s62/Umqls74K6O+sBz2PsO0rfLbXC7V4DkH3SDLnOaqQO1wqjGKfSV9gtVJqg2d7zrWc/Z84xycKR621JzCPTlpWOqNQ6AwcqJTaCzgC+J6IjHLvVLpZ0ultkWvxHOrkWd8G7AiMAFYC18ebnXgQkV7A3cBEpdSn7n2dqax0RqHwNrCNK721s63DoJR62/l9D/gbMBJ4V0QGAji/7zmHBz2PsO1b+2yvF2rxHILukUiUUu8qpdqUUu3AL9HlBYp/Jh8CfUSkybM951rO/k2d4xOBiDSjBcJMpdRfnc2dsqx0RqHwDDDEsZLogh70ui/mPFUMEekpIpuYdWA0sBD9H401xKlovSnO9nGORcV+wCdOd/YRYLSIbOaoE0aj9cMrgU9FZD/HgmKc61r1QC2eQ9A9EomplByORZcX0P/jRMdyaHtgCHrA1Pcbclq6c4DjnfO9z9c8k+OBfzrHx47z/qYDLyulfu7a1TnLStyDGnEsaOuBV9EWFJfFnZ8K/7cd0NYgLwKLzP9D629nA68BjwKbO9sF+IXzLF4C9nFd6wxgqbOc7tq+D7ri+DdwC8kdMPw9Wh2yHq3HPbMWzyHoHklYAp7Jb53/vABdSQ10HX+Z8/+W4LIyC/qGnPL3tPOs/gx0dbZ3c9JLnf07xP0sXHk+EK22WQDMd5YjO2tZsW4uLP/f3v27RhHEYRh/XggarSxstBYLEWvBJsEfjWJlc13+AgnWaewsrEIqC7EQFG0kiAgWqTQWoqBgqYiSxlIsBOFrsZPlhNxJ7o4g5PlUOzc3x3Qvs8e+K0m9/Xj7SJI0gqEgSeoZCpKknqEgSeoZCpKknqEg7SBJJbk/NJ5L8j3J0za+mikadpMsJzk8i71Ks2QoSDv7CZxOcqiNLzL05HZVrVfVrSl+fxkwFPTfMRSk0Z4Bl9v1gO7BLwCSLCVZa9f3Wkf+qySfklxrny9snyzaeK2tuw4cBzaSbLS5S0k2k7xN8rj18Eh7zlCQRntIV/MwD5yha84c5Rjdk7FXgLEniKpaBbaAxapaTHIUWAEuVFdk+Aa4MYP9S7s29++vSPtTVb1vVcoDulPDOE+qK5T7mGS39cdn6V7q8rK9kOsAsLnL35BmwlCQxlsHbgMLjO///zV0vf160t/8fRqfH7E2wIuqGky4R2lmvH0kjXcXuFlVHyZY+wU41VpGjwDnh+Z+0L36EeA1cC7JCeibbk9Os2lpUp4UpDGq6huwOuHar0ke0bVjfgbeDU3fAZ4n2Wr/KywBD5IcbPMrdC2k0p6yJVWS1PP2kSSpZyhIknqGgiSpZyhIknqGgiSpZyhIknqGgiSp9we9mbCk9Ag4VAAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 296 | |
| }, | |
| "id": "J_jkPwBkHuT-", | |
| "outputId": "52986565-a1ac-4fe8-de7a-43cf45b32e50" | |
| }, | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "sensor_values = df['sensor_25']\n", | |
| "n = 100\n", | |
| "sensor_values = sensor_values[:n]\n", | |
| "\n", | |
| "min_val = min(sensor_values)\n", | |
| "max_val = max(sensor_values)\n", | |
| "\n", | |
| "plt.plot( sensor_values, color=\"blue\", linestyle=\"-\",\n", | |
| " marker=\".\", linewidth=0.5 )\n", | |
| "plt.hlines(min_val, 0, n, color=\"green\")\n", | |
| "plt.hlines(max_val, 0, n, color=\"red\")\n", | |
| "plt.xlabel(\"Minute\", fontsize=10)\n", | |
| "plt.ylabel(\"Sensor value\", fontsize=10)\n", | |
| "plt.title(\"The first \"+str(n)+\" sensor values of Sensor 25\", fontsize=14)\n", | |
| "plt.grid()\n", | |
| "#plt.savefig(\"sensor_25.png\")\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5Zwfa0nRz_Y3" | |
| }, | |
| "source": [ | |
| "#### *Machine status*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "rDXdlSZxr5SC", | |
| "outputId": "21371378-fa65-48f9-ab9b-28f35c23f63a" | |
| }, | |
| "source": [ | |
| "machine_status = df['machine_status'].unique()\n", | |
| "machine_status" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array(['NORMAL', 'BROKEN', 'RECOVERING'], dtype=object)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 411 | |
| }, | |
| "id": "XODi_LMlseKj", | |
| "outputId": "31a71795-b193-410d-96d4-0059d2ab3bf0" | |
| }, | |
| "source": [ | |
| "df_status_NORMAL = df[ df['machine_status']==\"NORMAL\" ]\n", | |
| "print(\"Machine was having 'NORMAL' status {} times.\".format(len(df_status_NORMAL)))\n", | |
| "df_status_NORMAL.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Machine was having 'NORMAL' status 205836 times.\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "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>timestamp</th>\n", | |
| " <th>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " <th>machine_status</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2018-04-01 00:00:00</td>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.3750</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.0556</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2018-04-01 00:01:00</td>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.3750</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.0556</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2018-04-01 00:02:00</td>\n", | |
| " <td>2.444734</td>\n", | |
| " <td>47.35243</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.397570</td>\n", | |
| " <td>638.8889</td>\n", | |
| " <td>73.54598</td>\n", | |
| " <td>13.32465</td>\n", | |
| " <td>16.03733</td>\n", | |
| " <td>15.61777</td>\n", | |
| " <td>15.01013</td>\n", | |
| " <td>37.86777</td>\n", | |
| " <td>48.17723</td>\n", | |
| " <td>32.08894</td>\n", | |
| " <td>1.708474</td>\n", | |
| " <td>420.8480</td>\n", | |
| " <td>462.7798</td>\n", | |
| " <td>459.6364</td>\n", | |
| " <td>2.500062</td>\n", | |
| " <td>666.2234</td>\n", | |
| " <td>399.9418</td>\n", | |
| " <td>880.4237</td>\n", | |
| " <td>501.3617</td>\n", | |
| " <td>982.7342</td>\n", | |
| " <td>631.1326</td>\n", | |
| " <td>740.8031</td>\n", | |
| " <td>849.8997</td>\n", | |
| " <td>454.2390</td>\n", | |
| " <td>778.5734</td>\n", | |
| " <td>715.6266</td>\n", | |
| " <td>661.5740</td>\n", | |
| " <td>721.8750</td>\n", | |
| " <td>694.7721</td>\n", | |
| " <td>441.2635</td>\n", | |
| " <td>169.9820</td>\n", | |
| " <td>343.1955</td>\n", | |
| " <td>200.9694</td>\n", | |
| " <td>93.90508</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>31.25000</td>\n", | |
| " <td>69.53125</td>\n", | |
| " <td>30.46875</td>\n", | |
| " <td>31.770830</td>\n", | |
| " <td>41.66666</td>\n", | |
| " <td>39.351852</td>\n", | |
| " <td>65.39352</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194443</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>67.12963</td>\n", | |
| " <td>241.3194</td>\n", | |
| " <td>203.7037</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2018-04-01 00:03:00</td>\n", | |
| " <td>2.460474</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.1684</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>628.1250</td>\n", | |
| " <td>76.98898</td>\n", | |
| " <td>13.31742</td>\n", | |
| " <td>16.24711</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>38.57977</td>\n", | |
| " <td>48.65607</td>\n", | |
| " <td>31.67221</td>\n", | |
| " <td>1.579427</td>\n", | |
| " <td>420.7494</td>\n", | |
| " <td>462.8980</td>\n", | |
| " <td>460.8858</td>\n", | |
| " <td>2.509521</td>\n", | |
| " <td>666.0114</td>\n", | |
| " <td>399.1046</td>\n", | |
| " <td>878.8917</td>\n", | |
| " <td>499.0430</td>\n", | |
| " <td>977.7520</td>\n", | |
| " <td>625.4076</td>\n", | |
| " <td>739.2722</td>\n", | |
| " <td>847.7579</td>\n", | |
| " <td>474.8731</td>\n", | |
| " <td>779.5091</td>\n", | |
| " <td>690.4011</td>\n", | |
| " <td>686.1111</td>\n", | |
| " <td>754.6875</td>\n", | |
| " <td>683.3831</td>\n", | |
| " <td>446.2493</td>\n", | |
| " <td>166.4987</td>\n", | |
| " <td>343.9586</td>\n", | |
| " <td>193.1689</td>\n", | |
| " <td>101.04060</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>72.13541</td>\n", | |
| " <td>30.46875</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>40.88541</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>64.81481</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>66.84028</td>\n", | |
| " <td>240.4514</td>\n", | |
| " <td>203.1250</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2018-04-01 00:04:00</td>\n", | |
| " <td>2.445718</td>\n", | |
| " <td>47.13541</td>\n", | |
| " <td>53.2118</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>636.4583</td>\n", | |
| " <td>76.58897</td>\n", | |
| " <td>13.35359</td>\n", | |
| " <td>16.21094</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>39.48939</td>\n", | |
| " <td>49.06298</td>\n", | |
| " <td>31.95202</td>\n", | |
| " <td>1.683831</td>\n", | |
| " <td>419.8926</td>\n", | |
| " <td>461.4906</td>\n", | |
| " <td>468.2206</td>\n", | |
| " <td>2.604785</td>\n", | |
| " <td>663.2111</td>\n", | |
| " <td>400.5426</td>\n", | |
| " <td>882.5874</td>\n", | |
| " <td>498.5383</td>\n", | |
| " <td>979.5755</td>\n", | |
| " <td>627.1830</td>\n", | |
| " <td>737.6033</td>\n", | |
| " <td>846.9182</td>\n", | |
| " <td>408.8159</td>\n", | |
| " <td>785.2307</td>\n", | |
| " <td>704.6937</td>\n", | |
| " <td>631.4814</td>\n", | |
| " <td>766.1458</td>\n", | |
| " <td>702.4431</td>\n", | |
| " <td>433.9081</td>\n", | |
| " <td>164.7498</td>\n", | |
| " <td>339.9630</td>\n", | |
| " <td>193.8770</td>\n", | |
| " <td>101.70380</td>\n", | |
| " <td>42.70833</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>76.82291</td>\n", | |
| " <td>30.98958</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>65.10416</td>\n", | |
| " <td>51.79398</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>158.2755</td>\n", | |
| " <td>66.55093</td>\n", | |
| " <td>242.1875</td>\n", | |
| " <td>201.3889</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " timestamp sensor_00 ... sensor_51 machine_status\n", | |
| "0 2018-04-01 00:00:00 2.465394 ... 201.3889 NORMAL\n", | |
| "1 2018-04-01 00:01:00 2.465394 ... 201.3889 NORMAL\n", | |
| "2 2018-04-01 00:02:00 2.444734 ... 203.7037 NORMAL\n", | |
| "3 2018-04-01 00:03:00 2.460474 ... 203.1250 NORMAL\n", | |
| "4 2018-04-01 00:04:00 2.445718 ... 201.3889 NORMAL\n", | |
| "\n", | |
| "[5 rows x 53 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 411 | |
| }, | |
| "id": "zp_lLKpVtmGm", | |
| "outputId": "514505b8-6cd5-4fe7-aa5b-64a033959101" | |
| }, | |
| "source": [ | |
| "df_status_BROKEN = df[ df['machine_status']==\"BROKEN\" ]\n", | |
| "print(\"Machine was having 'BROKEN' status {} times.\".format(len(df_status_BROKEN)))\n", | |
| "df_status_BROKEN.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Machine was having 'BROKEN' status 7 times.\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "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>timestamp</th>\n", | |
| " <th>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " <th>machine_status</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>17155</th>\n", | |
| " <td>2018-04-12 21:55:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>53.34201</td>\n", | |
| " <td>52.821180</td>\n", | |
| " <td>43.402775</td>\n", | |
| " <td>202.526031</td>\n", | |
| " <td>49.79289</td>\n", | |
| " <td>3.219039</td>\n", | |
| " <td>16.89091</td>\n", | |
| " <td>16.86921</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>35.530850</td>\n", | |
| " <td>3.625588</td>\n", | |
| " <td>1.602259</td>\n", | |
| " <td>0.237091</td>\n", | |
| " <td>407.4979</td>\n", | |
| " <td>451.3286</td>\n", | |
| " <td>449.1867</td>\n", | |
| " <td>2.387357</td>\n", | |
| " <td>652.2382</td>\n", | |
| " <td>390.5987</td>\n", | |
| " <td>858.3630</td>\n", | |
| " <td>461.2751</td>\n", | |
| " <td>955.74270</td>\n", | |
| " <td>614.6786</td>\n", | |
| " <td>653.9383</td>\n", | |
| " <td>753.0676</td>\n", | |
| " <td>570.81680</td>\n", | |
| " <td>1161.1310</td>\n", | |
| " <td>783.6125</td>\n", | |
| " <td>710.6481</td>\n", | |
| " <td>960.9374</td>\n", | |
| " <td>742.2943</td>\n", | |
| " <td>566.6204</td>\n", | |
| " <td>261.7709</td>\n", | |
| " <td>399.129100</td>\n", | |
| " <td>301.1411</td>\n", | |
| " <td>114.20790</td>\n", | |
| " <td>52.08333</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>87.23958</td>\n", | |
| " <td>39.583330</td>\n", | |
| " <td>36.979160</td>\n", | |
| " <td>50.78125</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>51.215280</td>\n", | |
| " <td>50.636570</td>\n", | |
| " <td>46.006940</td>\n", | |
| " <td>409.143500</td>\n", | |
| " <td>121.527800</td>\n", | |
| " <td>401.909700</td>\n", | |
| " <td>324.652800</td>\n", | |
| " <td>BROKEN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>24510</th>\n", | |
| " <td>2018-04-18 00:30:00</td>\n", | |
| " <td>1.093982</td>\n", | |
| " <td>42.53472</td>\n", | |
| " <td>47.699650</td>\n", | |
| " <td>41.449650</td>\n", | |
| " <td>206.038757</td>\n", | |
| " <td>60.30106</td>\n", | |
| " <td>12.304690</td>\n", | |
| " <td>15.15480</td>\n", | |
| " <td>14.18547</td>\n", | |
| " <td>13.86719</td>\n", | |
| " <td>28.304880</td>\n", | |
| " <td>30.434710</td>\n", | |
| " <td>21.437910</td>\n", | |
| " <td>6.803444</td>\n", | |
| " <td>420.0946</td>\n", | |
| " <td>460.7847</td>\n", | |
| " <td>457.7386</td>\n", | |
| " <td>2.481055</td>\n", | |
| " <td>664.2351</td>\n", | |
| " <td>398.1912</td>\n", | |
| " <td>879.1428</td>\n", | |
| " <td>458.3324</td>\n", | |
| " <td>944.30770</td>\n", | |
| " <td>625.1230</td>\n", | |
| " <td>650.4600</td>\n", | |
| " <td>748.0622</td>\n", | |
| " <td>502.59550</td>\n", | |
| " <td>1063.0030</td>\n", | |
| " <td>784.2626</td>\n", | |
| " <td>671.2963</td>\n", | |
| " <td>1016.6670</td>\n", | |
| " <td>828.1685</td>\n", | |
| " <td>578.9925</td>\n", | |
| " <td>177.4708</td>\n", | |
| " <td>411.165200</td>\n", | |
| " <td>434.2556</td>\n", | |
| " <td>73.31284</td>\n", | |
| " <td>37.76041</td>\n", | |
| " <td>32.81250</td>\n", | |
| " <td>71.61458</td>\n", | |
| " <td>28.645830</td>\n", | |
| " <td>29.947916</td>\n", | |
| " <td>42.70833</td>\n", | |
| " <td>34.72222</td>\n", | |
| " <td>31.539350</td>\n", | |
| " <td>34.432870</td>\n", | |
| " <td>33.275460</td>\n", | |
| " <td>59.895830</td>\n", | |
| " <td>44.560180</td>\n", | |
| " <td>177.662000</td>\n", | |
| " <td>183.738400</td>\n", | |
| " <td>BROKEN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>69318</th>\n", | |
| " <td>2018-05-19 03:18:00</td>\n", | |
| " <td>2.258796</td>\n", | |
| " <td>47.26563</td>\n", | |
| " <td>52.734370</td>\n", | |
| " <td>43.446178</td>\n", | |
| " <td>200.115738</td>\n", | |
| " <td>66.14643</td>\n", | |
| " <td>13.592300</td>\n", | |
| " <td>15.91435</td>\n", | |
| " <td>15.14757</td>\n", | |
| " <td>14.79311</td>\n", | |
| " <td>43.998860</td>\n", | |
| " <td>43.623220</td>\n", | |
| " <td>22.736040</td>\n", | |
| " <td>9.277993</td>\n", | |
| " <td>420.3359</td>\n", | |
| " <td>463.0843</td>\n", | |
| " <td>462.6495</td>\n", | |
| " <td>2.539193</td>\n", | |
| " <td>665.5446</td>\n", | |
| " <td>399.1660</td>\n", | |
| " <td>880.9252</td>\n", | |
| " <td>498.6224</td>\n", | |
| " <td>997.63640</td>\n", | |
| " <td>619.0558</td>\n", | |
| " <td>719.8438</td>\n", | |
| " <td>867.5176</td>\n", | |
| " <td>551.63670</td>\n", | |
| " <td>1154.4050</td>\n", | |
| " <td>761.1199</td>\n", | |
| " <td>655.5555</td>\n", | |
| " <td>1024.4790</td>\n", | |
| " <td>849.8132</td>\n", | |
| " <td>608.9143</td>\n", | |
| " <td>171.3203</td>\n", | |
| " <td>350.311700</td>\n", | |
| " <td>285.3491</td>\n", | |
| " <td>75.20248</td>\n", | |
| " <td>47.39583</td>\n", | |
| " <td>29.16667</td>\n", | |
| " <td>66.66666</td>\n", | |
| " <td>32.291660</td>\n", | |
| " <td>31.250000</td>\n", | |
| " <td>39.06250</td>\n", | |
| " <td>35.01157</td>\n", | |
| " <td>37.905090</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>45.428240</td>\n", | |
| " <td>144.675900</td>\n", | |
| " <td>49.768520</td>\n", | |
| " <td>246.238400</td>\n", | |
| " <td>257.523100</td>\n", | |
| " <td>BROKEN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>77790</th>\n", | |
| " <td>2018-05-25 00:30:00</td>\n", | |
| " <td>2.321759</td>\n", | |
| " <td>47.48264</td>\n", | |
| " <td>51.475693</td>\n", | |
| " <td>42.795135</td>\n", | |
| " <td>612.152800</td>\n", | |
| " <td>67.30158</td>\n", | |
| " <td>14.062500</td>\n", | |
| " <td>16.60880</td>\n", | |
| " <td>15.94329</td>\n", | |
| " <td>15.59606</td>\n", | |
| " <td>27.092980</td>\n", | |
| " <td>44.793620</td>\n", | |
| " <td>32.745590</td>\n", | |
| " <td>2.178048</td>\n", | |
| " <td>420.7917</td>\n", | |
| " <td>463.3876</td>\n", | |
| " <td>464.5768</td>\n", | |
| " <td>2.557975</td>\n", | |
| " <td>665.4158</td>\n", | |
| " <td>399.1982</td>\n", | |
| " <td>885.1603</td>\n", | |
| " <td>533.6134</td>\n", | |
| " <td>982.50690</td>\n", | |
| " <td>627.0386</td>\n", | |
| " <td>746.0360</td>\n", | |
| " <td>854.0722</td>\n", | |
| " <td>478.34040</td>\n", | |
| " <td>1095.9630</td>\n", | |
| " <td>724.8124</td>\n", | |
| " <td>698.6111</td>\n", | |
| " <td>971.8749</td>\n", | |
| " <td>882.1304</td>\n", | |
| " <td>499.8158</td>\n", | |
| " <td>171.7490</td>\n", | |
| " <td>354.075700</td>\n", | |
| " <td>250.9113</td>\n", | |
| " <td>72.70645</td>\n", | |
| " <td>381.77080</td>\n", | |
| " <td>417.18750</td>\n", | |
| " <td>427.34370</td>\n", | |
| " <td>212.760400</td>\n", | |
| " <td>176.822900</td>\n", | |
| " <td>202.34370</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>57.870370</td>\n", | |
| " <td>127.893500</td>\n", | |
| " <td>153.935200</td>\n", | |
| " <td>155.381900</td>\n", | |
| " <td>65.682870</td>\n", | |
| " <td>220.196800</td>\n", | |
| " <td>267.361100</td>\n", | |
| " <td>BROKEN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>128040</th>\n", | |
| " <td>2018-06-28 22:00:00</td>\n", | |
| " <td>0.364005</td>\n", | |
| " <td>40.19097</td>\n", | |
| " <td>45.225690</td>\n", | |
| " <td>40.190971</td>\n", | |
| " <td>201.368622</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>11.335360</td>\n", | |
| " <td>15.27054</td>\n", | |
| " <td>15.18374</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>2.002474</td>\n", | |
| " <td>1.960537</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>197.9393</td>\n", | |
| " <td>206.4634</td>\n", | |
| " <td>193.7957</td>\n", | |
| " <td>0.444666</td>\n", | |
| " <td>108.9490</td>\n", | |
| " <td>125.4787</td>\n", | |
| " <td>158.0601</td>\n", | |
| " <td>128.2272</td>\n", | |
| " <td>96.38937</td>\n", | |
| " <td>103.7937</td>\n", | |
| " <td>143.1029</td>\n", | |
| " <td>156.3189</td>\n", | |
| " <td>21.31752</td>\n", | |
| " <td>258.0632</td>\n", | |
| " <td>109.4662</td>\n", | |
| " <td>297.2222</td>\n", | |
| " <td>575.0000</td>\n", | |
| " <td>436.2560</td>\n", | |
| " <td>258.6194</td>\n", | |
| " <td>343.9342</td>\n", | |
| " <td>694.479126</td>\n", | |
| " <td>367.8615</td>\n", | |
| " <td>23.78439</td>\n", | |
| " <td>28.38542</td>\n", | |
| " <td>22.13542</td>\n", | |
| " <td>36.71875</td>\n", | |
| " <td>23.177082</td>\n", | |
| " <td>24.739580</td>\n", | |
| " <td>32.29166</td>\n", | |
| " <td>28.06713</td>\n", | |
| " <td>28.067129</td>\n", | |
| " <td>29.513889</td>\n", | |
| " <td>29.224537</td>\n", | |
| " <td>29.224537</td>\n", | |
| " <td>29.513889</td>\n", | |
| " <td>32.407406</td>\n", | |
| " <td>202.699667</td>\n", | |
| " <td>BROKEN</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " timestamp sensor_00 ... sensor_51 machine_status\n", | |
| "17155 2018-04-12 21:55:00 0.000000 ... 324.652800 BROKEN\n", | |
| "24510 2018-04-18 00:30:00 1.093982 ... 183.738400 BROKEN\n", | |
| "69318 2018-05-19 03:18:00 2.258796 ... 257.523100 BROKEN\n", | |
| "77790 2018-05-25 00:30:00 2.321759 ... 267.361100 BROKEN\n", | |
| "128040 2018-06-28 22:00:00 0.364005 ... 202.699667 BROKEN\n", | |
| "\n", | |
| "[5 rows x 53 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 411 | |
| }, | |
| "id": "8jzm66vktwdk", | |
| "outputId": "54456ae6-ed36-4ff8-c920-ae011e6df733" | |
| }, | |
| "source": [ | |
| "df_status_RECOVERING = df[ df['machine_status']==\"RECOVERING\" ]\n", | |
| "print(\"Machine was having 'RECOVERING' status {} times.\".format(len(df_status_RECOVERING)))\n", | |
| "df_status_RECOVERING.head()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Machine was having 'RECOVERING' status 14477 times.\n" | |
| ], | |
| "name": "stdout" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "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>timestamp</th>\n", | |
| " <th>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " <th>machine_status</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>17156</th>\n", | |
| " <td>2018-04-12 21:56:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>53.55902</td>\n", | |
| " <td>52.77777</td>\n", | |
| " <td>43.402775</td>\n", | |
| " <td>204.725098</td>\n", | |
| " <td>53.74214</td>\n", | |
| " <td>3.045428</td>\n", | |
| " <td>17.42621</td>\n", | |
| " <td>15.740740</td>\n", | |
| " <td>16.17477</td>\n", | |
| " <td>40.31071</td>\n", | |
| " <td>3.730241</td>\n", | |
| " <td>1.612210</td>\n", | |
| " <td>0.318798</td>\n", | |
| " <td>404.9974</td>\n", | |
| " <td>450.2004</td>\n", | |
| " <td>454.0266</td>\n", | |
| " <td>2.448104</td>\n", | |
| " <td>651.1287</td>\n", | |
| " <td>390.6628</td>\n", | |
| " <td>857.0173</td>\n", | |
| " <td>461.6187</td>\n", | |
| " <td>951.3060</td>\n", | |
| " <td>612.6481</td>\n", | |
| " <td>652.3843</td>\n", | |
| " <td>750.5814</td>\n", | |
| " <td>550.9968</td>\n", | |
| " <td>1118.163</td>\n", | |
| " <td>773.6210</td>\n", | |
| " <td>704.6296</td>\n", | |
| " <td>1003.1250</td>\n", | |
| " <td>714.9668</td>\n", | |
| " <td>548.0704</td>\n", | |
| " <td>270.0769</td>\n", | |
| " <td>403.3277</td>\n", | |
| " <td>299.2007</td>\n", | |
| " <td>110.5263</td>\n", | |
| " <td>53.64583</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>84.63541</td>\n", | |
| " <td>40.62500</td>\n", | |
| " <td>36.979164</td>\n", | |
| " <td>49.47916</td>\n", | |
| " <td>50.34722</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>49.18982</td>\n", | |
| " <td>49.47917</td>\n", | |
| " <td>431.7130</td>\n", | |
| " <td>133.1019</td>\n", | |
| " <td>419.2708</td>\n", | |
| " <td>341.7245</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17157</th>\n", | |
| " <td>2018-04-12 21:57:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>53.55902</td>\n", | |
| " <td>52.77777</td>\n", | |
| " <td>43.402775</td>\n", | |
| " <td>201.137131</td>\n", | |
| " <td>52.49996</td>\n", | |
| " <td>7.537616</td>\n", | |
| " <td>13.53443</td>\n", | |
| " <td>9.324364</td>\n", | |
| " <td>16.05179</td>\n", | |
| " <td>38.93098</td>\n", | |
| " <td>3.816472</td>\n", | |
| " <td>1.631223</td>\n", | |
| " <td>0.342867</td>\n", | |
| " <td>409.9810</td>\n", | |
| " <td>447.9742</td>\n", | |
| " <td>449.0287</td>\n", | |
| " <td>2.410462</td>\n", | |
| " <td>650.5953</td>\n", | |
| " <td>390.4939</td>\n", | |
| " <td>863.1273</td>\n", | |
| " <td>458.5190</td>\n", | |
| " <td>948.8156</td>\n", | |
| " <td>615.3629</td>\n", | |
| " <td>646.3538</td>\n", | |
| " <td>749.3739</td>\n", | |
| " <td>553.7806</td>\n", | |
| " <td>1147.219</td>\n", | |
| " <td>783.5280</td>\n", | |
| " <td>716.2037</td>\n", | |
| " <td>1040.1040</td>\n", | |
| " <td>740.6817</td>\n", | |
| " <td>561.4838</td>\n", | |
| " <td>265.9168</td>\n", | |
| " <td>415.0607</td>\n", | |
| " <td>305.2495</td>\n", | |
| " <td>115.3448</td>\n", | |
| " <td>55.20833</td>\n", | |
| " <td>35.15625</td>\n", | |
| " <td>81.25000</td>\n", | |
| " <td>40.88541</td>\n", | |
| " <td>36.979160</td>\n", | |
| " <td>48.17708</td>\n", | |
| " <td>49.76852</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>53.81944</td>\n", | |
| " <td>451.3889</td>\n", | |
| " <td>140.0463</td>\n", | |
| " <td>433.1597</td>\n", | |
| " <td>466.1458</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17158</th>\n", | |
| " <td>2018-04-12 21:58:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>52.77777</td>\n", | |
| " <td>52.69097</td>\n", | |
| " <td>43.402770</td>\n", | |
| " <td>204.030655</td>\n", | |
| " <td>57.19875</td>\n", | |
| " <td>7.609953</td>\n", | |
| " <td>16.60880</td>\n", | |
| " <td>16.203700</td>\n", | |
| " <td>16.09520</td>\n", | |
| " <td>33.43375</td>\n", | |
| " <td>3.860711</td>\n", | |
| " <td>1.622106</td>\n", | |
| " <td>0.304665</td>\n", | |
| " <td>412.2902</td>\n", | |
| " <td>449.5466</td>\n", | |
| " <td>453.7787</td>\n", | |
| " <td>2.429593</td>\n", | |
| " <td>652.1424</td>\n", | |
| " <td>390.2556</td>\n", | |
| " <td>857.9081</td>\n", | |
| " <td>461.1630</td>\n", | |
| " <td>950.7607</td>\n", | |
| " <td>614.9879</td>\n", | |
| " <td>649.2152</td>\n", | |
| " <td>746.4882</td>\n", | |
| " <td>541.1052</td>\n", | |
| " <td>1131.809</td>\n", | |
| " <td>787.4129</td>\n", | |
| " <td>678.2407</td>\n", | |
| " <td>976.5624</td>\n", | |
| " <td>719.6953</td>\n", | |
| " <td>558.5663</td>\n", | |
| " <td>266.8813</td>\n", | |
| " <td>415.8537</td>\n", | |
| " <td>306.2030</td>\n", | |
| " <td>105.0438</td>\n", | |
| " <td>57.03125</td>\n", | |
| " <td>35.15625</td>\n", | |
| " <td>79.16666</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>36.718750</td>\n", | |
| " <td>46.87500</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>50.34722</td>\n", | |
| " <td>47.45370</td>\n", | |
| " <td>58.15972</td>\n", | |
| " <td>466.4352</td>\n", | |
| " <td>144.9653</td>\n", | |
| " <td>442.7083</td>\n", | |
| " <td>366.0301</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17159</th>\n", | |
| " <td>2018-04-12 21:59:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>52.60416</td>\n", | |
| " <td>52.73437</td>\n", | |
| " <td>43.446180</td>\n", | |
| " <td>203.567688</td>\n", | |
| " <td>50.96181</td>\n", | |
| " <td>7.573785</td>\n", | |
| " <td>16.70284</td>\n", | |
| " <td>16.160300</td>\n", | |
| " <td>16.08796</td>\n", | |
| " <td>33.13226</td>\n", | |
| " <td>4.496508</td>\n", | |
| " <td>1.650150</td>\n", | |
| " <td>0.606178</td>\n", | |
| " <td>408.3951</td>\n", | |
| " <td>450.6098</td>\n", | |
| " <td>444.7706</td>\n", | |
| " <td>2.361692</td>\n", | |
| " <td>652.0457</td>\n", | |
| " <td>390.4180</td>\n", | |
| " <td>858.0203</td>\n", | |
| " <td>460.7399</td>\n", | |
| " <td>949.6810</td>\n", | |
| " <td>614.2903</td>\n", | |
| " <td>649.3373</td>\n", | |
| " <td>748.6567</td>\n", | |
| " <td>546.8779</td>\n", | |
| " <td>1148.804</td>\n", | |
| " <td>782.2913</td>\n", | |
| " <td>682.4074</td>\n", | |
| " <td>930.2083</td>\n", | |
| " <td>723.4662</td>\n", | |
| " <td>550.8478</td>\n", | |
| " <td>263.7073</td>\n", | |
| " <td>403.5153</td>\n", | |
| " <td>303.2734</td>\n", | |
| " <td>113.0340</td>\n", | |
| " <td>57.55208</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>75.26041</td>\n", | |
| " <td>42.18750</td>\n", | |
| " <td>36.718750</td>\n", | |
| " <td>45.83333</td>\n", | |
| " <td>47.45370</td>\n", | |
| " <td>49.76852</td>\n", | |
| " <td>46.58565</td>\n", | |
| " <td>63.65741</td>\n", | |
| " <td>474.8264</td>\n", | |
| " <td>149.0162</td>\n", | |
| " <td>449.6528</td>\n", | |
| " <td>374.4213</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17160</th>\n", | |
| " <td>2018-04-12 22:00:00</td>\n", | |
| " <td>2.372221</td>\n", | |
| " <td>52.51736</td>\n", | |
| " <td>52.69097</td>\n", | |
| " <td>43.446180</td>\n", | |
| " <td>203.567688</td>\n", | |
| " <td>49.78948</td>\n", | |
| " <td>7.559317</td>\n", | |
| " <td>16.56539</td>\n", | |
| " <td>16.239870</td>\n", | |
| " <td>16.05179</td>\n", | |
| " <td>35.34599</td>\n", | |
| " <td>12.028980</td>\n", | |
| " <td>1.904167</td>\n", | |
| " <td>0.589342</td>\n", | |
| " <td>411.0616</td>\n", | |
| " <td>449.2088</td>\n", | |
| " <td>441.3550</td>\n", | |
| " <td>2.311379</td>\n", | |
| " <td>650.0242</td>\n", | |
| " <td>391.6447</td>\n", | |
| " <td>864.0715</td>\n", | |
| " <td>459.4494</td>\n", | |
| " <td>950.6841</td>\n", | |
| " <td>614.7383</td>\n", | |
| " <td>649.4260</td>\n", | |
| " <td>750.3677</td>\n", | |
| " <td>550.1708</td>\n", | |
| " <td>1145.063</td>\n", | |
| " <td>780.6435</td>\n", | |
| " <td>707.8704</td>\n", | |
| " <td>949.9999</td>\n", | |
| " <td>720.6281</td>\n", | |
| " <td>542.5764</td>\n", | |
| " <td>270.2823</td>\n", | |
| " <td>410.3059</td>\n", | |
| " <td>297.6039</td>\n", | |
| " <td>119.8698</td>\n", | |
| " <td>55.98958</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>64.84375</td>\n", | |
| " <td>44.53125</td>\n", | |
| " <td>36.718750</td>\n", | |
| " <td>44.27083</td>\n", | |
| " <td>46.29630</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>45.13889</td>\n", | |
| " <td>75.23148</td>\n", | |
| " <td>477.7199</td>\n", | |
| " <td>162.6157</td>\n", | |
| " <td>448.7847</td>\n", | |
| " <td>356.1921</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " timestamp sensor_00 ... sensor_51 machine_status\n", | |
| "17156 2018-04-12 21:56:00 0.000000 ... 341.7245 RECOVERING\n", | |
| "17157 2018-04-12 21:57:00 0.000000 ... 466.1458 RECOVERING\n", | |
| "17158 2018-04-12 21:58:00 0.000000 ... 366.0301 RECOVERING\n", | |
| "17159 2018-04-12 21:59:00 0.000000 ... 374.4213 RECOVERING\n", | |
| "17160 2018-04-12 22:00:00 2.372221 ... 356.1921 RECOVERING\n", | |
| "\n", | |
| "[5 rows x 53 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "id": "weI5SMyLt4mM", | |
| "outputId": "7ecc4a14-1f93-4a5e-d907-9f028e377d09" | |
| }, | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "xpos = [1,2,3]\n", | |
| "data = [len(df_status_NORMAL),\n", | |
| " len(df_status_BROKEN),\n", | |
| " len(df_status_RECOVERING)]\n", | |
| "\n", | |
| "fig, ax = plt.subplots()\n", | |
| "ax.bar(xpos,data)\n", | |
| "ax.set_xticks(xpos)\n", | |
| "ax.set_xticklabels(machine_status)\n", | |
| "ax.set_title(\"How often was the machine in each machine state?\")\n", | |
| "plt.show()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [], | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "dX-sYV030qm8" | |
| }, | |
| "source": [ | |
| "#### *When did the stutus got updated? BROKEN > RECOVERING > NORMAL*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Umkg6tPRy246", | |
| "outputId": "1ee650d6-5c03-4637-ebc4-94c850ead4f1" | |
| }, | |
| "source": [ | |
| "df_status_BROKEN.index.values" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([ 17155, 24510, 69318, 77790, 128040, 141131, 166440])" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 779 | |
| }, | |
| "id": "EoM4M01wz8ko", | |
| "outputId": "b30c798b-0276-4973-e8c4-0034be0d1c70" | |
| }, | |
| "source": [ | |
| "df[17155:]" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>timestamp</th>\n", | |
| " <th>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " <th>machine_status</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>17155</th>\n", | |
| " <td>2018-04-12 21:55:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>53.34201</td>\n", | |
| " <td>52.821180</td>\n", | |
| " <td>43.402775</td>\n", | |
| " <td>202.526031</td>\n", | |
| " <td>49.79289</td>\n", | |
| " <td>3.219039</td>\n", | |
| " <td>16.89091</td>\n", | |
| " <td>16.869210</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>35.53085</td>\n", | |
| " <td>3.625588</td>\n", | |
| " <td>1.602259</td>\n", | |
| " <td>0.237091</td>\n", | |
| " <td>407.4979</td>\n", | |
| " <td>451.3286</td>\n", | |
| " <td>449.1867</td>\n", | |
| " <td>2.387357</td>\n", | |
| " <td>652.2382</td>\n", | |
| " <td>390.5987</td>\n", | |
| " <td>858.3630</td>\n", | |
| " <td>461.2751</td>\n", | |
| " <td>955.7427</td>\n", | |
| " <td>614.6786</td>\n", | |
| " <td>653.9383</td>\n", | |
| " <td>753.0676</td>\n", | |
| " <td>570.8168</td>\n", | |
| " <td>1161.1310</td>\n", | |
| " <td>783.6125</td>\n", | |
| " <td>710.6481</td>\n", | |
| " <td>960.9374</td>\n", | |
| " <td>742.2943</td>\n", | |
| " <td>566.6204</td>\n", | |
| " <td>261.7709</td>\n", | |
| " <td>399.1291</td>\n", | |
| " <td>301.1411</td>\n", | |
| " <td>114.2079</td>\n", | |
| " <td>52.08333</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>87.23958</td>\n", | |
| " <td>39.583330</td>\n", | |
| " <td>36.979160</td>\n", | |
| " <td>50.78125</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>50.63657</td>\n", | |
| " <td>46.006940</td>\n", | |
| " <td>409.1435</td>\n", | |
| " <td>121.5278</td>\n", | |
| " <td>401.90970</td>\n", | |
| " <td>324.6528</td>\n", | |
| " <td>BROKEN</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17156</th>\n", | |
| " <td>2018-04-12 21:56:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>53.55902</td>\n", | |
| " <td>52.777770</td>\n", | |
| " <td>43.402775</td>\n", | |
| " <td>204.725098</td>\n", | |
| " <td>53.74214</td>\n", | |
| " <td>3.045428</td>\n", | |
| " <td>17.42621</td>\n", | |
| " <td>15.740740</td>\n", | |
| " <td>16.17477</td>\n", | |
| " <td>40.31071</td>\n", | |
| " <td>3.730241</td>\n", | |
| " <td>1.612210</td>\n", | |
| " <td>0.318798</td>\n", | |
| " <td>404.9974</td>\n", | |
| " <td>450.2004</td>\n", | |
| " <td>454.0266</td>\n", | |
| " <td>2.448104</td>\n", | |
| " <td>651.1287</td>\n", | |
| " <td>390.6628</td>\n", | |
| " <td>857.0173</td>\n", | |
| " <td>461.6187</td>\n", | |
| " <td>951.3060</td>\n", | |
| " <td>612.6481</td>\n", | |
| " <td>652.3843</td>\n", | |
| " <td>750.5814</td>\n", | |
| " <td>550.9968</td>\n", | |
| " <td>1118.1630</td>\n", | |
| " <td>773.6210</td>\n", | |
| " <td>704.6296</td>\n", | |
| " <td>1003.1250</td>\n", | |
| " <td>714.9668</td>\n", | |
| " <td>548.0704</td>\n", | |
| " <td>270.0769</td>\n", | |
| " <td>403.3277</td>\n", | |
| " <td>299.2007</td>\n", | |
| " <td>110.5263</td>\n", | |
| " <td>53.64583</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>84.63541</td>\n", | |
| " <td>40.625000</td>\n", | |
| " <td>36.979164</td>\n", | |
| " <td>49.47916</td>\n", | |
| " <td>50.34722</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>49.18982</td>\n", | |
| " <td>49.479170</td>\n", | |
| " <td>431.7130</td>\n", | |
| " <td>133.1019</td>\n", | |
| " <td>419.27080</td>\n", | |
| " <td>341.7245</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17157</th>\n", | |
| " <td>2018-04-12 21:57:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>53.55902</td>\n", | |
| " <td>52.777770</td>\n", | |
| " <td>43.402775</td>\n", | |
| " <td>201.137131</td>\n", | |
| " <td>52.49996</td>\n", | |
| " <td>7.537616</td>\n", | |
| " <td>13.53443</td>\n", | |
| " <td>9.324364</td>\n", | |
| " <td>16.05179</td>\n", | |
| " <td>38.93098</td>\n", | |
| " <td>3.816472</td>\n", | |
| " <td>1.631223</td>\n", | |
| " <td>0.342867</td>\n", | |
| " <td>409.9810</td>\n", | |
| " <td>447.9742</td>\n", | |
| " <td>449.0287</td>\n", | |
| " <td>2.410462</td>\n", | |
| " <td>650.5953</td>\n", | |
| " <td>390.4939</td>\n", | |
| " <td>863.1273</td>\n", | |
| " <td>458.5190</td>\n", | |
| " <td>948.8156</td>\n", | |
| " <td>615.3629</td>\n", | |
| " <td>646.3538</td>\n", | |
| " <td>749.3739</td>\n", | |
| " <td>553.7806</td>\n", | |
| " <td>1147.2190</td>\n", | |
| " <td>783.5280</td>\n", | |
| " <td>716.2037</td>\n", | |
| " <td>1040.1040</td>\n", | |
| " <td>740.6817</td>\n", | |
| " <td>561.4838</td>\n", | |
| " <td>265.9168</td>\n", | |
| " <td>415.0607</td>\n", | |
| " <td>305.2495</td>\n", | |
| " <td>115.3448</td>\n", | |
| " <td>55.20833</td>\n", | |
| " <td>35.15625</td>\n", | |
| " <td>81.25000</td>\n", | |
| " <td>40.885410</td>\n", | |
| " <td>36.979160</td>\n", | |
| " <td>48.17708</td>\n", | |
| " <td>49.76852</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>53.819440</td>\n", | |
| " <td>451.3889</td>\n", | |
| " <td>140.0463</td>\n", | |
| " <td>433.15970</td>\n", | |
| " <td>466.1458</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17158</th>\n", | |
| " <td>2018-04-12 21:58:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>52.77777</td>\n", | |
| " <td>52.690970</td>\n", | |
| " <td>43.402770</td>\n", | |
| " <td>204.030655</td>\n", | |
| " <td>57.19875</td>\n", | |
| " <td>7.609953</td>\n", | |
| " <td>16.60880</td>\n", | |
| " <td>16.203700</td>\n", | |
| " <td>16.09520</td>\n", | |
| " <td>33.43375</td>\n", | |
| " <td>3.860711</td>\n", | |
| " <td>1.622106</td>\n", | |
| " <td>0.304665</td>\n", | |
| " <td>412.2902</td>\n", | |
| " <td>449.5466</td>\n", | |
| " <td>453.7787</td>\n", | |
| " <td>2.429593</td>\n", | |
| " <td>652.1424</td>\n", | |
| " <td>390.2556</td>\n", | |
| " <td>857.9081</td>\n", | |
| " <td>461.1630</td>\n", | |
| " <td>950.7607</td>\n", | |
| " <td>614.9879</td>\n", | |
| " <td>649.2152</td>\n", | |
| " <td>746.4882</td>\n", | |
| " <td>541.1052</td>\n", | |
| " <td>1131.8090</td>\n", | |
| " <td>787.4129</td>\n", | |
| " <td>678.2407</td>\n", | |
| " <td>976.5624</td>\n", | |
| " <td>719.6953</td>\n", | |
| " <td>558.5663</td>\n", | |
| " <td>266.8813</td>\n", | |
| " <td>415.8537</td>\n", | |
| " <td>306.2030</td>\n", | |
| " <td>105.0438</td>\n", | |
| " <td>57.03125</td>\n", | |
| " <td>35.15625</td>\n", | |
| " <td>79.16666</td>\n", | |
| " <td>41.406250</td>\n", | |
| " <td>36.718750</td>\n", | |
| " <td>46.87500</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>50.34722</td>\n", | |
| " <td>47.45370</td>\n", | |
| " <td>58.159720</td>\n", | |
| " <td>466.4352</td>\n", | |
| " <td>144.9653</td>\n", | |
| " <td>442.70830</td>\n", | |
| " <td>366.0301</td>\n", | |
| " <td>RECOVERING</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17159</th>\n", | |
| " <td>2018-04-12 21:59:00</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>52.60416</td>\n", | |
| " <td>52.734370</td>\n", | |
| " <td>43.446180</td>\n", | |
| " <td>203.567688</td>\n", | |
| " <td>50.96181</td>\n", | |
| " <td>7.573785</td>\n", | |
| " <td>16.70284</td>\n", | |
| " <td>16.160300</td>\n", | |
| " <td>16.08796</td>\n", | |
| " <td>33.13226</td>\n", | |
| " <td>4.496508</td>\n", | |
| " <td>1.650150</td>\n", | |
| " <td>0.606178</td>\n", | |
| " <td>408.3951</td>\n", | |
| " <td>450.6098</td>\n", | |
| " <td>444.7706</td>\n", | |
| " <td>2.361692</td>\n", | |
| " <td>652.0457</td>\n", | |
| " <td>390.4180</td>\n", | |
| " <td>858.0203</td>\n", | |
| " <td>460.7399</td>\n", | |
| " <td>949.6810</td>\n", | |
| " <td>614.2903</td>\n", | |
| " <td>649.3373</td>\n", | |
| " <td>748.6567</td>\n", | |
| " <td>546.8779</td>\n", | |
| " <td>1148.8040</td>\n", | |
| " <td>782.2913</td>\n", | |
| " <td>682.4074</td>\n", | |
| " <td>930.2083</td>\n", | |
| " <td>723.4662</td>\n", | |
| " <td>550.8478</td>\n", | |
| " <td>263.7073</td>\n", | |
| " <td>403.5153</td>\n", | |
| " <td>303.2734</td>\n", | |
| " <td>113.0340</td>\n", | |
| " <td>57.55208</td>\n", | |
| " <td>35.41666</td>\n", | |
| " <td>75.26041</td>\n", | |
| " <td>42.187500</td>\n", | |
| " <td>36.718750</td>\n", | |
| " <td>45.83333</td>\n", | |
| " <td>47.45370</td>\n", | |
| " <td>49.76852</td>\n", | |
| " <td>46.58565</td>\n", | |
| " <td>63.657410</td>\n", | |
| " <td>474.8264</td>\n", | |
| " <td>149.0162</td>\n", | |
| " <td>449.65280</td>\n", | |
| " <td>374.4213</td>\n", | |
| " <td>RECOVERING</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", | |
| " <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", | |
| " <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>220315</th>\n", | |
| " <td>2018-08-31 23:55:00</td>\n", | |
| " <td>2.407350</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520830</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>634.722229</td>\n", | |
| " <td>64.59095</td>\n", | |
| " <td>15.118630</td>\n", | |
| " <td>16.65220</td>\n", | |
| " <td>15.653930</td>\n", | |
| " <td>15.16204</td>\n", | |
| " <td>43.17085</td>\n", | |
| " <td>54.160520</td>\n", | |
| " <td>38.054240</td>\n", | |
| " <td>13.265320</td>\n", | |
| " <td>420.7993</td>\n", | |
| " <td>463.2318</td>\n", | |
| " <td>458.3615</td>\n", | |
| " <td>2.499117</td>\n", | |
| " <td>676.6655</td>\n", | |
| " <td>405.7680</td>\n", | |
| " <td>894.5920</td>\n", | |
| " <td>543.5801</td>\n", | |
| " <td>1109.5010</td>\n", | |
| " <td>611.1745</td>\n", | |
| " <td>700.5885</td>\n", | |
| " <td>796.5964</td>\n", | |
| " <td>692.1138</td>\n", | |
| " <td>779.2067</td>\n", | |
| " <td>485.0358</td>\n", | |
| " <td>691.6666</td>\n", | |
| " <td>974.9999</td>\n", | |
| " <td>927.6135</td>\n", | |
| " <td>477.3156</td>\n", | |
| " <td>266.0334</td>\n", | |
| " <td>578.5221</td>\n", | |
| " <td>817.5707</td>\n", | |
| " <td>0.0000</td>\n", | |
| " <td>47.13541</td>\n", | |
| " <td>29.16667</td>\n", | |
| " <td>71.61458</td>\n", | |
| " <td>30.468750</td>\n", | |
| " <td>30.208330</td>\n", | |
| " <td>38.28125</td>\n", | |
| " <td>68.28703</td>\n", | |
| " <td>52.37268</td>\n", | |
| " <td>48.32176</td>\n", | |
| " <td>41.087960</td>\n", | |
| " <td>212.3843</td>\n", | |
| " <td>153.6458</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>231.1921</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220316</th>\n", | |
| " <td>2018-08-31 23:56:00</td>\n", | |
| " <td>2.400463</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.564240</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>630.902771</td>\n", | |
| " <td>65.83363</td>\n", | |
| " <td>15.154800</td>\n", | |
| " <td>16.70284</td>\n", | |
| " <td>15.653930</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>43.21038</td>\n", | |
| " <td>54.526020</td>\n", | |
| " <td>38.534850</td>\n", | |
| " <td>13.242270</td>\n", | |
| " <td>422.1567</td>\n", | |
| " <td>463.1928</td>\n", | |
| " <td>468.4388</td>\n", | |
| " <td>2.618476</td>\n", | |
| " <td>676.6547</td>\n", | |
| " <td>406.2575</td>\n", | |
| " <td>895.5599</td>\n", | |
| " <td>541.7014</td>\n", | |
| " <td>1106.3710</td>\n", | |
| " <td>609.4917</td>\n", | |
| " <td>698.4915</td>\n", | |
| " <td>800.1906</td>\n", | |
| " <td>697.8002</td>\n", | |
| " <td>797.5571</td>\n", | |
| " <td>510.9510</td>\n", | |
| " <td>672.2222</td>\n", | |
| " <td>927.0833</td>\n", | |
| " <td>907.9463</td>\n", | |
| " <td>487.8679</td>\n", | |
| " <td>262.2222</td>\n", | |
| " <td>568.1035</td>\n", | |
| " <td>807.0151</td>\n", | |
| " <td>0.0000</td>\n", | |
| " <td>46.87500</td>\n", | |
| " <td>28.90625</td>\n", | |
| " <td>73.17708</td>\n", | |
| " <td>30.208332</td>\n", | |
| " <td>29.947920</td>\n", | |
| " <td>38.28125</td>\n", | |
| " <td>66.84028</td>\n", | |
| " <td>50.63657</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>40.798610</td>\n", | |
| " <td>213.8310</td>\n", | |
| " <td>156.2500</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>231.1921</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220317</th>\n", | |
| " <td>2018-08-31 23:57:00</td>\n", | |
| " <td>2.396528</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520830</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>625.925903</td>\n", | |
| " <td>67.29445</td>\n", | |
| " <td>15.089700</td>\n", | |
| " <td>16.70284</td>\n", | |
| " <td>15.697340</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>43.12836</td>\n", | |
| " <td>55.117790</td>\n", | |
| " <td>38.526780</td>\n", | |
| " <td>13.188660</td>\n", | |
| " <td>420.2166</td>\n", | |
| " <td>462.4065</td>\n", | |
| " <td>468.6293</td>\n", | |
| " <td>2.620500</td>\n", | |
| " <td>677.3162</td>\n", | |
| " <td>407.1144</td>\n", | |
| " <td>892.2204</td>\n", | |
| " <td>542.8578</td>\n", | |
| " <td>1106.6980</td>\n", | |
| " <td>610.9940</td>\n", | |
| " <td>703.1645</td>\n", | |
| " <td>800.3767</td>\n", | |
| " <td>704.6601</td>\n", | |
| " <td>799.3120</td>\n", | |
| " <td>492.7720</td>\n", | |
| " <td>689.3519</td>\n", | |
| " <td>924.4791</td>\n", | |
| " <td>926.8102</td>\n", | |
| " <td>494.1249</td>\n", | |
| " <td>260.8372</td>\n", | |
| " <td>553.8872</td>\n", | |
| " <td>805.5605</td>\n", | |
| " <td>0.0000</td>\n", | |
| " <td>46.09375</td>\n", | |
| " <td>28.64583</td>\n", | |
| " <td>77.08333</td>\n", | |
| " <td>29.947920</td>\n", | |
| " <td>30.208330</td>\n", | |
| " <td>39.06250</td>\n", | |
| " <td>65.39352</td>\n", | |
| " <td>48.90046</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>40.798610</td>\n", | |
| " <td>217.3032</td>\n", | |
| " <td>155.3819</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>232.0602</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220318</th>\n", | |
| " <td>2018-08-31 23:58:00</td>\n", | |
| " <td>2.406366</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520832</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>635.648100</td>\n", | |
| " <td>65.09175</td>\n", | |
| " <td>15.118630</td>\n", | |
| " <td>16.56539</td>\n", | |
| " <td>15.740740</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>42.35746</td>\n", | |
| " <td>55.993210</td>\n", | |
| " <td>38.891590</td>\n", | |
| " <td>13.173460</td>\n", | |
| " <td>420.5700</td>\n", | |
| " <td>457.0362</td>\n", | |
| " <td>459.7941</td>\n", | |
| " <td>2.514596</td>\n", | |
| " <td>672.6165</td>\n", | |
| " <td>404.3277</td>\n", | |
| " <td>887.9969</td>\n", | |
| " <td>539.3630</td>\n", | |
| " <td>1103.9550</td>\n", | |
| " <td>605.7183</td>\n", | |
| " <td>697.3713</td>\n", | |
| " <td>793.7070</td>\n", | |
| " <td>706.9692</td>\n", | |
| " <td>793.0610</td>\n", | |
| " <td>490.2170</td>\n", | |
| " <td>687.0370</td>\n", | |
| " <td>931.7708</td>\n", | |
| " <td>915.4362</td>\n", | |
| " <td>484.1161</td>\n", | |
| " <td>261.3184</td>\n", | |
| " <td>559.4439</td>\n", | |
| " <td>807.0808</td>\n", | |
| " <td>0.0000</td>\n", | |
| " <td>45.83333</td>\n", | |
| " <td>28.38542</td>\n", | |
| " <td>78.64583</td>\n", | |
| " <td>29.947916</td>\n", | |
| " <td>30.208332</td>\n", | |
| " <td>40.62500</td>\n", | |
| " <td>64.23611</td>\n", | |
| " <td>47.74306</td>\n", | |
| " <td>48.32176</td>\n", | |
| " <td>40.509258</td>\n", | |
| " <td>222.5116</td>\n", | |
| " <td>153.9352</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>234.0856</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220319</th>\n", | |
| " <td>2018-08-31 23:59:00</td>\n", | |
| " <td>2.396528</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520832</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>639.814800</td>\n", | |
| " <td>65.45634</td>\n", | |
| " <td>15.118630</td>\n", | |
| " <td>16.65220</td>\n", | |
| " <td>15.653930</td>\n", | |
| " <td>15.01013</td>\n", | |
| " <td>42.62814</td>\n", | |
| " <td>56.496420</td>\n", | |
| " <td>39.409570</td>\n", | |
| " <td>13.125930</td>\n", | |
| " <td>421.2080</td>\n", | |
| " <td>468.9915</td>\n", | |
| " <td>456.5726</td>\n", | |
| " <td>2.487299</td>\n", | |
| " <td>676.5834</td>\n", | |
| " <td>405.6293</td>\n", | |
| " <td>897.8508</td>\n", | |
| " <td>542.0950</td>\n", | |
| " <td>1108.8270</td>\n", | |
| " <td>608.5364</td>\n", | |
| " <td>698.0792</td>\n", | |
| " <td>800.0387</td>\n", | |
| " <td>703.6251</td>\n", | |
| " <td>800.2143</td>\n", | |
| " <td>496.4068</td>\n", | |
| " <td>686.1111</td>\n", | |
| " <td>917.7083</td>\n", | |
| " <td>926.3979</td>\n", | |
| " <td>489.0367</td>\n", | |
| " <td>258.4387</td>\n", | |
| " <td>558.0558</td>\n", | |
| " <td>811.1204</td>\n", | |
| " <td>0.0000</td>\n", | |
| " <td>45.31250</td>\n", | |
| " <td>27.86458</td>\n", | |
| " <td>77.86458</td>\n", | |
| " <td>29.947916</td>\n", | |
| " <td>30.208332</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>62.78935</td>\n", | |
| " <td>46.29630</td>\n", | |
| " <td>48.90046</td>\n", | |
| " <td>40.219910</td>\n", | |
| " <td>227.4306</td>\n", | |
| " <td>150.4630</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>234.0856</td>\n", | |
| " <td>NORMAL</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>203165 rows × 53 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " timestamp sensor_00 ... sensor_51 machine_status\n", | |
| "17155 2018-04-12 21:55:00 0.000000 ... 324.6528 BROKEN\n", | |
| "17156 2018-04-12 21:56:00 0.000000 ... 341.7245 RECOVERING\n", | |
| "17157 2018-04-12 21:57:00 0.000000 ... 466.1458 RECOVERING\n", | |
| "17158 2018-04-12 21:58:00 0.000000 ... 366.0301 RECOVERING\n", | |
| "17159 2018-04-12 21:59:00 0.000000 ... 374.4213 RECOVERING\n", | |
| "... ... ... ... ... ...\n", | |
| "220315 2018-08-31 23:55:00 2.407350 ... 231.1921 NORMAL\n", | |
| "220316 2018-08-31 23:56:00 2.400463 ... 231.1921 NORMAL\n", | |
| "220317 2018-08-31 23:57:00 2.396528 ... 232.0602 NORMAL\n", | |
| "220318 2018-08-31 23:58:00 2.406366 ... 234.0856 NORMAL\n", | |
| "220319 2018-08-31 23:59:00 2.396528 ... 234.0856 NORMAL\n", | |
| "\n", | |
| "[203165 rows x 53 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "-VSDjBuWup83", | |
| "outputId": "064a6614-9b0a-4a7e-a376-b6e53edcbce1" | |
| }, | |
| "source": [ | |
| "recovering_times_hours = []\n", | |
| "\n", | |
| "for i in df_status_BROKEN.index.values:\n", | |
| " print(\"\\nMachine in status 'BROKEN' in row: {0}\".format(i) )\n", | |
| "\n", | |
| " still_broken = True\n", | |
| " j = i\n", | |
| " while still_broken:\n", | |
| " j += 1\n", | |
| " machine_status_in_row_j = df.iloc[j][\"machine_status\"] \n", | |
| " if machine_status_in_row_j != \"RECOVERING\":\n", | |
| " still_broken = False\n", | |
| " \n", | |
| " print(\"Machine went back to status '{0}' after {1} rows\"\n", | |
| " .format(machine_status_in_row_j, j-i-1)\n", | |
| " )\n", | |
| " recovering_hours = (j-i)/60\n", | |
| " recovering_days = recovering_hours / 24\n", | |
| " print(\"Machine was in 'RECOVERING' state for {0:.1f} hours : {1:.1f} days\"\n", | |
| " .format( recovering_hours, recovering_days )\n", | |
| " )\n", | |
| " \n", | |
| " recovering_times_hours.append(recovering_hours)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 17155\n", | |
| "Machine went back to status 'NORMAL' after 944 rows\n", | |
| "Machine was in 'RECOVERING' state for 15.8 hours : 0.7 days\n", | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 24510\n", | |
| "Machine went back to status 'NORMAL' after 3110 rows\n", | |
| "Machine was in 'RECOVERING' state for 51.9 hours : 2.2 days\n", | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 69318\n", | |
| "Machine went back to status 'NORMAL' after 1312 rows\n", | |
| "Machine was in 'RECOVERING' state for 21.9 hours : 0.9 days\n", | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 77790\n", | |
| "Machine went back to status 'NORMAL' after 605 rows\n", | |
| "Machine was in 'RECOVERING' state for 10.1 hours : 0.4 days\n", | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 128040\n", | |
| "Machine went back to status 'NORMAL' after 8390 rows\n", | |
| "Machine was in 'RECOVERING' state for 139.8 hours : 5.8 days\n", | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 141131\n", | |
| "Machine went back to status 'NORMAL' after 41 rows\n", | |
| "Machine was in 'RECOVERING' state for 0.7 hours : 0.0 days\n", | |
| "\n", | |
| "Machine in status 'BROKEN' in row: 166440\n", | |
| "Machine went back to status 'NORMAL' after 75 rows\n", | |
| "Machine was in 'RECOVERING' state for 1.3 hours : 0.1 days\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "su6DzYYG5Sec" | |
| }, | |
| "source": [ | |
| "#### *Normalize the features*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 439 | |
| }, | |
| "id": "MRr-9xaL6EKX", | |
| "outputId": "e0115a90-33e0-411e-c424-c22cd284a110" | |
| }, | |
| "source": [ | |
| "sensor_data = df.iloc[:,1:52]\n", | |
| "sensor_data" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.211800</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.375000</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.989580</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.05560</td>\n", | |
| " <td>201.3889</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>2.465394</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.211800</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>634.375000</td>\n", | |
| " <td>76.45975</td>\n", | |
| " <td>13.41146</td>\n", | |
| " <td>16.13136</td>\n", | |
| " <td>15.56713</td>\n", | |
| " <td>15.05353</td>\n", | |
| " <td>37.22740</td>\n", | |
| " <td>47.52422</td>\n", | |
| " <td>31.11716</td>\n", | |
| " <td>1.681353</td>\n", | |
| " <td>419.5747</td>\n", | |
| " <td>461.8781</td>\n", | |
| " <td>466.3284</td>\n", | |
| " <td>2.565284</td>\n", | |
| " <td>665.3993</td>\n", | |
| " <td>398.9862</td>\n", | |
| " <td>880.0001</td>\n", | |
| " <td>498.8926</td>\n", | |
| " <td>975.9409</td>\n", | |
| " <td>627.6740</td>\n", | |
| " <td>741.7151</td>\n", | |
| " <td>848.0708</td>\n", | |
| " <td>429.0377</td>\n", | |
| " <td>785.1935</td>\n", | |
| " <td>684.9443</td>\n", | |
| " <td>594.4445</td>\n", | |
| " <td>682.8125</td>\n", | |
| " <td>680.4416</td>\n", | |
| " <td>433.7037</td>\n", | |
| " <td>171.9375</td>\n", | |
| " <td>341.9039</td>\n", | |
| " <td>195.0655</td>\n", | |
| " <td>90.32386</td>\n", | |
| " <td>40.36458</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>70.57291</td>\n", | |
| " <td>30.989580</td>\n", | |
| " <td>31.770832</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>39.641200</td>\n", | |
| " <td>65.68287</td>\n", | |
| " <td>50.92593</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>157.9861</td>\n", | |
| " <td>67.70834</td>\n", | |
| " <td>243.05560</td>\n", | |
| " <td>201.3889</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>2.444734</td>\n", | |
| " <td>47.35243</td>\n", | |
| " <td>53.211800</td>\n", | |
| " <td>46.397570</td>\n", | |
| " <td>638.888900</td>\n", | |
| " <td>73.54598</td>\n", | |
| " <td>13.32465</td>\n", | |
| " <td>16.03733</td>\n", | |
| " <td>15.61777</td>\n", | |
| " <td>15.01013</td>\n", | |
| " <td>37.86777</td>\n", | |
| " <td>48.17723</td>\n", | |
| " <td>32.08894</td>\n", | |
| " <td>1.708474</td>\n", | |
| " <td>420.8480</td>\n", | |
| " <td>462.7798</td>\n", | |
| " <td>459.6364</td>\n", | |
| " <td>2.500062</td>\n", | |
| " <td>666.2234</td>\n", | |
| " <td>399.9418</td>\n", | |
| " <td>880.4237</td>\n", | |
| " <td>501.3617</td>\n", | |
| " <td>982.7342</td>\n", | |
| " <td>631.1326</td>\n", | |
| " <td>740.8031</td>\n", | |
| " <td>849.8997</td>\n", | |
| " <td>454.2390</td>\n", | |
| " <td>778.5734</td>\n", | |
| " <td>715.6266</td>\n", | |
| " <td>661.5740</td>\n", | |
| " <td>721.8750</td>\n", | |
| " <td>694.7721</td>\n", | |
| " <td>441.2635</td>\n", | |
| " <td>169.9820</td>\n", | |
| " <td>343.1955</td>\n", | |
| " <td>200.9694</td>\n", | |
| " <td>93.90508</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>31.25000</td>\n", | |
| " <td>69.53125</td>\n", | |
| " <td>30.468750</td>\n", | |
| " <td>31.770830</td>\n", | |
| " <td>41.66666</td>\n", | |
| " <td>39.351852</td>\n", | |
| " <td>65.39352</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194443</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>67.12963</td>\n", | |
| " <td>241.31940</td>\n", | |
| " <td>203.7037</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>2.460474</td>\n", | |
| " <td>47.09201</td>\n", | |
| " <td>53.168400</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>628.125000</td>\n", | |
| " <td>76.98898</td>\n", | |
| " <td>13.31742</td>\n", | |
| " <td>16.24711</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>38.57977</td>\n", | |
| " <td>48.65607</td>\n", | |
| " <td>31.67221</td>\n", | |
| " <td>1.579427</td>\n", | |
| " <td>420.7494</td>\n", | |
| " <td>462.8980</td>\n", | |
| " <td>460.8858</td>\n", | |
| " <td>2.509521</td>\n", | |
| " <td>666.0114</td>\n", | |
| " <td>399.1046</td>\n", | |
| " <td>878.8917</td>\n", | |
| " <td>499.0430</td>\n", | |
| " <td>977.7520</td>\n", | |
| " <td>625.4076</td>\n", | |
| " <td>739.2722</td>\n", | |
| " <td>847.7579</td>\n", | |
| " <td>474.8731</td>\n", | |
| " <td>779.5091</td>\n", | |
| " <td>690.4011</td>\n", | |
| " <td>686.1111</td>\n", | |
| " <td>754.6875</td>\n", | |
| " <td>683.3831</td>\n", | |
| " <td>446.2493</td>\n", | |
| " <td>166.4987</td>\n", | |
| " <td>343.9586</td>\n", | |
| " <td>193.1689</td>\n", | |
| " <td>101.04060</td>\n", | |
| " <td>41.92708</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>72.13541</td>\n", | |
| " <td>30.468750</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>40.88541</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>64.81481</td>\n", | |
| " <td>51.21528</td>\n", | |
| " <td>38.194440</td>\n", | |
| " <td>155.9606</td>\n", | |
| " <td>66.84028</td>\n", | |
| " <td>240.45140</td>\n", | |
| " <td>203.1250</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>2.445718</td>\n", | |
| " <td>47.13541</td>\n", | |
| " <td>53.211800</td>\n", | |
| " <td>46.397568</td>\n", | |
| " <td>636.458300</td>\n", | |
| " <td>76.58897</td>\n", | |
| " <td>13.35359</td>\n", | |
| " <td>16.21094</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.08247</td>\n", | |
| " <td>39.48939</td>\n", | |
| " <td>49.06298</td>\n", | |
| " <td>31.95202</td>\n", | |
| " <td>1.683831</td>\n", | |
| " <td>419.8926</td>\n", | |
| " <td>461.4906</td>\n", | |
| " <td>468.2206</td>\n", | |
| " <td>2.604785</td>\n", | |
| " <td>663.2111</td>\n", | |
| " <td>400.5426</td>\n", | |
| " <td>882.5874</td>\n", | |
| " <td>498.5383</td>\n", | |
| " <td>979.5755</td>\n", | |
| " <td>627.1830</td>\n", | |
| " <td>737.6033</td>\n", | |
| " <td>846.9182</td>\n", | |
| " <td>408.8159</td>\n", | |
| " <td>785.2307</td>\n", | |
| " <td>704.6937</td>\n", | |
| " <td>631.4814</td>\n", | |
| " <td>766.1458</td>\n", | |
| " <td>702.4431</td>\n", | |
| " <td>433.9081</td>\n", | |
| " <td>164.7498</td>\n", | |
| " <td>339.9630</td>\n", | |
| " <td>193.8770</td>\n", | |
| " <td>101.70380</td>\n", | |
| " <td>42.70833</td>\n", | |
| " <td>31.51042</td>\n", | |
| " <td>76.82291</td>\n", | |
| " <td>30.989580</td>\n", | |
| " <td>31.510420</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>65.10416</td>\n", | |
| " <td>51.79398</td>\n", | |
| " <td>38.773150</td>\n", | |
| " <td>158.2755</td>\n", | |
| " <td>66.55093</td>\n", | |
| " <td>242.18750</td>\n", | |
| " <td>201.3889</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", | |
| " <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", | |
| " <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>220315</th>\n", | |
| " <td>2.407350</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520830</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>634.722229</td>\n", | |
| " <td>64.59095</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>16.65220</td>\n", | |
| " <td>15.65393</td>\n", | |
| " <td>15.16204</td>\n", | |
| " <td>43.17085</td>\n", | |
| " <td>54.16052</td>\n", | |
| " <td>38.05424</td>\n", | |
| " <td>13.265320</td>\n", | |
| " <td>420.7993</td>\n", | |
| " <td>463.2318</td>\n", | |
| " <td>458.3615</td>\n", | |
| " <td>2.499117</td>\n", | |
| " <td>676.6655</td>\n", | |
| " <td>405.7680</td>\n", | |
| " <td>894.5920</td>\n", | |
| " <td>543.5801</td>\n", | |
| " <td>1109.5010</td>\n", | |
| " <td>611.1745</td>\n", | |
| " <td>700.5885</td>\n", | |
| " <td>796.5964</td>\n", | |
| " <td>692.1138</td>\n", | |
| " <td>779.2067</td>\n", | |
| " <td>485.0358</td>\n", | |
| " <td>691.6666</td>\n", | |
| " <td>974.9999</td>\n", | |
| " <td>927.6135</td>\n", | |
| " <td>477.3156</td>\n", | |
| " <td>266.0334</td>\n", | |
| " <td>578.5221</td>\n", | |
| " <td>817.5707</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>47.13541</td>\n", | |
| " <td>29.16667</td>\n", | |
| " <td>71.61458</td>\n", | |
| " <td>30.468750</td>\n", | |
| " <td>30.208330</td>\n", | |
| " <td>38.28125</td>\n", | |
| " <td>68.287030</td>\n", | |
| " <td>52.37268</td>\n", | |
| " <td>48.32176</td>\n", | |
| " <td>41.087960</td>\n", | |
| " <td>212.3843</td>\n", | |
| " <td>153.64580</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>231.1921</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220316</th>\n", | |
| " <td>2.400463</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.564240</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>630.902771</td>\n", | |
| " <td>65.83363</td>\n", | |
| " <td>15.15480</td>\n", | |
| " <td>16.70284</td>\n", | |
| " <td>15.65393</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>43.21038</td>\n", | |
| " <td>54.52602</td>\n", | |
| " <td>38.53485</td>\n", | |
| " <td>13.242270</td>\n", | |
| " <td>422.1567</td>\n", | |
| " <td>463.1928</td>\n", | |
| " <td>468.4388</td>\n", | |
| " <td>2.618476</td>\n", | |
| " <td>676.6547</td>\n", | |
| " <td>406.2575</td>\n", | |
| " <td>895.5599</td>\n", | |
| " <td>541.7014</td>\n", | |
| " <td>1106.3710</td>\n", | |
| " <td>609.4917</td>\n", | |
| " <td>698.4915</td>\n", | |
| " <td>800.1906</td>\n", | |
| " <td>697.8002</td>\n", | |
| " <td>797.5571</td>\n", | |
| " <td>510.9510</td>\n", | |
| " <td>672.2222</td>\n", | |
| " <td>927.0833</td>\n", | |
| " <td>907.9463</td>\n", | |
| " <td>487.8679</td>\n", | |
| " <td>262.2222</td>\n", | |
| " <td>568.1035</td>\n", | |
| " <td>807.0151</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>46.87500</td>\n", | |
| " <td>28.90625</td>\n", | |
| " <td>73.17708</td>\n", | |
| " <td>30.208332</td>\n", | |
| " <td>29.947920</td>\n", | |
| " <td>38.28125</td>\n", | |
| " <td>66.840280</td>\n", | |
| " <td>50.63657</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>40.798610</td>\n", | |
| " <td>213.8310</td>\n", | |
| " <td>156.25000</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>231.1921</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220317</th>\n", | |
| " <td>2.396528</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520830</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>625.925903</td>\n", | |
| " <td>67.29445</td>\n", | |
| " <td>15.08970</td>\n", | |
| " <td>16.70284</td>\n", | |
| " <td>15.69734</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>43.12836</td>\n", | |
| " <td>55.11779</td>\n", | |
| " <td>38.52678</td>\n", | |
| " <td>13.188660</td>\n", | |
| " <td>420.2166</td>\n", | |
| " <td>462.4065</td>\n", | |
| " <td>468.6293</td>\n", | |
| " <td>2.620500</td>\n", | |
| " <td>677.3162</td>\n", | |
| " <td>407.1144</td>\n", | |
| " <td>892.2204</td>\n", | |
| " <td>542.8578</td>\n", | |
| " <td>1106.6980</td>\n", | |
| " <td>610.9940</td>\n", | |
| " <td>703.1645</td>\n", | |
| " <td>800.3767</td>\n", | |
| " <td>704.6601</td>\n", | |
| " <td>799.3120</td>\n", | |
| " <td>492.7720</td>\n", | |
| " <td>689.3519</td>\n", | |
| " <td>924.4791</td>\n", | |
| " <td>926.8102</td>\n", | |
| " <td>494.1249</td>\n", | |
| " <td>260.8372</td>\n", | |
| " <td>553.8872</td>\n", | |
| " <td>805.5605</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>46.09375</td>\n", | |
| " <td>28.64583</td>\n", | |
| " <td>77.08333</td>\n", | |
| " <td>29.947920</td>\n", | |
| " <td>30.208330</td>\n", | |
| " <td>39.06250</td>\n", | |
| " <td>65.393520</td>\n", | |
| " <td>48.90046</td>\n", | |
| " <td>48.03241</td>\n", | |
| " <td>40.798610</td>\n", | |
| " <td>217.3032</td>\n", | |
| " <td>155.38190</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>232.0602</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220318</th>\n", | |
| " <td>2.406366</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520832</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>635.648100</td>\n", | |
| " <td>65.09175</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>16.56539</td>\n", | |
| " <td>15.74074</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>42.35746</td>\n", | |
| " <td>55.99321</td>\n", | |
| " <td>38.89159</td>\n", | |
| " <td>13.173460</td>\n", | |
| " <td>420.5700</td>\n", | |
| " <td>457.0362</td>\n", | |
| " <td>459.7941</td>\n", | |
| " <td>2.514596</td>\n", | |
| " <td>672.6165</td>\n", | |
| " <td>404.3277</td>\n", | |
| " <td>887.9969</td>\n", | |
| " <td>539.3630</td>\n", | |
| " <td>1103.9550</td>\n", | |
| " <td>605.7183</td>\n", | |
| " <td>697.3713</td>\n", | |
| " <td>793.7070</td>\n", | |
| " <td>706.9692</td>\n", | |
| " <td>793.0610</td>\n", | |
| " <td>490.2170</td>\n", | |
| " <td>687.0370</td>\n", | |
| " <td>931.7708</td>\n", | |
| " <td>915.4362</td>\n", | |
| " <td>484.1161</td>\n", | |
| " <td>261.3184</td>\n", | |
| " <td>559.4439</td>\n", | |
| " <td>807.0808</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>45.83333</td>\n", | |
| " <td>28.38542</td>\n", | |
| " <td>78.64583</td>\n", | |
| " <td>29.947916</td>\n", | |
| " <td>30.208332</td>\n", | |
| " <td>40.62500</td>\n", | |
| " <td>64.236110</td>\n", | |
| " <td>47.74306</td>\n", | |
| " <td>48.32176</td>\n", | |
| " <td>40.509258</td>\n", | |
| " <td>222.5116</td>\n", | |
| " <td>153.93520</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>234.0856</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>220319</th>\n", | |
| " <td>2.396528</td>\n", | |
| " <td>47.69965</td>\n", | |
| " <td>50.520832</td>\n", | |
| " <td>43.142361</td>\n", | |
| " <td>639.814800</td>\n", | |
| " <td>65.45634</td>\n", | |
| " <td>15.11863</td>\n", | |
| " <td>16.65220</td>\n", | |
| " <td>15.65393</td>\n", | |
| " <td>15.01013</td>\n", | |
| " <td>42.62814</td>\n", | |
| " <td>56.49642</td>\n", | |
| " <td>39.40957</td>\n", | |
| " <td>13.125930</td>\n", | |
| " <td>421.2080</td>\n", | |
| " <td>468.9915</td>\n", | |
| " <td>456.5726</td>\n", | |
| " <td>2.487299</td>\n", | |
| " <td>676.5834</td>\n", | |
| " <td>405.6293</td>\n", | |
| " <td>897.8508</td>\n", | |
| " <td>542.0950</td>\n", | |
| " <td>1108.8270</td>\n", | |
| " <td>608.5364</td>\n", | |
| " <td>698.0792</td>\n", | |
| " <td>800.0387</td>\n", | |
| " <td>703.6251</td>\n", | |
| " <td>800.2143</td>\n", | |
| " <td>496.4068</td>\n", | |
| " <td>686.1111</td>\n", | |
| " <td>917.7083</td>\n", | |
| " <td>926.3979</td>\n", | |
| " <td>489.0367</td>\n", | |
| " <td>258.4387</td>\n", | |
| " <td>558.0558</td>\n", | |
| " <td>811.1204</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>45.31250</td>\n", | |
| " <td>27.86458</td>\n", | |
| " <td>77.86458</td>\n", | |
| " <td>29.947916</td>\n", | |
| " <td>30.208332</td>\n", | |
| " <td>41.40625</td>\n", | |
| " <td>62.789350</td>\n", | |
| " <td>46.29630</td>\n", | |
| " <td>48.90046</td>\n", | |
| " <td>40.219910</td>\n", | |
| " <td>227.4306</td>\n", | |
| " <td>150.46300</td>\n", | |
| " <td>183.04926</td>\n", | |
| " <td>234.0856</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>220320 rows × 51 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " sensor_00 sensor_01 sensor_02 ... sensor_49 sensor_50 sensor_51\n", | |
| "0 2.465394 47.09201 53.211800 ... 67.70834 243.05560 201.3889\n", | |
| "1 2.465394 47.09201 53.211800 ... 67.70834 243.05560 201.3889\n", | |
| "2 2.444734 47.35243 53.211800 ... 67.12963 241.31940 203.7037\n", | |
| "3 2.460474 47.09201 53.168400 ... 66.84028 240.45140 203.1250\n", | |
| "4 2.445718 47.13541 53.211800 ... 66.55093 242.18750 201.3889\n", | |
| "... ... ... ... ... ... ... ...\n", | |
| "220315 2.407350 47.69965 50.520830 ... 153.64580 183.04926 231.1921\n", | |
| "220316 2.400463 47.69965 50.564240 ... 156.25000 183.04926 231.1921\n", | |
| "220317 2.396528 47.69965 50.520830 ... 155.38190 183.04926 232.0602\n", | |
| "220318 2.406366 47.69965 50.520832 ... 153.93520 183.04926 234.0856\n", | |
| "220319 2.396528 47.69965 50.520832 ... 150.46300 183.04926 234.0856\n", | |
| "\n", | |
| "[220320 rows x 51 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 317 | |
| }, | |
| "id": "r2dFLTLFNruD", | |
| "outputId": "2357d94e-1bac-4c21-bdf3-eab2081beb8d" | |
| }, | |
| "source": [ | |
| "sensor_data.describe()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>sensor_00</th>\n", | |
| " <th>sensor_01</th>\n", | |
| " <th>sensor_02</th>\n", | |
| " <th>sensor_03</th>\n", | |
| " <th>sensor_04</th>\n", | |
| " <th>sensor_05</th>\n", | |
| " <th>sensor_06</th>\n", | |
| " <th>sensor_07</th>\n", | |
| " <th>sensor_08</th>\n", | |
| " <th>sensor_09</th>\n", | |
| " <th>sensor_10</th>\n", | |
| " <th>sensor_11</th>\n", | |
| " <th>sensor_12</th>\n", | |
| " <th>sensor_13</th>\n", | |
| " <th>sensor_14</th>\n", | |
| " <th>sensor_16</th>\n", | |
| " <th>sensor_17</th>\n", | |
| " <th>sensor_18</th>\n", | |
| " <th>sensor_19</th>\n", | |
| " <th>sensor_20</th>\n", | |
| " <th>sensor_21</th>\n", | |
| " <th>sensor_22</th>\n", | |
| " <th>sensor_23</th>\n", | |
| " <th>sensor_24</th>\n", | |
| " <th>sensor_25</th>\n", | |
| " <th>sensor_26</th>\n", | |
| " <th>sensor_27</th>\n", | |
| " <th>sensor_28</th>\n", | |
| " <th>sensor_29</th>\n", | |
| " <th>sensor_30</th>\n", | |
| " <th>sensor_31</th>\n", | |
| " <th>sensor_32</th>\n", | |
| " <th>sensor_33</th>\n", | |
| " <th>sensor_34</th>\n", | |
| " <th>sensor_35</th>\n", | |
| " <th>sensor_36</th>\n", | |
| " <th>sensor_37</th>\n", | |
| " <th>sensor_38</th>\n", | |
| " <th>sensor_39</th>\n", | |
| " <th>sensor_40</th>\n", | |
| " <th>sensor_41</th>\n", | |
| " <th>sensor_42</th>\n", | |
| " <th>sensor_43</th>\n", | |
| " <th>sensor_44</th>\n", | |
| " <th>sensor_45</th>\n", | |
| " <th>sensor_46</th>\n", | |
| " <th>sensor_47</th>\n", | |
| " <th>sensor_48</th>\n", | |
| " <th>sensor_49</th>\n", | |
| " <th>sensor_50</th>\n", | |
| " <th>sensor_51</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " <td>220320.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>2.372221</td>\n", | |
| " <td>47.591611</td>\n", | |
| " <td>50.867392</td>\n", | |
| " <td>43.752481</td>\n", | |
| " <td>590.673936</td>\n", | |
| " <td>73.396414</td>\n", | |
| " <td>13.501537</td>\n", | |
| " <td>15.843152</td>\n", | |
| " <td>15.200721</td>\n", | |
| " <td>14.799210</td>\n", | |
| " <td>41.470339</td>\n", | |
| " <td>41.918319</td>\n", | |
| " <td>29.136975</td>\n", | |
| " <td>7.078858</td>\n", | |
| " <td>376.860041</td>\n", | |
| " <td>416.472892</td>\n", | |
| " <td>421.127517</td>\n", | |
| " <td>2.303785</td>\n", | |
| " <td>590.829775</td>\n", | |
| " <td>360.805165</td>\n", | |
| " <td>796.225942</td>\n", | |
| " <td>459.792815</td>\n", | |
| " <td>922.609264</td>\n", | |
| " <td>556.235397</td>\n", | |
| " <td>649.144799</td>\n", | |
| " <td>786.411781</td>\n", | |
| " <td>501.506589</td>\n", | |
| " <td>851.690339</td>\n", | |
| " <td>576.195305</td>\n", | |
| " <td>614.596442</td>\n", | |
| " <td>863.323100</td>\n", | |
| " <td>804.283915</td>\n", | |
| " <td>486.405980</td>\n", | |
| " <td>234.971776</td>\n", | |
| " <td>427.129817</td>\n", | |
| " <td>593.033876</td>\n", | |
| " <td>60.787360</td>\n", | |
| " <td>49.655946</td>\n", | |
| " <td>36.610444</td>\n", | |
| " <td>68.844530</td>\n", | |
| " <td>35.365126</td>\n", | |
| " <td>35.453455</td>\n", | |
| " <td>43.879591</td>\n", | |
| " <td>42.656877</td>\n", | |
| " <td>43.094984</td>\n", | |
| " <td>48.018585</td>\n", | |
| " <td>44.340903</td>\n", | |
| " <td>150.889044</td>\n", | |
| " <td>57.119968</td>\n", | |
| " <td>183.049260</td>\n", | |
| " <td>202.699667</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>0.402564</td>\n", | |
| " <td>3.293904</td>\n", | |
| " <td>3.666662</td>\n", | |
| " <td>2.418782</td>\n", | |
| " <td>144.017702</td>\n", | |
| " <td>17.297501</td>\n", | |
| " <td>2.140046</td>\n", | |
| " <td>2.173755</td>\n", | |
| " <td>2.013639</td>\n", | |
| " <td>2.070033</td>\n", | |
| " <td>12.092997</td>\n", | |
| " <td>13.055862</td>\n", | |
| " <td>10.113499</td>\n", | |
| " <td>6.901457</td>\n", | |
| " <td>113.200986</td>\n", | |
| " <td>126.063772</td>\n", | |
| " <td>129.142691</td>\n", | |
| " <td>0.765803</td>\n", | |
| " <td>199.338581</td>\n", | |
| " <td>101.970415</td>\n", | |
| " <td>226.671085</td>\n", | |
| " <td>154.513958</td>\n", | |
| " <td>291.824683</td>\n", | |
| " <td>182.291359</td>\n", | |
| " <td>220.847121</td>\n", | |
| " <td>246.652412</td>\n", | |
| " <td>169.817006</td>\n", | |
| " <td>313.062664</td>\n", | |
| " <td>225.727198</td>\n", | |
| " <td>195.610904</td>\n", | |
| " <td>283.534464</td>\n", | |
| " <td>260.562141</td>\n", | |
| " <td>150.746362</td>\n", | |
| " <td>88.372856</td>\n", | |
| " <td>141.767371</td>\n", | |
| " <td>289.375003</td>\n", | |
| " <td>37.603518</td>\n", | |
| " <td>10.539752</td>\n", | |
| " <td>15.612766</td>\n", | |
| " <td>21.369829</td>\n", | |
| " <td>7.898181</td>\n", | |
| " <td>10.258892</td>\n", | |
| " <td>11.043727</td>\n", | |
| " <td>11.575646</td>\n", | |
| " <td>12.836733</td>\n", | |
| " <td>15.640325</td>\n", | |
| " <td>10.441797</td>\n", | |
| " <td>82.239917</td>\n", | |
| " <td>19.142425</td>\n", | |
| " <td>52.630590</td>\n", | |
| " <td>105.693568</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>33.159720</td>\n", | |
| " <td>31.640620</td>\n", | |
| " <td>2.798032</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.014468</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.028935</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>32.409550</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>95.527660</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>43.154790</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>4.319347</td>\n", | |
| " <td>0.636574</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>23.958330</td>\n", | |
| " <td>0.240716</td>\n", | |
| " <td>6.460602</td>\n", | |
| " <td>54.882370</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.260970</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>24.479166</td>\n", | |
| " <td>19.270830</td>\n", | |
| " <td>23.437500</td>\n", | |
| " <td>20.833330</td>\n", | |
| " <td>22.135416</td>\n", | |
| " <td>24.479166</td>\n", | |
| " <td>25.752316</td>\n", | |
| " <td>26.331018</td>\n", | |
| " <td>26.331018</td>\n", | |
| " <td>27.199070</td>\n", | |
| " <td>26.331018</td>\n", | |
| " <td>26.620370</td>\n", | |
| " <td>27.488426</td>\n", | |
| " <td>27.777779</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>2.419155</td>\n", | |
| " <td>46.310760</td>\n", | |
| " <td>50.390620</td>\n", | |
| " <td>42.838539</td>\n", | |
| " <td>626.620400</td>\n", | |
| " <td>69.977213</td>\n", | |
| " <td>13.346350</td>\n", | |
| " <td>15.856480</td>\n", | |
| " <td>15.183740</td>\n", | |
| " <td>15.010130</td>\n", | |
| " <td>40.705417</td>\n", | |
| " <td>38.857022</td>\n", | |
| " <td>28.687178</td>\n", | |
| " <td>1.538652</td>\n", | |
| " <td>418.100925</td>\n", | |
| " <td>459.447800</td>\n", | |
| " <td>454.131950</td>\n", | |
| " <td>2.447450</td>\n", | |
| " <td>662.766800</td>\n", | |
| " <td>398.020575</td>\n", | |
| " <td>875.461300</td>\n", | |
| " <td>478.942500</td>\n", | |
| " <td>950.919700</td>\n", | |
| " <td>601.149500</td>\n", | |
| " <td>693.932600</td>\n", | |
| " <td>790.343525</td>\n", | |
| " <td>448.299675</td>\n", | |
| " <td>782.685650</td>\n", | |
| " <td>518.964700</td>\n", | |
| " <td>627.777800</td>\n", | |
| " <td>839.062400</td>\n", | |
| " <td>760.703950</td>\n", | |
| " <td>489.753000</td>\n", | |
| " <td>172.486475</td>\n", | |
| " <td>353.182075</td>\n", | |
| " <td>288.559000</td>\n", | |
| " <td>28.803398</td>\n", | |
| " <td>45.572910</td>\n", | |
| " <td>32.552080</td>\n", | |
| " <td>57.812500</td>\n", | |
| " <td>32.552080</td>\n", | |
| " <td>32.812500</td>\n", | |
| " <td>39.583330</td>\n", | |
| " <td>36.747684</td>\n", | |
| " <td>36.747684</td>\n", | |
| " <td>40.509258</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>83.912030</td>\n", | |
| " <td>47.743060</td>\n", | |
| " <td>182.581000</td>\n", | |
| " <td>180.555600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>2.455556</td>\n", | |
| " <td>48.133678</td>\n", | |
| " <td>51.649300</td>\n", | |
| " <td>44.227428</td>\n", | |
| " <td>632.638916</td>\n", | |
| " <td>75.576145</td>\n", | |
| " <td>13.628470</td>\n", | |
| " <td>16.167530</td>\n", | |
| " <td>15.451390</td>\n", | |
| " <td>15.082470</td>\n", | |
| " <td>44.290480</td>\n", | |
| " <td>45.362290</td>\n", | |
| " <td>32.515630</td>\n", | |
| " <td>2.930587</td>\n", | |
| " <td>420.106000</td>\n", | |
| " <td>462.855850</td>\n", | |
| " <td>462.017950</td>\n", | |
| " <td>2.533686</td>\n", | |
| " <td>665.672050</td>\n", | |
| " <td>399.366900</td>\n", | |
| " <td>879.697300</td>\n", | |
| " <td>531.854100</td>\n", | |
| " <td>981.924500</td>\n", | |
| " <td>625.872650</td>\n", | |
| " <td>740.199250</td>\n", | |
| " <td>861.831750</td>\n", | |
| " <td>494.475250</td>\n", | |
| " <td>967.231500</td>\n", | |
| " <td>564.894700</td>\n", | |
| " <td>668.981400</td>\n", | |
| " <td>917.708300</td>\n", | |
| " <td>878.807600</td>\n", | |
| " <td>512.267800</td>\n", | |
| " <td>226.367700</td>\n", | |
| " <td>473.340800</td>\n", | |
| " <td>709.637350</td>\n", | |
| " <td>64.291375</td>\n", | |
| " <td>49.479160</td>\n", | |
| " <td>35.416660</td>\n", | |
| " <td>66.406250</td>\n", | |
| " <td>34.895832</td>\n", | |
| " <td>35.156250</td>\n", | |
| " <td>42.968750</td>\n", | |
| " <td>40.509260</td>\n", | |
| " <td>40.219910</td>\n", | |
| " <td>44.849540</td>\n", | |
| " <td>42.534720</td>\n", | |
| " <td>138.020800</td>\n", | |
| " <td>52.662040</td>\n", | |
| " <td>183.049260</td>\n", | |
| " <td>199.942100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>2.499826</td>\n", | |
| " <td>49.479160</td>\n", | |
| " <td>52.777770</td>\n", | |
| " <td>45.312500</td>\n", | |
| " <td>637.615723</td>\n", | |
| " <td>80.911770</td>\n", | |
| " <td>14.539930</td>\n", | |
| " <td>16.427950</td>\n", | |
| " <td>15.697340</td>\n", | |
| " <td>15.118630</td>\n", | |
| " <td>47.463485</td>\n", | |
| " <td>49.656238</td>\n", | |
| " <td>34.939455</td>\n", | |
| " <td>12.859338</td>\n", | |
| " <td>420.997000</td>\n", | |
| " <td>464.302600</td>\n", | |
| " <td>466.855700</td>\n", | |
| " <td>2.587667</td>\n", | |
| " <td>667.146625</td>\n", | |
| " <td>400.088300</td>\n", | |
| " <td>882.129800</td>\n", | |
| " <td>534.254400</td>\n", | |
| " <td>1090.807250</td>\n", | |
| " <td>628.607500</td>\n", | |
| " <td>750.356125</td>\n", | |
| " <td>919.098450</td>\n", | |
| " <td>536.272050</td>\n", | |
| " <td>1043.972000</td>\n", | |
| " <td>743.947000</td>\n", | |
| " <td>697.222200</td>\n", | |
| " <td>981.249900</td>\n", | |
| " <td>943.858175</td>\n", | |
| " <td>555.156900</td>\n", | |
| " <td>316.839525</td>\n", | |
| " <td>528.889800</td>\n", | |
| " <td>837.327975</td>\n", | |
| " <td>90.820915</td>\n", | |
| " <td>53.645830</td>\n", | |
| " <td>39.062500</td>\n", | |
| " <td>77.864580</td>\n", | |
| " <td>37.760410</td>\n", | |
| " <td>36.979164</td>\n", | |
| " <td>46.614580</td>\n", | |
| " <td>45.138890</td>\n", | |
| " <td>44.849540</td>\n", | |
| " <td>51.215280</td>\n", | |
| " <td>46.585650</td>\n", | |
| " <td>208.333300</td>\n", | |
| " <td>60.763890</td>\n", | |
| " <td>204.571800</td>\n", | |
| " <td>214.699100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>2.549016</td>\n", | |
| " <td>56.727430</td>\n", | |
| " <td>56.032990</td>\n", | |
| " <td>48.220490</td>\n", | |
| " <td>800.000000</td>\n", | |
| " <td>99.999880</td>\n", | |
| " <td>22.251160</td>\n", | |
| " <td>23.596640</td>\n", | |
| " <td>24.348960</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>76.106860</td>\n", | |
| " <td>60.000000</td>\n", | |
| " <td>45.000000</td>\n", | |
| " <td>31.187550</td>\n", | |
| " <td>500.000000</td>\n", | |
| " <td>739.741500</td>\n", | |
| " <td>599.999939</td>\n", | |
| " <td>4.873250</td>\n", | |
| " <td>878.917900</td>\n", | |
| " <td>448.907900</td>\n", | |
| " <td>1107.526000</td>\n", | |
| " <td>594.061100</td>\n", | |
| " <td>1227.564000</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " <td>839.575000</td>\n", | |
| " <td>1214.420000</td>\n", | |
| " <td>2000.000000</td>\n", | |
| " <td>1841.146000</td>\n", | |
| " <td>1466.281000</td>\n", | |
| " <td>1600.000000</td>\n", | |
| " <td>1800.000000</td>\n", | |
| " <td>1839.211000</td>\n", | |
| " <td>1578.600000</td>\n", | |
| " <td>425.549800</td>\n", | |
| " <td>694.479126</td>\n", | |
| " <td>984.060700</td>\n", | |
| " <td>174.901200</td>\n", | |
| " <td>417.708300</td>\n", | |
| " <td>547.916600</td>\n", | |
| " <td>512.760400</td>\n", | |
| " <td>420.312500</td>\n", | |
| " <td>374.218800</td>\n", | |
| " <td>408.593700</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " <td>320.312500</td>\n", | |
| " <td>370.370400</td>\n", | |
| " <td>303.530100</td>\n", | |
| " <td>561.632000</td>\n", | |
| " <td>464.409700</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " <td>1000.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " sensor_00 sensor_01 ... sensor_50 sensor_51\n", | |
| "count 220320.000000 220320.000000 ... 220320.000000 220320.000000\n", | |
| "mean 2.372221 47.591611 ... 183.049260 202.699667\n", | |
| "std 0.402564 3.293904 ... 52.630590 105.693568\n", | |
| "min 0.000000 0.000000 ... 27.488426 27.777779\n", | |
| "25% 2.419155 46.310760 ... 182.581000 180.555600\n", | |
| "50% 2.455556 48.133678 ... 183.049260 199.942100\n", | |
| "75% 2.499826 49.479160 ... 204.571800 214.699100\n", | |
| "max 2.549016 56.727430 ... 1000.000000 1000.000000\n", | |
| "\n", | |
| "[8 rows x 51 columns]" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 18 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FbehErLA5tr3" | |
| }, | |
| "source": [ | |
| "**Min-Max Normalization / Scaling**\n", | |
| "\n", | |
| "<center>\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "</center>\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "_ypQjQOh5S4J" | |
| }, | |
| "source": [ | |
| "from sklearn.preprocessing import MinMaxScaler\n", | |
| "\n", | |
| "scaler = MinMaxScaler(feature_range=(0, 1))\n", | |
| "\n", | |
| "scaled_sensor_data = scaler.fit_transform(sensor_data)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bFipgKwA6e1I", | |
| "outputId": "704ec7b1-a9a2-4ada-cff0-1d88f59107c5" | |
| }, | |
| "source": [ | |
| "scaled_sensor_data" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[0.9671944 , 0.83014531, 0.87665996, ..., 0.09385329, 0.22166027,\n", | |
| " 0.17857144],\n", | |
| " [0.9671944 , 0.83014531, 0.87665996, ..., 0.09385329, 0.22166027,\n", | |
| " 0.17857144],\n", | |
| " [0.95908931, 0.83473604, 0.87665996, ..., 0.0925314 , 0.21987499,\n", | |
| " 0.18095238],\n", | |
| " ...,\n", | |
| " [0.9401777 , 0.84085688, 0.75901303, ..., 0.29411756, 0.15995782,\n", | |
| " 0.21011906],\n", | |
| " [0.94403723, 0.84085688, 0.75901312, ..., 0.290813 , 0.15995782,\n", | |
| " 0.21220233],\n", | |
| " [0.9401777 , 0.84085688, 0.75901312, ..., 0.28288179, 0.15995782,\n", | |
| " 0.21220233]])" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "EcLcnmnKBybJ" | |
| }, | |
| "source": [ | |
| "### **Prepare Data**\n", | |
| "\n", | |
| "#### *Check whether the pump failed in given time window of (start, stop)*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "mQefapdeA-GB" | |
| }, | |
| "source": [ | |
| "def check_for_pump_failure(start, stop):\n", | |
| " for min in range(start,stop):\n", | |
| " current_machine_status = df[\"machine_status\"].iloc[min]\n", | |
| " if current_machine_status in [\"BROKEN\", \"RECOVERING\"]:\n", | |
| " return 1 # machine failure\n", | |
| " \n", | |
| " return 0 # no machine failure" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "c4W-nhktBwF2", | |
| "outputId": "f78d0d28-02b3-4f4a-bc16-83ae7ee8f79c" | |
| }, | |
| "source": [ | |
| "check_for_pump_failure(0,17000)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 22 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "VNF4QJCnRGwr", | |
| "outputId": "e58299a2-34b6-44ff-ffbc-2f8f80c5592a" | |
| }, | |
| "source": [ | |
| "df_status_BROKEN.index.values" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([ 17155, 24510, 69318, 77790, 128040, 141131, 166440])" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 26 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "imbwPX_PB3dI", | |
| "outputId": "4f1efdef-0f4e-4770-de73-8b92ca4b5256" | |
| }, | |
| "source": [ | |
| "check_for_pump_failure(17000,20000)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 23 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "gXok5m1mEa6v" | |
| }, | |
| "source": [ | |
| "#### *Prepare Samples for training and testing*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "aXBCjSLHLutO" | |
| }, | |
| "source": [ | |
| "input_minutes_window = 60 # 60 minutes\n", | |
| "output_minutes_window= 60 * 24 # 1 day\n", | |
| "total_n_examples = 5000" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "xPW8S5FYvvso" | |
| }, | |
| "source": [ | |
| "<center>\n", | |
| "\n", | |
| "flatten()\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "</center>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "oNcfwPC1EgMF", | |
| "outputId": "4e2dcbcd-d0fc-4215-9b41-327c0d494d24" | |
| }, | |
| "source": [ | |
| "import numpy as np\n", | |
| "\n", | |
| "max_row_n = total_n_rows - input_minutes_window - output_minutes_window\n", | |
| "\n", | |
| "training_pairs = []\n", | |
| "considered_rnd_minutes = set([])\n", | |
| "\n", | |
| "for n_exp in range(0, total_n_examples):\n", | |
| "\n", | |
| " if n_exp % 100 == 0:\n", | |
| " print(\"{0} examples collected so far.\".format(n_exp))\n", | |
| " \n", | |
| " found_example_where_pump_worked_in_input_window = False\n", | |
| " while not found_example_where_pump_worked_in_input_window:\n", | |
| "\n", | |
| " rnd_minute = np.random.randint(0,max_row_n)\n", | |
| "\n", | |
| " for rnd_minute in considered_rnd_minutes:\n", | |
| " rnd_minute = np.random.randint(0,max_row_n)\n", | |
| " \n", | |
| " considered_rnd_minutes.add(rnd_minute)\n", | |
| " start = rnd_minute\n", | |
| " stop = start + input_minutes_window\n", | |
| " \n", | |
| " \"\"\"\n", | |
| " Assure that the pump had worked for given input window,\n", | |
| " becuase it is comparitively easy to predict wheter the pump would fail \n", | |
| " for the given output window if it has already been failed already.\n", | |
| " \"\"\"\n", | |
| " if check_for_pump_failure(start,stop)==0:\n", | |
| " found_example_where_pump_worked_in_input_window = True\n", | |
| " \n", | |
| " single_input_window = scaled_sensor_data[rnd_minute:rnd_minute + input_minutes_window]\n", | |
| " single_input_vector = single_input_window.flatten()\n", | |
| " \n", | |
| " start = rnd_minute + input_minutes_window\n", | |
| " stop = rnd_minute + input_minutes_window + output_minutes_window\n", | |
| " output_value = check_for_pump_failure(start,stop)\n", | |
| " \n", | |
| " training_pairs.append( (single_input_vector, output_value) )" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "0 examples collected so far.\n", | |
| "100 examples collected so far.\n", | |
| "200 examples collected so far.\n", | |
| "300 examples collected so far.\n", | |
| "400 examples collected so far.\n", | |
| "500 examples collected so far.\n", | |
| "600 examples collected so far.\n", | |
| "700 examples collected so far.\n", | |
| "800 examples collected so far.\n", | |
| "900 examples collected so far.\n", | |
| "1000 examples collected so far.\n", | |
| "1100 examples collected so far.\n", | |
| "1200 examples collected so far.\n", | |
| "1300 examples collected so far.\n", | |
| "1400 examples collected so far.\n", | |
| "1500 examples collected so far.\n", | |
| "1600 examples collected so far.\n", | |
| "1700 examples collected so far.\n", | |
| "1800 examples collected so far.\n", | |
| "1900 examples collected so far.\n", | |
| "2000 examples collected so far.\n", | |
| "2100 examples collected so far.\n", | |
| "2200 examples collected so far.\n", | |
| "2300 examples collected so far.\n", | |
| "2400 examples collected so far.\n", | |
| "2500 examples collected so far.\n", | |
| "2600 examples collected so far.\n", | |
| "2700 examples collected so far.\n", | |
| "2800 examples collected so far.\n", | |
| "2900 examples collected so far.\n", | |
| "3000 examples collected so far.\n", | |
| "3100 examples collected so far.\n", | |
| "3200 examples collected so far.\n", | |
| "3300 examples collected so far.\n", | |
| "3400 examples collected so far.\n", | |
| "3500 examples collected so far.\n", | |
| "3600 examples collected so far.\n", | |
| "3700 examples collected so far.\n", | |
| "3800 examples collected so far.\n", | |
| "3900 examples collected so far.\n", | |
| "4000 examples collected so far.\n", | |
| "4100 examples collected so far.\n", | |
| "4200 examples collected so far.\n", | |
| "4300 examples collected so far.\n", | |
| "4400 examples collected so far.\n", | |
| "4500 examples collected so far.\n", | |
| "4600 examples collected so far.\n", | |
| "4700 examples collected so far.\n", | |
| "4800 examples collected so far.\n", | |
| "4900 examples collected so far.\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "fL5wQEJePr98", | |
| "outputId": "6f9f0afa-428f-42f8-ff00-139d1d49541e" | |
| }, | |
| "source": [ | |
| "len(training_pairs)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "5000" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 29 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "IQXZ3oPYPvVB", | |
| "outputId": "3c446357-5fa8-4277-d92e-94c24ee4d978" | |
| }, | |
| "source": [ | |
| "input_vec_len = training_pairs[0][0].shape[0]\n", | |
| "output_vec_len = 1\n", | |
| "\n", | |
| "Temp = np.zeros( (total_n_examples, input_vec_len + output_vec_len))\n", | |
| "print(\"Shape of Temp is\", Temp.shape)\n", | |
| "\n", | |
| "for nr in range(0, total_n_examples):\n", | |
| " (x,y) = training_pairs[nr]\n", | |
| " Temp[nr,0:input_vec_len] = x\n", | |
| " Temp[nr,input_vec_len] = y" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Shape of Temp is (5000, 3061)\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "Xu3Amh43R1BG" | |
| }, | |
| "source": [ | |
| "from sklearn.model_selection import train_test_split\n", | |
| "\n", | |
| "X = Temp[0:, 0:input_vec_len]\n", | |
| "y = Temp[0:, input_vec_len]\n", | |
| "\n", | |
| "x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=1, test_size=0.3)\n", | |
| "x_test, x_val, y_test, y_val = train_test_split(x_test, y_test, random_state=1, test_size=0.5)" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "r3Rie0gUR7nX", | |
| "outputId": "fbe7511a-c3f3-42ef-e8be-722f87656c42" | |
| }, | |
| "source": [ | |
| "x_train.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(3500, 3060)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 32 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "9A7-SiI5R-qP", | |
| "outputId": "a1efcea3-37f2-403e-bc08-e0c85d078642" | |
| }, | |
| "source": [ | |
| "y_train.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(3500,)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 33 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "LHDqyC2eSGuI", | |
| "outputId": "88ddaa04-eede-428d-bfbb-d8e031c05af6" | |
| }, | |
| "source": [ | |
| "x_test.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(750, 3060)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 34 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "D15O8XZ8SH3X", | |
| "outputId": "09277d1a-2844-4ecb-ceae-82a59bd4350d" | |
| }, | |
| "source": [ | |
| "y_test.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(750,)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 35 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bkN4FymEdxU5", | |
| "outputId": "d96dbe1e-c629-48c0-ce0d-69c0df87d39b" | |
| }, | |
| "source": [ | |
| "x_val.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(750, 3060)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 36 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "fM4R8TDSd1Dz", | |
| "outputId": "c84a4760-0a79-4bd3-d953-376677ab7818" | |
| }, | |
| "source": [ | |
| "y_val.shape" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(750,)" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 37 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e4Qu6DCkoL8B" | |
| }, | |
| "source": [ | |
| "#### *Build & Train a Neural Network Model*" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 35 | |
| }, | |
| "id": "u0TU1N734syR", | |
| "outputId": "fee0306b-90d0-4c45-d2f1-e05dcbeafa88" | |
| }, | |
| "source": [ | |
| "import tensorflow\n", | |
| "tensorflow.__version__" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| }, | |
| "text/plain": [ | |
| "'2.3.0'" | |
| ] | |
| }, | |
| "metadata": { | |
| "tags": [] | |
| }, | |
| "execution_count": 38 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "RT9Hq9F_SRAJ" | |
| }, | |
| "source": [ | |
| "import tensorflow as tf\n", | |
| "from tensorflow.keras import layers\n", | |
| "from keras.callbacks import EarlyStopping\n", | |
| "from keras.callbacks import ModelCheckpoint\n", | |
| "\n", | |
| "model = tf.keras.Sequential()\n", | |
| "model.add(layers.Dense(300, activation='relu', input_shape=(input_vec_len,)) )\n", | |
| "model.add(layers.Dense(60, activation='relu'))\n", | |
| "model.add(layers.Dense(12, activation='relu'))\n", | |
| "model.add(layers.Dense(1))\n", | |
| "\n", | |
| "model.compile(optimizer='sgd', \n", | |
| " loss=tf.keras.losses.MeanSquaredError(),\n", | |
| " metrics=['accuracy'])\n", | |
| "\n", | |
| "model.build()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "T5d6cZOuSZ3m", | |
| "outputId": "7b447ec8-1c7b-4a73-8775-ccb1313d57fe" | |
| }, | |
| "source": [ | |
| "model.summary()" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Model: \"sequential\"\n", | |
| "_________________________________________________________________\n", | |
| "Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| "dense (Dense) (None, 300) 918300 \n", | |
| "_________________________________________________________________\n", | |
| "dense_1 (Dense) (None, 60) 18060 \n", | |
| "_________________________________________________________________\n", | |
| "dense_2 (Dense) (None, 12) 732 \n", | |
| "_________________________________________________________________\n", | |
| "dense_3 (Dense) (None, 1) 13 \n", | |
| "=================================================================\n", | |
| "Total params: 937,105\n", | |
| "Trainable params: 937,105\n", | |
| "Non-trainable params: 0\n", | |
| "_________________________________________________________________\n" | |
| ], | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "SjEIDWUMSb75", | |
| "outputId": "6a8d406d-8453-4bf0-c2ea-cbea2d330d55" | |
| }, | |
| "source": [ | |
| "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=500)\n", | |
| "mc = ModelCheckpoint('best_model-{epoch:03d}-{val_loss:.3f}.h5', monitor='val_loss', mode='min', save_best_only=True)\n", | |
| "history = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=3000, verbose=1, callbacks=[es, mc])" | |
| ], | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0707 - accuracy: 0.9394 - val_loss: 0.0442 - val_accuracy: 0.9533\n", | |
| "Epoch 2/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0514 - accuracy: 0.9451 - val_loss: 0.0440 - val_accuracy: 0.9533\n", | |
| "Epoch 3/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0510 - accuracy: 0.9451 - val_loss: 0.0438 - val_accuracy: 0.9533\n", | |
| "Epoch 4/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0505 - accuracy: 0.9451 - val_loss: 0.0438 - val_accuracy: 0.9533\n", | |
| "Epoch 5/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0495 - accuracy: 0.9451 - val_loss: 0.0432 - val_accuracy: 0.9533\n", | |
| "Epoch 6/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0488 - accuracy: 0.9451 - val_loss: 0.0432 - val_accuracy: 0.9533\n", | |
| "Epoch 7/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0482 - accuracy: 0.9451 - val_loss: 0.0435 - val_accuracy: 0.9533\n", | |
| "Epoch 8/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0478 - accuracy: 0.9454 - val_loss: 0.0447 - val_accuracy: 0.9533\n", | |
| "Epoch 9/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0472 - accuracy: 0.9463 - val_loss: 0.0421 - val_accuracy: 0.9533\n", | |
| "Epoch 10/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0467 - accuracy: 0.9466 - val_loss: 0.0437 - val_accuracy: 0.9547\n", | |
| "Epoch 11/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0461 - accuracy: 0.9466 - val_loss: 0.0434 - val_accuracy: 0.9547\n", | |
| "Epoch 12/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0459 - accuracy: 0.9471 - val_loss: 0.0421 - val_accuracy: 0.9547\n", | |
| "Epoch 13/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0455 - accuracy: 0.9483 - val_loss: 0.0426 - val_accuracy: 0.9547\n", | |
| "Epoch 14/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0452 - accuracy: 0.9486 - val_loss: 0.0465 - val_accuracy: 0.9547\n", | |
| "Epoch 15/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0449 - accuracy: 0.9500 - val_loss: 0.0419 - val_accuracy: 0.9533\n", | |
| "Epoch 16/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0449 - accuracy: 0.9497 - val_loss: 0.0477 - val_accuracy: 0.9533\n", | |
| "Epoch 17/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0444 - accuracy: 0.9494 - val_loss: 0.0435 - val_accuracy: 0.9547\n", | |
| "Epoch 18/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0439 - accuracy: 0.9511 - val_loss: 0.0407 - val_accuracy: 0.9547\n", | |
| "Epoch 19/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0435 - accuracy: 0.9503 - val_loss: 0.0413 - val_accuracy: 0.9547\n", | |
| "Epoch 20/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0436 - accuracy: 0.9511 - val_loss: 0.0419 - val_accuracy: 0.9533\n", | |
| "Epoch 21/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0432 - accuracy: 0.9517 - val_loss: 0.0406 - val_accuracy: 0.9533\n", | |
| "Epoch 22/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0429 - accuracy: 0.9511 - val_loss: 0.0409 - val_accuracy: 0.9533\n", | |
| "Epoch 23/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0424 - accuracy: 0.9511 - val_loss: 0.0427 - val_accuracy: 0.9533\n", | |
| "Epoch 24/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0427 - accuracy: 0.9506 - val_loss: 0.0405 - val_accuracy: 0.9547\n", | |
| "Epoch 25/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0426 - accuracy: 0.9509 - val_loss: 0.0397 - val_accuracy: 0.9547\n", | |
| "Epoch 26/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0423 - accuracy: 0.9517 - val_loss: 0.0397 - val_accuracy: 0.9547\n", | |
| "Epoch 27/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0422 - accuracy: 0.9509 - val_loss: 0.0419 - val_accuracy: 0.9547\n", | |
| "Epoch 28/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0415 - accuracy: 0.9517 - val_loss: 0.0398 - val_accuracy: 0.9533\n", | |
| "Epoch 29/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0415 - accuracy: 0.9514 - val_loss: 0.0410 - val_accuracy: 0.9533\n", | |
| "Epoch 30/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0411 - accuracy: 0.9511 - val_loss: 0.0407 - val_accuracy: 0.9547\n", | |
| "Epoch 31/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0407 - accuracy: 0.9520 - val_loss: 0.0412 - val_accuracy: 0.9547\n", | |
| "Epoch 32/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0405 - accuracy: 0.9514 - val_loss: 0.0413 - val_accuracy: 0.9547\n", | |
| "Epoch 33/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0406 - accuracy: 0.9520 - val_loss: 0.0408 - val_accuracy: 0.9547\n", | |
| "Epoch 34/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0407 - accuracy: 0.9511 - val_loss: 0.0396 - val_accuracy: 0.9533\n", | |
| "Epoch 35/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0401 - accuracy: 0.9520 - val_loss: 0.0417 - val_accuracy: 0.9547\n", | |
| "Epoch 36/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0409 - accuracy: 0.9509 - val_loss: 0.0384 - val_accuracy: 0.9547\n", | |
| "Epoch 37/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0403 - accuracy: 0.9517 - val_loss: 0.0405 - val_accuracy: 0.9547\n", | |
| "Epoch 38/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0396 - accuracy: 0.9523 - val_loss: 0.0418 - val_accuracy: 0.9547\n", | |
| "Epoch 39/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0394 - accuracy: 0.9523 - val_loss: 0.0387 - val_accuracy: 0.9533\n", | |
| "Epoch 40/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0395 - accuracy: 0.9520 - val_loss: 0.0399 - val_accuracy: 0.9547\n", | |
| "Epoch 41/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0395 - accuracy: 0.9520 - val_loss: 0.0392 - val_accuracy: 0.9533\n", | |
| "Epoch 42/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0393 - accuracy: 0.9523 - val_loss: 0.0420 - val_accuracy: 0.9533\n", | |
| "Epoch 43/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0385 - accuracy: 0.9514 - val_loss: 0.0382 - val_accuracy: 0.9547\n", | |
| "Epoch 44/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0394 - accuracy: 0.9517 - val_loss: 0.0380 - val_accuracy: 0.9547\n", | |
| "Epoch 45/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0383 - accuracy: 0.9520 - val_loss: 0.0378 - val_accuracy: 0.9547\n", | |
| "Epoch 46/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0383 - accuracy: 0.9526 - val_loss: 0.0424 - val_accuracy: 0.9533\n", | |
| "Epoch 47/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0377 - accuracy: 0.9523 - val_loss: 0.0375 - val_accuracy: 0.9533\n", | |
| "Epoch 48/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0382 - accuracy: 0.9520 - val_loss: 0.0367 - val_accuracy: 0.9547\n", | |
| "Epoch 49/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0373 - accuracy: 0.9520 - val_loss: 0.0473 - val_accuracy: 0.9547\n", | |
| "Epoch 50/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0381 - accuracy: 0.9523 - val_loss: 0.0363 - val_accuracy: 0.9547\n", | |
| "Epoch 51/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0375 - accuracy: 0.9520 - val_loss: 0.0377 - val_accuracy: 0.9547\n", | |
| "Epoch 52/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0375 - accuracy: 0.9523 - val_loss: 0.0360 - val_accuracy: 0.9547\n", | |
| "Epoch 53/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0372 - accuracy: 0.9523 - val_loss: 0.0366 - val_accuracy: 0.9547\n", | |
| "Epoch 54/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0370 - accuracy: 0.9517 - val_loss: 0.0382 - val_accuracy: 0.9547\n", | |
| "Epoch 55/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0376 - accuracy: 0.9520 - val_loss: 0.0505 - val_accuracy: 0.9547\n", | |
| "Epoch 56/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0368 - accuracy: 0.9523 - val_loss: 0.0855 - val_accuracy: 0.9213\n", | |
| "Epoch 57/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0367 - accuracy: 0.9520 - val_loss: 0.0398 - val_accuracy: 0.9547\n", | |
| "Epoch 58/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0373 - accuracy: 0.9523 - val_loss: 0.0422 - val_accuracy: 0.9560\n", | |
| "Epoch 59/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0357 - accuracy: 0.9520 - val_loss: 0.0413 - val_accuracy: 0.9547\n", | |
| "Epoch 60/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0362 - accuracy: 0.9520 - val_loss: 0.0351 - val_accuracy: 0.9547\n", | |
| "Epoch 61/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0362 - accuracy: 0.9526 - val_loss: 0.0492 - val_accuracy: 0.9547\n", | |
| "Epoch 62/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0359 - accuracy: 0.9526 - val_loss: 0.0432 - val_accuracy: 0.9547\n", | |
| "Epoch 63/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0358 - accuracy: 0.9534 - val_loss: 0.0375 - val_accuracy: 0.9547\n", | |
| "Epoch 64/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0347 - accuracy: 0.9534 - val_loss: 0.0350 - val_accuracy: 0.9547\n", | |
| "Epoch 65/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0348 - accuracy: 0.9526 - val_loss: 0.0341 - val_accuracy: 0.9547\n", | |
| "Epoch 66/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0346 - accuracy: 0.9531 - val_loss: 0.0752 - val_accuracy: 0.9533\n", | |
| "Epoch 67/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0352 - accuracy: 0.9543 - val_loss: 0.0382 - val_accuracy: 0.9560\n", | |
| "Epoch 68/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0346 - accuracy: 0.9526 - val_loss: 0.0364 - val_accuracy: 0.9560\n", | |
| "Epoch 69/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0352 - accuracy: 0.9531 - val_loss: 0.0356 - val_accuracy: 0.9547\n", | |
| "Epoch 70/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0341 - accuracy: 0.9537 - val_loss: 0.0360 - val_accuracy: 0.9547\n", | |
| "Epoch 71/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0343 - accuracy: 0.9523 - val_loss: 0.0343 - val_accuracy: 0.9547\n", | |
| "Epoch 72/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0341 - accuracy: 0.9543 - val_loss: 0.0398 - val_accuracy: 0.9547\n", | |
| "Epoch 73/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0343 - accuracy: 0.9529 - val_loss: 0.0355 - val_accuracy: 0.9547\n", | |
| "Epoch 74/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0341 - accuracy: 0.9529 - val_loss: 0.0330 - val_accuracy: 0.9547\n", | |
| "Epoch 75/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0331 - accuracy: 0.9554 - val_loss: 0.0362 - val_accuracy: 0.9587\n", | |
| "Epoch 76/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0335 - accuracy: 0.9531 - val_loss: 0.0337 - val_accuracy: 0.9547\n", | |
| "Epoch 77/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0335 - accuracy: 0.9546 - val_loss: 0.0337 - val_accuracy: 0.9547\n", | |
| "Epoch 78/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0332 - accuracy: 0.9540 - val_loss: 0.0353 - val_accuracy: 0.9573\n", | |
| "Epoch 79/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0331 - accuracy: 0.9551 - val_loss: 0.0334 - val_accuracy: 0.9547\n", | |
| "Epoch 80/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0321 - accuracy: 0.9563 - val_loss: 0.0367 - val_accuracy: 0.9613\n", | |
| "Epoch 81/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0326 - accuracy: 0.9563 - val_loss: 0.0376 - val_accuracy: 0.9547\n", | |
| "Epoch 82/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0324 - accuracy: 0.9557 - val_loss: 0.0870 - val_accuracy: 0.8507\n", | |
| "Epoch 83/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0326 - accuracy: 0.9534 - val_loss: 0.0371 - val_accuracy: 0.9547\n", | |
| "Epoch 84/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0316 - accuracy: 0.9571 - val_loss: 0.0396 - val_accuracy: 0.9667\n", | |
| "Epoch 85/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0313 - accuracy: 0.9571 - val_loss: 0.0345 - val_accuracy: 0.9547\n", | |
| "Epoch 86/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0327 - accuracy: 0.9560 - val_loss: 0.0347 - val_accuracy: 0.9560\n", | |
| "Epoch 87/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0316 - accuracy: 0.9577 - val_loss: 0.0316 - val_accuracy: 0.9560\n", | |
| "Epoch 88/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0316 - accuracy: 0.9580 - val_loss: 0.0347 - val_accuracy: 0.9547\n", | |
| "Epoch 89/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0313 - accuracy: 0.9597 - val_loss: 0.0333 - val_accuracy: 0.9573\n", | |
| "Epoch 90/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0305 - accuracy: 0.9614 - val_loss: 0.0347 - val_accuracy: 0.9547\n", | |
| "Epoch 91/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0301 - accuracy: 0.9600 - val_loss: 0.0360 - val_accuracy: 0.9573\n", | |
| "Epoch 92/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0310 - accuracy: 0.9603 - val_loss: 0.0323 - val_accuracy: 0.9600\n", | |
| "Epoch 93/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0304 - accuracy: 0.9606 - val_loss: 0.0327 - val_accuracy: 0.9547\n", | |
| "Epoch 94/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0305 - accuracy: 0.9609 - val_loss: 0.0302 - val_accuracy: 0.9573\n", | |
| "Epoch 95/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0299 - accuracy: 0.9614 - val_loss: 0.0318 - val_accuracy: 0.9547\n", | |
| "Epoch 96/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0300 - accuracy: 0.9600 - val_loss: 0.0319 - val_accuracy: 0.9547\n", | |
| "Epoch 97/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0307 - accuracy: 0.9606 - val_loss: 0.0330 - val_accuracy: 0.9613\n", | |
| "Epoch 98/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0308 - accuracy: 0.9614 - val_loss: 0.0300 - val_accuracy: 0.9587\n", | |
| "Epoch 99/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0297 - accuracy: 0.9634 - val_loss: 0.0304 - val_accuracy: 0.9587\n", | |
| "Epoch 100/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0286 - accuracy: 0.9629 - val_loss: 0.0308 - val_accuracy: 0.9573\n", | |
| "Epoch 101/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0303 - accuracy: 0.9631 - val_loss: 0.0329 - val_accuracy: 0.9547\n", | |
| "Epoch 102/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0294 - accuracy: 0.9617 - val_loss: 0.0290 - val_accuracy: 0.9613\n", | |
| "Epoch 103/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0282 - accuracy: 0.9654 - val_loss: 0.0296 - val_accuracy: 0.9600\n", | |
| "Epoch 104/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0290 - accuracy: 0.9666 - val_loss: 0.0323 - val_accuracy: 0.9547\n", | |
| "Epoch 105/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0291 - accuracy: 0.9631 - val_loss: 0.0301 - val_accuracy: 0.9573\n", | |
| "Epoch 106/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0286 - accuracy: 0.9637 - val_loss: 0.0375 - val_accuracy: 0.9573\n", | |
| "Epoch 107/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0293 - accuracy: 0.9634 - val_loss: 0.0281 - val_accuracy: 0.9640\n", | |
| "Epoch 108/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0278 - accuracy: 0.9640 - val_loss: 0.0299 - val_accuracy: 0.9720\n", | |
| "Epoch 109/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0273 - accuracy: 0.9671 - val_loss: 0.0299 - val_accuracy: 0.9587\n", | |
| "Epoch 110/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0275 - accuracy: 0.9666 - val_loss: 0.0274 - val_accuracy: 0.9680\n", | |
| "Epoch 111/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0273 - accuracy: 0.9694 - val_loss: 0.0386 - val_accuracy: 0.9600\n", | |
| "Epoch 112/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0279 - accuracy: 0.9660 - val_loss: 0.0306 - val_accuracy: 0.9600\n", | |
| "Epoch 113/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0272 - accuracy: 0.9686 - val_loss: 0.0283 - val_accuracy: 0.9693\n", | |
| "Epoch 114/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0271 - accuracy: 0.9680 - val_loss: 0.0289 - val_accuracy: 0.9720\n", | |
| "Epoch 115/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0275 - accuracy: 0.9674 - val_loss: 0.0282 - val_accuracy: 0.9720\n", | |
| "Epoch 116/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0260 - accuracy: 0.9717 - val_loss: 0.0308 - val_accuracy: 0.9560\n", | |
| "Epoch 117/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0265 - accuracy: 0.9677 - val_loss: 0.0295 - val_accuracy: 0.9560\n", | |
| "Epoch 118/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0253 - accuracy: 0.9697 - val_loss: 0.0457 - val_accuracy: 0.9547\n", | |
| "Epoch 119/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0267 - accuracy: 0.9686 - val_loss: 0.0351 - val_accuracy: 0.9720\n", | |
| "Epoch 120/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0257 - accuracy: 0.9700 - val_loss: 0.0258 - val_accuracy: 0.9627\n", | |
| "Epoch 121/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0251 - accuracy: 0.9700 - val_loss: 0.0292 - val_accuracy: 0.9573\n", | |
| "Epoch 122/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0253 - accuracy: 0.9711 - val_loss: 0.0445 - val_accuracy: 0.9547\n", | |
| "Epoch 123/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0253 - accuracy: 0.9709 - val_loss: 0.0283 - val_accuracy: 0.9600\n", | |
| "Epoch 124/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0252 - accuracy: 0.9677 - val_loss: 0.0302 - val_accuracy: 0.9707\n", | |
| "Epoch 125/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0242 - accuracy: 0.9737 - val_loss: 0.0282 - val_accuracy: 0.9600\n", | |
| "Epoch 126/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0250 - accuracy: 0.9700 - val_loss: 0.0346 - val_accuracy: 0.9720\n", | |
| "Epoch 127/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0245 - accuracy: 0.9734 - val_loss: 0.0279 - val_accuracy: 0.9587\n", | |
| "Epoch 128/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0242 - accuracy: 0.9723 - val_loss: 0.0317 - val_accuracy: 0.9693\n", | |
| "Epoch 129/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0238 - accuracy: 0.9737 - val_loss: 0.0286 - val_accuracy: 0.9573\n", | |
| "Epoch 130/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0242 - accuracy: 0.9731 - val_loss: 0.0249 - val_accuracy: 0.9627\n", | |
| "Epoch 131/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0230 - accuracy: 0.9751 - val_loss: 0.0271 - val_accuracy: 0.9733\n", | |
| "Epoch 132/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0234 - accuracy: 0.9740 - val_loss: 0.0265 - val_accuracy: 0.9667\n", | |
| "Epoch 133/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0246 - accuracy: 0.9734 - val_loss: 0.0258 - val_accuracy: 0.9733\n", | |
| "Epoch 134/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0237 - accuracy: 0.9729 - val_loss: 0.0381 - val_accuracy: 0.9520\n", | |
| "Epoch 135/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0240 - accuracy: 0.9717 - val_loss: 0.0239 - val_accuracy: 0.9720\n", | |
| "Epoch 136/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0238 - accuracy: 0.9749 - val_loss: 0.0529 - val_accuracy: 0.9507\n", | |
| "Epoch 137/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0252 - accuracy: 0.9694 - val_loss: 0.0310 - val_accuracy: 0.9587\n", | |
| "Epoch 138/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0245 - accuracy: 0.9714 - val_loss: 0.0265 - val_accuracy: 0.9720\n", | |
| "Epoch 139/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0224 - accuracy: 0.9757 - val_loss: 0.0257 - val_accuracy: 0.9707\n", | |
| "Epoch 140/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0225 - accuracy: 0.9749 - val_loss: 0.0232 - val_accuracy: 0.9760\n", | |
| "Epoch 141/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0226 - accuracy: 0.9740 - val_loss: 0.0238 - val_accuracy: 0.9667\n", | |
| "Epoch 142/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0229 - accuracy: 0.9737 - val_loss: 0.0257 - val_accuracy: 0.9693\n", | |
| "Epoch 143/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0235 - accuracy: 0.9731 - val_loss: 0.0233 - val_accuracy: 0.9747\n", | |
| "Epoch 144/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0220 - accuracy: 0.9751 - val_loss: 0.0443 - val_accuracy: 0.9333\n", | |
| "Epoch 145/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0235 - accuracy: 0.9723 - val_loss: 0.0256 - val_accuracy: 0.9640\n", | |
| "Epoch 146/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0223 - accuracy: 0.9749 - val_loss: 0.0268 - val_accuracy: 0.9733\n", | |
| "Epoch 147/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0212 - accuracy: 0.9757 - val_loss: 0.0260 - val_accuracy: 0.9627\n", | |
| "Epoch 148/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0207 - accuracy: 0.9780 - val_loss: 0.0240 - val_accuracy: 0.9720\n", | |
| "Epoch 149/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0208 - accuracy: 0.9760 - val_loss: 0.0236 - val_accuracy: 0.9667\n", | |
| "Epoch 150/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0228 - accuracy: 0.9714 - val_loss: 0.0656 - val_accuracy: 0.9107\n", | |
| "Epoch 151/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0220 - accuracy: 0.9749 - val_loss: 0.0238 - val_accuracy: 0.9720\n", | |
| "Epoch 152/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0207 - accuracy: 0.9763 - val_loss: 0.0224 - val_accuracy: 0.9760\n", | |
| "Epoch 153/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0208 - accuracy: 0.9763 - val_loss: 0.0295 - val_accuracy: 0.9707\n", | |
| "Epoch 154/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0203 - accuracy: 0.9786 - val_loss: 0.0315 - val_accuracy: 0.9600\n", | |
| "Epoch 155/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0212 - accuracy: 0.9763 - val_loss: 0.0218 - val_accuracy: 0.9747\n", | |
| "Epoch 156/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0198 - accuracy: 0.9791 - val_loss: 0.0229 - val_accuracy: 0.9733\n", | |
| "Epoch 157/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0206 - accuracy: 0.9751 - val_loss: 0.0233 - val_accuracy: 0.9720\n", | |
| "Epoch 158/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0206 - accuracy: 0.9774 - val_loss: 0.0222 - val_accuracy: 0.9760\n", | |
| "Epoch 159/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0201 - accuracy: 0.9786 - val_loss: 0.0241 - val_accuracy: 0.9693\n", | |
| "Epoch 160/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0200 - accuracy: 0.9774 - val_loss: 0.0281 - val_accuracy: 0.9733\n", | |
| "Epoch 161/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0209 - accuracy: 0.9766 - val_loss: 0.0229 - val_accuracy: 0.9707\n", | |
| "Epoch 162/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0193 - accuracy: 0.9769 - val_loss: 0.0642 - val_accuracy: 0.9000\n", | |
| "Epoch 163/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0211 - accuracy: 0.9760 - val_loss: 0.0312 - val_accuracy: 0.9667\n", | |
| "Epoch 164/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0194 - accuracy: 0.9789 - val_loss: 0.0226 - val_accuracy: 0.9693\n", | |
| "Epoch 165/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0190 - accuracy: 0.9786 - val_loss: 0.0292 - val_accuracy: 0.9640\n", | |
| "Epoch 166/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0186 - accuracy: 0.9811 - val_loss: 0.0249 - val_accuracy: 0.9640\n", | |
| "Epoch 167/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0183 - accuracy: 0.9797 - val_loss: 0.0220 - val_accuracy: 0.9760\n", | |
| "Epoch 168/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0184 - accuracy: 0.9794 - val_loss: 0.0416 - val_accuracy: 0.9560\n", | |
| "Epoch 169/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0199 - accuracy: 0.9789 - val_loss: 0.0206 - val_accuracy: 0.9773\n", | |
| "Epoch 170/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0185 - accuracy: 0.9791 - val_loss: 0.0226 - val_accuracy: 0.9720\n", | |
| "Epoch 171/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0193 - accuracy: 0.9794 - val_loss: 0.0223 - val_accuracy: 0.9653\n", | |
| "Epoch 172/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0205 - accuracy: 0.9760 - val_loss: 0.0214 - val_accuracy: 0.9760\n", | |
| "Epoch 173/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0189 - accuracy: 0.9794 - val_loss: 0.0539 - val_accuracy: 0.9227\n", | |
| "Epoch 174/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0180 - accuracy: 0.9791 - val_loss: 0.0238 - val_accuracy: 0.9733\n", | |
| "Epoch 175/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0183 - accuracy: 0.9791 - val_loss: 0.0201 - val_accuracy: 0.9813\n", | |
| "Epoch 176/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0188 - accuracy: 0.9786 - val_loss: 0.0220 - val_accuracy: 0.9747\n", | |
| "Epoch 177/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0185 - accuracy: 0.9789 - val_loss: 0.0231 - val_accuracy: 0.9680\n", | |
| "Epoch 178/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0177 - accuracy: 0.9797 - val_loss: 0.0360 - val_accuracy: 0.9613\n", | |
| "Epoch 179/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0183 - accuracy: 0.9809 - val_loss: 0.0234 - val_accuracy: 0.9680\n", | |
| "Epoch 180/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0173 - accuracy: 0.9803 - val_loss: 0.0199 - val_accuracy: 0.9760\n", | |
| "Epoch 181/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0181 - accuracy: 0.9783 - val_loss: 0.0218 - val_accuracy: 0.9733\n", | |
| "Epoch 182/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0165 - accuracy: 0.9826 - val_loss: 0.0220 - val_accuracy: 0.9720\n", | |
| "Epoch 183/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0185 - accuracy: 0.9789 - val_loss: 0.0200 - val_accuracy: 0.9773\n", | |
| "Epoch 184/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0185 - accuracy: 0.9791 - val_loss: 0.0215 - val_accuracy: 0.9773\n", | |
| "Epoch 185/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0168 - accuracy: 0.9806 - val_loss: 0.0311 - val_accuracy: 0.9653\n", | |
| "Epoch 186/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0167 - accuracy: 0.9820 - val_loss: 0.0552 - val_accuracy: 0.9173\n", | |
| "Epoch 187/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0173 - accuracy: 0.9823 - val_loss: 0.0182 - val_accuracy: 0.9827\n", | |
| "Epoch 188/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0166 - accuracy: 0.9834 - val_loss: 0.0261 - val_accuracy: 0.9653\n", | |
| "Epoch 189/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0172 - accuracy: 0.9797 - val_loss: 0.0200 - val_accuracy: 0.9773\n", | |
| "Epoch 190/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0171 - accuracy: 0.9800 - val_loss: 0.0203 - val_accuracy: 0.9760\n", | |
| "Epoch 191/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0173 - accuracy: 0.9800 - val_loss: 0.0243 - val_accuracy: 0.9707\n", | |
| "Epoch 192/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0166 - accuracy: 0.9817 - val_loss: 0.0201 - val_accuracy: 0.9813\n", | |
| "Epoch 193/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0158 - accuracy: 0.9831 - val_loss: 0.0185 - val_accuracy: 0.9800\n", | |
| "Epoch 194/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0163 - accuracy: 0.9809 - val_loss: 0.0203 - val_accuracy: 0.9800\n", | |
| "Epoch 195/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0165 - accuracy: 0.9806 - val_loss: 0.0222 - val_accuracy: 0.9773\n", | |
| "Epoch 196/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0158 - accuracy: 0.9817 - val_loss: 0.0208 - val_accuracy: 0.9773\n", | |
| "Epoch 197/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0156 - accuracy: 0.9823 - val_loss: 0.0267 - val_accuracy: 0.9627\n", | |
| "Epoch 198/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0153 - accuracy: 0.9837 - val_loss: 0.0213 - val_accuracy: 0.9733\n", | |
| "Epoch 199/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0173 - accuracy: 0.9811 - val_loss: 0.0233 - val_accuracy: 0.9667\n", | |
| "Epoch 200/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0158 - accuracy: 0.9823 - val_loss: 0.0180 - val_accuracy: 0.9813\n", | |
| "Epoch 201/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0163 - accuracy: 0.9817 - val_loss: 0.0182 - val_accuracy: 0.9813\n", | |
| "Epoch 202/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0154 - accuracy: 0.9834 - val_loss: 0.0252 - val_accuracy: 0.9627\n", | |
| "Epoch 203/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0155 - accuracy: 0.9826 - val_loss: 0.0179 - val_accuracy: 0.9813\n", | |
| "Epoch 204/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0150 - accuracy: 0.9849 - val_loss: 0.0210 - val_accuracy: 0.9773\n", | |
| "Epoch 205/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0160 - accuracy: 0.9817 - val_loss: 0.0171 - val_accuracy: 0.9827\n", | |
| "Epoch 206/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0147 - accuracy: 0.9840 - val_loss: 0.0219 - val_accuracy: 0.9720\n", | |
| "Epoch 207/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0154 - accuracy: 0.9826 - val_loss: 0.0167 - val_accuracy: 0.9813\n", | |
| "Epoch 208/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0159 - accuracy: 0.9823 - val_loss: 0.0316 - val_accuracy: 0.9560\n", | |
| "Epoch 209/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0165 - accuracy: 0.9826 - val_loss: 0.0603 - val_accuracy: 0.9253\n", | |
| "Epoch 210/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0155 - accuracy: 0.9834 - val_loss: 0.0186 - val_accuracy: 0.9733\n", | |
| "Epoch 211/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 0.0149 - accuracy: 0.9831 - val_loss: 0.0205 - val_accuracy: 0.9760\n", | |
| "Epoch 212/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 0.0156 - accuracy: 0.9834 - val_loss: 0.0170 - val_accuracy: 0.9827\n", | |
| "Epoch 213/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 0.0151 - accuracy: 0.9849 - val_loss: 0.0215 - val_accuracy: 0.9760\n", | |
| "Epoch 214/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 0.0153 - accuracy: 0.9849 - val_loss: 0.0215 - val_accuracy: 0.9760\n", | |
| "Epoch 215/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 0.0140 - accuracy: 0.9849 - val_loss: 0.0219 - val_accuracy: 0.9760\n", | |
| "Epoch 216/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 0.0147 - accuracy: 0.9829 - val_loss: 0.0186 - val_accuracy: 0.9773\n", | |
| "Epoch 217/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0148 - accuracy: 0.9840 - val_loss: 0.0340 - val_accuracy: 0.9680\n", | |
| "Epoch 218/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0142 - accuracy: 0.9851 - val_loss: 0.0170 - val_accuracy: 0.9813\n", | |
| "Epoch 219/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0145 - accuracy: 0.9834 - val_loss: 0.0217 - val_accuracy: 0.9733\n", | |
| "Epoch 220/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0144 - accuracy: 0.9846 - val_loss: 0.0177 - val_accuracy: 0.9800\n", | |
| "Epoch 221/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0136 - accuracy: 0.9857 - val_loss: 0.0191 - val_accuracy: 0.9760\n", | |
| "Epoch 222/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0139 - accuracy: 0.9834 - val_loss: 0.0213 - val_accuracy: 0.9760\n", | |
| "Epoch 223/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0152 - accuracy: 0.9826 - val_loss: 0.0173 - val_accuracy: 0.9827\n", | |
| "Epoch 224/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0145 - accuracy: 0.9826 - val_loss: 0.0185 - val_accuracy: 0.9760\n", | |
| "Epoch 225/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0139 - accuracy: 0.9831 - val_loss: 0.0223 - val_accuracy: 0.9733\n", | |
| "Epoch 226/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0143 - accuracy: 0.9840 - val_loss: 0.0175 - val_accuracy: 0.9787\n", | |
| "Epoch 227/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0151 - accuracy: 0.9811 - val_loss: 0.0169 - val_accuracy: 0.9800\n", | |
| "Epoch 228/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0138 - accuracy: 0.9849 - val_loss: 0.0197 - val_accuracy: 0.9773\n", | |
| "Epoch 229/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0131 - accuracy: 0.9854 - val_loss: 0.0327 - val_accuracy: 0.9573\n", | |
| "Epoch 230/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0138 - accuracy: 0.9854 - val_loss: 0.0334 - val_accuracy: 0.9640\n", | |
| "Epoch 231/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0146 - accuracy: 0.9829 - val_loss: 0.0182 - val_accuracy: 0.9760\n", | |
| "Epoch 232/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0138 - accuracy: 0.9849 - val_loss: 0.0394 - val_accuracy: 0.9560\n", | |
| "Epoch 233/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0146 - accuracy: 0.9823 - val_loss: 0.0291 - val_accuracy: 0.9640\n", | |
| "Epoch 234/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0142 - accuracy: 0.9829 - val_loss: 0.0216 - val_accuracy: 0.9800\n", | |
| "Epoch 235/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0134 - accuracy: 0.9854 - val_loss: 0.0175 - val_accuracy: 0.9800\n", | |
| "Epoch 236/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0132 - accuracy: 0.9863 - val_loss: 0.0153 - val_accuracy: 0.9827\n", | |
| "Epoch 237/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0134 - accuracy: 0.9857 - val_loss: 0.0178 - val_accuracy: 0.9787\n", | |
| "Epoch 238/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0138 - accuracy: 0.9843 - val_loss: 0.0155 - val_accuracy: 0.9800\n", | |
| "Epoch 239/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0130 - accuracy: 0.9851 - val_loss: 0.0165 - val_accuracy: 0.9800\n", | |
| "Epoch 240/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0130 - accuracy: 0.9854 - val_loss: 0.0153 - val_accuracy: 0.9840\n", | |
| "Epoch 241/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0130 - accuracy: 0.9854 - val_loss: 0.0220 - val_accuracy: 0.9733\n", | |
| "Epoch 242/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0125 - accuracy: 0.9854 - val_loss: 0.0166 - val_accuracy: 0.9800\n", | |
| "Epoch 243/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0134 - accuracy: 0.9863 - val_loss: 0.0179 - val_accuracy: 0.9787\n", | |
| "Epoch 244/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0133 - accuracy: 0.9854 - val_loss: 0.0204 - val_accuracy: 0.9773\n", | |
| "Epoch 245/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0135 - accuracy: 0.9849 - val_loss: 0.0161 - val_accuracy: 0.9840\n", | |
| "Epoch 246/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0132 - accuracy: 0.9869 - val_loss: 0.0188 - val_accuracy: 0.9760\n", | |
| "Epoch 247/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0136 - accuracy: 0.9834 - val_loss: 0.0191 - val_accuracy: 0.9773\n", | |
| "Epoch 248/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0126 - accuracy: 0.9854 - val_loss: 0.0147 - val_accuracy: 0.9827\n", | |
| "Epoch 249/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0118 - accuracy: 0.9863 - val_loss: 0.0182 - val_accuracy: 0.9773\n", | |
| "Epoch 250/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0133 - accuracy: 0.9854 - val_loss: 0.0241 - val_accuracy: 0.9787\n", | |
| "Epoch 251/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0130 - accuracy: 0.9840 - val_loss: 0.0199 - val_accuracy: 0.9720\n", | |
| "Epoch 252/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0126 - accuracy: 0.9857 - val_loss: 0.0183 - val_accuracy: 0.9800\n", | |
| "Epoch 253/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0119 - accuracy: 0.9863 - val_loss: 0.0422 - val_accuracy: 0.9400\n", | |
| "Epoch 254/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0132 - accuracy: 0.9840 - val_loss: 0.0158 - val_accuracy: 0.9827\n", | |
| "Epoch 255/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0123 - accuracy: 0.9840 - val_loss: 0.0174 - val_accuracy: 0.9813\n", | |
| "Epoch 256/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0121 - accuracy: 0.9874 - val_loss: 0.0143 - val_accuracy: 0.9853\n", | |
| "Epoch 257/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0122 - accuracy: 0.9866 - val_loss: 0.0150 - val_accuracy: 0.9840\n", | |
| "Epoch 258/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0118 - accuracy: 0.9874 - val_loss: 0.0163 - val_accuracy: 0.9813\n", | |
| "Epoch 259/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0117 - accuracy: 0.9854 - val_loss: 0.0153 - val_accuracy: 0.9800\n", | |
| "Epoch 260/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0125 - accuracy: 0.9854 - val_loss: 0.0161 - val_accuracy: 0.9813\n", | |
| "Epoch 261/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0114 - accuracy: 0.9880 - val_loss: 0.0170 - val_accuracy: 0.9827\n", | |
| "Epoch 262/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0109 - accuracy: 0.9863 - val_loss: 0.0157 - val_accuracy: 0.9813\n", | |
| "Epoch 263/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0119 - accuracy: 0.9877 - val_loss: 0.0166 - val_accuracy: 0.9800\n", | |
| "Epoch 264/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0128 - accuracy: 0.9863 - val_loss: 0.0151 - val_accuracy: 0.9813\n", | |
| "Epoch 265/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0124 - accuracy: 0.9849 - val_loss: 0.0166 - val_accuracy: 0.9773\n", | |
| "Epoch 266/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0121 - accuracy: 0.9869 - val_loss: 0.0186 - val_accuracy: 0.9800\n", | |
| "Epoch 267/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0112 - accuracy: 0.9880 - val_loss: 0.0156 - val_accuracy: 0.9813\n", | |
| "Epoch 268/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0111 - accuracy: 0.9869 - val_loss: 0.0144 - val_accuracy: 0.9827\n", | |
| "Epoch 269/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0116 - accuracy: 0.9857 - val_loss: 0.0154 - val_accuracy: 0.9787\n", | |
| "Epoch 270/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0123 - accuracy: 0.9854 - val_loss: 0.0205 - val_accuracy: 0.9760\n", | |
| "Epoch 271/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0113 - accuracy: 0.9880 - val_loss: 0.0201 - val_accuracy: 0.9787\n", | |
| "Epoch 272/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0126 - accuracy: 0.9854 - val_loss: 0.0165 - val_accuracy: 0.9813\n", | |
| "Epoch 273/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0115 - accuracy: 0.9866 - val_loss: 0.0208 - val_accuracy: 0.9773\n", | |
| "Epoch 274/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0128 - accuracy: 0.9857 - val_loss: 0.0183 - val_accuracy: 0.9813\n", | |
| "Epoch 275/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0124 - accuracy: 0.9854 - val_loss: 0.0136 - val_accuracy: 0.9827\n", | |
| "Epoch 276/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0119 - accuracy: 0.9846 - val_loss: 0.0133 - val_accuracy: 0.9853\n", | |
| "Epoch 277/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0109 - accuracy: 0.9889 - val_loss: 0.0145 - val_accuracy: 0.9800\n", | |
| "Epoch 278/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0108 - accuracy: 0.9871 - val_loss: 0.0206 - val_accuracy: 0.9733\n", | |
| "Epoch 279/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0112 - accuracy: 0.9897 - val_loss: 0.0166 - val_accuracy: 0.9787\n", | |
| "Epoch 280/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0116 - accuracy: 0.9869 - val_loss: 0.0158 - val_accuracy: 0.9800\n", | |
| "Epoch 281/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0107 - accuracy: 0.9877 - val_loss: 0.0205 - val_accuracy: 0.9733\n", | |
| "Epoch 282/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0128 - accuracy: 0.9849 - val_loss: 0.0145 - val_accuracy: 0.9867\n", | |
| "Epoch 283/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0110 - accuracy: 0.9877 - val_loss: 0.0143 - val_accuracy: 0.9813\n", | |
| "Epoch 284/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0105 - accuracy: 0.9883 - val_loss: 0.0181 - val_accuracy: 0.9813\n", | |
| "Epoch 285/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0104 - accuracy: 0.9883 - val_loss: 0.0171 - val_accuracy: 0.9800\n", | |
| "Epoch 286/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0109 - accuracy: 0.9883 - val_loss: 0.0201 - val_accuracy: 0.9760\n", | |
| "Epoch 287/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0098 - accuracy: 0.9894 - val_loss: 0.0151 - val_accuracy: 0.9800\n", | |
| "Epoch 288/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0109 - accuracy: 0.9877 - val_loss: 0.0143 - val_accuracy: 0.9853\n", | |
| "Epoch 289/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0103 - accuracy: 0.9889 - val_loss: 0.0147 - val_accuracy: 0.9867\n", | |
| "Epoch 290/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0103 - accuracy: 0.9869 - val_loss: 0.0153 - val_accuracy: 0.9813\n", | |
| "Epoch 291/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0108 - accuracy: 0.9880 - val_loss: 0.0141 - val_accuracy: 0.9813\n", | |
| "Epoch 292/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0105 - accuracy: 0.9891 - val_loss: 0.0145 - val_accuracy: 0.9867\n", | |
| "Epoch 293/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0102 - accuracy: 0.9883 - val_loss: 0.0134 - val_accuracy: 0.9867\n", | |
| "Epoch 294/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0098 - accuracy: 0.9897 - val_loss: 0.0134 - val_accuracy: 0.9853\n", | |
| "Epoch 295/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0102 - accuracy: 0.9880 - val_loss: 0.0146 - val_accuracy: 0.9813\n", | |
| "Epoch 296/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0113 - accuracy: 0.9897 - val_loss: 0.0134 - val_accuracy: 0.9867\n", | |
| "Epoch 297/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0105 - accuracy: 0.9883 - val_loss: 0.0127 - val_accuracy: 0.9840\n", | |
| "Epoch 298/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9894 - val_loss: 0.0190 - val_accuracy: 0.9787\n", | |
| "Epoch 299/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0124 - accuracy: 0.9866 - val_loss: 0.0288 - val_accuracy: 0.9747\n", | |
| "Epoch 300/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0098 - accuracy: 0.9889 - val_loss: 0.0131 - val_accuracy: 0.9867\n", | |
| "Epoch 301/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0106 - accuracy: 0.9894 - val_loss: 0.0137 - val_accuracy: 0.9827\n", | |
| "Epoch 302/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0105 - accuracy: 0.9880 - val_loss: 0.0132 - val_accuracy: 0.9827\n", | |
| "Epoch 303/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9894 - val_loss: 0.0127 - val_accuracy: 0.9853\n", | |
| "Epoch 304/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0103 - accuracy: 0.9877 - val_loss: 0.0143 - val_accuracy: 0.9827\n", | |
| "Epoch 305/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0100 - accuracy: 0.9886 - val_loss: 0.0163 - val_accuracy: 0.9813\n", | |
| "Epoch 306/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0110 - accuracy: 0.9869 - val_loss: 0.0471 - val_accuracy: 0.9507\n", | |
| "Epoch 307/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0105 - accuracy: 0.9869 - val_loss: 0.0135 - val_accuracy: 0.9867\n", | |
| "Epoch 308/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0103 - accuracy: 0.9894 - val_loss: 0.0178 - val_accuracy: 0.9787\n", | |
| "Epoch 309/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0113 - accuracy: 0.9886 - val_loss: 0.0198 - val_accuracy: 0.9773\n", | |
| "Epoch 310/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9886 - val_loss: 0.0132 - val_accuracy: 0.9813\n", | |
| "Epoch 311/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0094 - accuracy: 0.9903 - val_loss: 0.0465 - val_accuracy: 0.9587\n", | |
| "Epoch 312/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9877 - val_loss: 0.0312 - val_accuracy: 0.9587\n", | |
| "Epoch 313/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0123 - accuracy: 0.9866 - val_loss: 0.0286 - val_accuracy: 0.9667\n", | |
| "Epoch 314/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0103 - accuracy: 0.9889 - val_loss: 0.0321 - val_accuracy: 0.9760\n", | |
| "Epoch 315/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0101 - accuracy: 0.9894 - val_loss: 0.0151 - val_accuracy: 0.9787\n", | |
| "Epoch 316/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0114 - accuracy: 0.9863 - val_loss: 0.0140 - val_accuracy: 0.9827\n", | |
| "Epoch 317/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9906 - val_loss: 0.0130 - val_accuracy: 0.9800\n", | |
| "Epoch 318/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0096 - accuracy: 0.9880 - val_loss: 0.0198 - val_accuracy: 0.9707\n", | |
| "Epoch 319/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0091 - accuracy: 0.9909 - val_loss: 0.2142 - val_accuracy: 0.6960\n", | |
| "Epoch 320/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0145 - accuracy: 0.9866 - val_loss: 0.0168 - val_accuracy: 0.9813\n", | |
| "Epoch 321/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0100 - accuracy: 0.9894 - val_loss: 0.0157 - val_accuracy: 0.9840\n", | |
| "Epoch 322/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9891 - val_loss: 0.0192 - val_accuracy: 0.9747\n", | |
| "Epoch 323/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0091 - accuracy: 0.9900 - val_loss: 0.0412 - val_accuracy: 0.9627\n", | |
| "Epoch 324/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0096 - accuracy: 0.9909 - val_loss: 0.0128 - val_accuracy: 0.9813\n", | |
| "Epoch 325/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0086 - accuracy: 0.9906 - val_loss: 0.0159 - val_accuracy: 0.9813\n", | |
| "Epoch 326/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0094 - accuracy: 0.9889 - val_loss: 0.0136 - val_accuracy: 0.9800\n", | |
| "Epoch 327/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0088 - accuracy: 0.9909 - val_loss: 0.0199 - val_accuracy: 0.9773\n", | |
| "Epoch 328/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0093 - accuracy: 0.9894 - val_loss: 0.0165 - val_accuracy: 0.9787\n", | |
| "Epoch 329/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0103 - accuracy: 0.9877 - val_loss: 0.0212 - val_accuracy: 0.9840\n", | |
| "Epoch 330/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0084 - accuracy: 0.9926 - val_loss: 0.0129 - val_accuracy: 0.9880\n", | |
| "Epoch 331/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9894 - val_loss: 0.0122 - val_accuracy: 0.9853\n", | |
| "Epoch 332/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0087 - accuracy: 0.9917 - val_loss: 0.0134 - val_accuracy: 0.9853\n", | |
| "Epoch 333/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0092 - accuracy: 0.9903 - val_loss: 0.0150 - val_accuracy: 0.9840\n", | |
| "Epoch 334/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0082 - accuracy: 0.9923 - val_loss: 0.0138 - val_accuracy: 0.9867\n", | |
| "Epoch 335/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0082 - accuracy: 0.9923 - val_loss: 0.0246 - val_accuracy: 0.9800\n", | |
| "Epoch 336/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0095 - accuracy: 0.9911 - val_loss: 0.0125 - val_accuracy: 0.9827\n", | |
| "Epoch 337/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0087 - accuracy: 0.9917 - val_loss: 0.0117 - val_accuracy: 0.9853\n", | |
| "Epoch 338/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0094 - accuracy: 0.9891 - val_loss: 0.0159 - val_accuracy: 0.9813\n", | |
| "Epoch 339/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0082 - accuracy: 0.9909 - val_loss: 0.0166 - val_accuracy: 0.9787\n", | |
| "Epoch 340/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0096 - accuracy: 0.9900 - val_loss: 0.0155 - val_accuracy: 0.9880\n", | |
| "Epoch 341/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0098 - accuracy: 0.9883 - val_loss: 0.0121 - val_accuracy: 0.9867\n", | |
| "Epoch 342/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0089 - accuracy: 0.9909 - val_loss: 0.0159 - val_accuracy: 0.9813\n", | |
| "Epoch 343/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0086 - accuracy: 0.9911 - val_loss: 0.0125 - val_accuracy: 0.9853\n", | |
| "Epoch 344/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0087 - accuracy: 0.9917 - val_loss: 0.0128 - val_accuracy: 0.9827\n", | |
| "Epoch 345/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0085 - accuracy: 0.9917 - val_loss: 0.0225 - val_accuracy: 0.9733\n", | |
| "Epoch 346/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0090 - accuracy: 0.9903 - val_loss: 0.0132 - val_accuracy: 0.9880\n", | |
| "Epoch 347/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0080 - accuracy: 0.9926 - val_loss: 0.0132 - val_accuracy: 0.9867\n", | |
| "Epoch 348/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0088 - accuracy: 0.9900 - val_loss: 0.0117 - val_accuracy: 0.9880\n", | |
| "Epoch 349/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0081 - accuracy: 0.9923 - val_loss: 0.0122 - val_accuracy: 0.9840\n", | |
| "Epoch 350/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0102 - accuracy: 0.9880 - val_loss: 0.0150 - val_accuracy: 0.9840\n", | |
| "Epoch 351/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9914 - val_loss: 0.0111 - val_accuracy: 0.9880\n", | |
| "Epoch 352/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0086 - accuracy: 0.9900 - val_loss: 0.0153 - val_accuracy: 0.9853\n", | |
| "Epoch 353/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0075 - accuracy: 0.9920 - val_loss: 0.0280 - val_accuracy: 0.9800\n", | |
| "Epoch 354/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0091 - accuracy: 0.9914 - val_loss: 0.0111 - val_accuracy: 0.9867\n", | |
| "Epoch 355/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0083 - accuracy: 0.9920 - val_loss: 0.0125 - val_accuracy: 0.9880\n", | |
| "Epoch 356/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0084 - accuracy: 0.9920 - val_loss: 0.0126 - val_accuracy: 0.9867\n", | |
| "Epoch 357/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0080 - accuracy: 0.9906 - val_loss: 0.0487 - val_accuracy: 0.9387\n", | |
| "Epoch 358/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0091 - accuracy: 0.9903 - val_loss: 0.0108 - val_accuracy: 0.9867\n", | |
| "Epoch 359/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9906 - val_loss: 0.0125 - val_accuracy: 0.9880\n", | |
| "Epoch 360/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9926 - val_loss: 0.0107 - val_accuracy: 0.9853\n", | |
| "Epoch 361/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0086 - accuracy: 0.9911 - val_loss: 0.0254 - val_accuracy: 0.9613\n", | |
| "Epoch 362/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0092 - accuracy: 0.9909 - val_loss: 0.0219 - val_accuracy: 0.9720\n", | |
| "Epoch 363/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0082 - accuracy: 0.9917 - val_loss: 0.0134 - val_accuracy: 0.9853\n", | |
| "Epoch 364/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0081 - accuracy: 0.9914 - val_loss: 0.0219 - val_accuracy: 0.9733\n", | |
| "Epoch 365/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0077 - accuracy: 0.9923 - val_loss: 0.0511 - val_accuracy: 0.9387\n", | |
| "Epoch 366/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0100 - accuracy: 0.9897 - val_loss: 0.0272 - val_accuracy: 0.9827\n", | |
| "Epoch 367/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0097 - accuracy: 0.9891 - val_loss: 0.0124 - val_accuracy: 0.9853\n", | |
| "Epoch 368/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0076 - accuracy: 0.9929 - val_loss: 0.0380 - val_accuracy: 0.9573\n", | |
| "Epoch 369/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0091 - accuracy: 0.9917 - val_loss: 0.0145 - val_accuracy: 0.9880\n", | |
| "Epoch 370/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0086 - accuracy: 0.9900 - val_loss: 0.0125 - val_accuracy: 0.9853\n", | |
| "Epoch 371/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0078 - accuracy: 0.9917 - val_loss: 0.0107 - val_accuracy: 0.9840\n", | |
| "Epoch 372/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9949 - val_loss: 0.0265 - val_accuracy: 0.9747\n", | |
| "Epoch 373/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0082 - accuracy: 0.9923 - val_loss: 0.0136 - val_accuracy: 0.9880\n", | |
| "Epoch 374/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0085 - accuracy: 0.9900 - val_loss: 0.0141 - val_accuracy: 0.9880\n", | |
| "Epoch 375/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0090 - accuracy: 0.9909 - val_loss: 0.0157 - val_accuracy: 0.9867\n", | |
| "Epoch 376/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9920 - val_loss: 0.0110 - val_accuracy: 0.9827\n", | |
| "Epoch 377/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0077 - accuracy: 0.9917 - val_loss: 0.0102 - val_accuracy: 0.9867\n", | |
| "Epoch 378/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0079 - accuracy: 0.9917 - val_loss: 0.0516 - val_accuracy: 0.9480\n", | |
| "Epoch 379/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9926 - val_loss: 0.0378 - val_accuracy: 0.9547\n", | |
| "Epoch 380/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0073 - accuracy: 0.9923 - val_loss: 0.0373 - val_accuracy: 0.9520\n", | |
| "Epoch 381/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0095 - accuracy: 0.9903 - val_loss: 0.0129 - val_accuracy: 0.9840\n", | |
| "Epoch 382/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0086 - accuracy: 0.9926 - val_loss: 0.0109 - val_accuracy: 0.9853\n", | |
| "Epoch 383/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9917 - val_loss: 0.0413 - val_accuracy: 0.9587\n", | |
| "Epoch 384/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0084 - accuracy: 0.9914 - val_loss: 0.0109 - val_accuracy: 0.9867\n", | |
| "Epoch 385/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0067 - accuracy: 0.9943 - val_loss: 0.0135 - val_accuracy: 0.9840\n", | |
| "Epoch 386/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0073 - accuracy: 0.9926 - val_loss: 0.0142 - val_accuracy: 0.9840\n", | |
| "Epoch 387/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9937 - val_loss: 0.0112 - val_accuracy: 0.9853\n", | |
| "Epoch 388/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9920 - val_loss: 0.0150 - val_accuracy: 0.9867\n", | |
| "Epoch 389/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0063 - accuracy: 0.9931 - val_loss: 0.0110 - val_accuracy: 0.9840\n", | |
| "Epoch 390/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9917 - val_loss: 0.0153 - val_accuracy: 0.9787\n", | |
| "Epoch 391/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0076 - accuracy: 0.9926 - val_loss: 0.0103 - val_accuracy: 0.9867\n", | |
| "Epoch 392/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0069 - accuracy: 0.9926 - val_loss: 0.0117 - val_accuracy: 0.9867\n", | |
| "Epoch 393/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9934 - val_loss: 0.0101 - val_accuracy: 0.9867\n", | |
| "Epoch 394/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0082 - accuracy: 0.9909 - val_loss: 0.0105 - val_accuracy: 0.9853\n", | |
| "Epoch 395/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0072 - accuracy: 0.9923 - val_loss: 0.0157 - val_accuracy: 0.9853\n", | |
| "Epoch 396/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0076 - accuracy: 0.9917 - val_loss: 0.0113 - val_accuracy: 0.9840\n", | |
| "Epoch 397/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9940 - val_loss: 0.0324 - val_accuracy: 0.9627\n", | |
| "Epoch 398/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0067 - accuracy: 0.9931 - val_loss: 0.0169 - val_accuracy: 0.9840\n", | |
| "Epoch 399/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0069 - accuracy: 0.9931 - val_loss: 0.0174 - val_accuracy: 0.9800\n", | |
| "Epoch 400/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9923 - val_loss: 0.0105 - val_accuracy: 0.9853\n", | |
| "Epoch 401/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0077 - accuracy: 0.9923 - val_loss: 0.0124 - val_accuracy: 0.9840\n", | |
| "Epoch 402/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0080 - accuracy: 0.9920 - val_loss: 0.0396 - val_accuracy: 0.9560\n", | |
| "Epoch 403/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0080 - accuracy: 0.9914 - val_loss: 0.0161 - val_accuracy: 0.9867\n", | |
| "Epoch 404/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9931 - val_loss: 0.0107 - val_accuracy: 0.9853\n", | |
| "Epoch 405/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0072 - accuracy: 0.9934 - val_loss: 0.0147 - val_accuracy: 0.9840\n", | |
| "Epoch 406/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9931 - val_loss: 0.0093 - val_accuracy: 0.9893\n", | |
| "Epoch 407/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0072 - accuracy: 0.9934 - val_loss: 0.0142 - val_accuracy: 0.9853\n", | |
| "Epoch 408/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9949 - val_loss: 0.0127 - val_accuracy: 0.9880\n", | |
| "Epoch 409/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0072 - accuracy: 0.9929 - val_loss: 0.0104 - val_accuracy: 0.9827\n", | |
| "Epoch 410/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0074 - accuracy: 0.9934 - val_loss: 0.0105 - val_accuracy: 0.9880\n", | |
| "Epoch 411/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0073 - accuracy: 0.9926 - val_loss: 0.0164 - val_accuracy: 0.9840\n", | |
| "Epoch 412/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0062 - accuracy: 0.9937 - val_loss: 0.0103 - val_accuracy: 0.9867\n", | |
| "Epoch 413/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9909 - val_loss: 0.0107 - val_accuracy: 0.9827\n", | |
| "Epoch 414/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0067 - accuracy: 0.9934 - val_loss: 0.0095 - val_accuracy: 0.9880\n", | |
| "Epoch 415/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0067 - accuracy: 0.9940 - val_loss: 0.0103 - val_accuracy: 0.9880\n", | |
| "Epoch 416/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0063 - accuracy: 0.9943 - val_loss: 0.0389 - val_accuracy: 0.9547\n", | |
| "Epoch 417/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0068 - accuracy: 0.9920 - val_loss: 0.0122 - val_accuracy: 0.9880\n", | |
| "Epoch 418/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0069 - accuracy: 0.9929 - val_loss: 0.0287 - val_accuracy: 0.9813\n", | |
| "Epoch 419/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9937 - val_loss: 0.0119 - val_accuracy: 0.9880\n", | |
| "Epoch 420/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0063 - accuracy: 0.9943 - val_loss: 0.0106 - val_accuracy: 0.9827\n", | |
| "Epoch 421/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0063 - accuracy: 0.9937 - val_loss: 0.0090 - val_accuracy: 0.9880\n", | |
| "Epoch 422/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0064 - accuracy: 0.9940 - val_loss: 0.0206 - val_accuracy: 0.9827\n", | |
| "Epoch 423/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0067 - accuracy: 0.9929 - val_loss: 0.0109 - val_accuracy: 0.9867\n", | |
| "Epoch 424/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9934 - val_loss: 0.0127 - val_accuracy: 0.9867\n", | |
| "Epoch 425/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0069 - accuracy: 0.9911 - val_loss: 0.0202 - val_accuracy: 0.9800\n", | |
| "Epoch 426/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9934 - val_loss: 0.0091 - val_accuracy: 0.9880\n", | |
| "Epoch 427/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0082 - accuracy: 0.9911 - val_loss: 0.0159 - val_accuracy: 0.9880\n", | |
| "Epoch 428/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0067 - accuracy: 0.9920 - val_loss: 0.0123 - val_accuracy: 0.9853\n", | |
| "Epoch 429/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0064 - accuracy: 0.9946 - val_loss: 0.0111 - val_accuracy: 0.9867\n", | |
| "Epoch 430/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9923 - val_loss: 0.0093 - val_accuracy: 0.9867\n", | |
| "Epoch 431/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0065 - accuracy: 0.9926 - val_loss: 0.0140 - val_accuracy: 0.9853\n", | |
| "Epoch 432/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9934 - val_loss: 0.0162 - val_accuracy: 0.9787\n", | |
| "Epoch 433/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0074 - accuracy: 0.9929 - val_loss: 0.0188 - val_accuracy: 0.9827\n", | |
| "Epoch 434/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9929 - val_loss: 0.0184 - val_accuracy: 0.9800\n", | |
| "Epoch 435/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0069 - accuracy: 0.9934 - val_loss: 0.0124 - val_accuracy: 0.9880\n", | |
| "Epoch 436/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9951 - val_loss: 0.0104 - val_accuracy: 0.9840\n", | |
| "Epoch 437/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0062 - accuracy: 0.9966 - val_loss: 0.0088 - val_accuracy: 0.9880\n", | |
| "Epoch 438/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0064 - accuracy: 0.9931 - val_loss: 0.0151 - val_accuracy: 0.9867\n", | |
| "Epoch 439/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9926 - val_loss: 0.0126 - val_accuracy: 0.9880\n", | |
| "Epoch 440/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9946 - val_loss: 0.0098 - val_accuracy: 0.9867\n", | |
| "Epoch 441/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0064 - accuracy: 0.9937 - val_loss: 0.0136 - val_accuracy: 0.9800\n", | |
| "Epoch 442/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9946 - val_loss: 0.0095 - val_accuracy: 0.9853\n", | |
| "Epoch 443/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0066 - accuracy: 0.9929 - val_loss: 0.0114 - val_accuracy: 0.9867\n", | |
| "Epoch 444/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0062 - accuracy: 0.9940 - val_loss: 0.0101 - val_accuracy: 0.9853\n", | |
| "Epoch 445/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9946 - val_loss: 0.0089 - val_accuracy: 0.9867\n", | |
| "Epoch 446/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9923 - val_loss: 0.0091 - val_accuracy: 0.9867\n", | |
| "Epoch 447/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0067 - accuracy: 0.9943 - val_loss: 0.0103 - val_accuracy: 0.9853\n", | |
| "Epoch 448/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9934 - val_loss: 0.0142 - val_accuracy: 0.9893\n", | |
| "Epoch 449/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0066 - accuracy: 0.9934 - val_loss: 0.0090 - val_accuracy: 0.9853\n", | |
| "Epoch 450/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9954 - val_loss: 0.0098 - val_accuracy: 0.9867\n", | |
| "Epoch 451/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9951 - val_loss: 0.0141 - val_accuracy: 0.9880\n", | |
| "Epoch 452/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0058 - accuracy: 0.9946 - val_loss: 0.0133 - val_accuracy: 0.9787\n", | |
| "Epoch 453/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9929 - val_loss: 0.0269 - val_accuracy: 0.9707\n", | |
| "Epoch 454/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0116 - accuracy: 0.9897 - val_loss: 0.0142 - val_accuracy: 0.9880\n", | |
| "Epoch 455/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9906 - val_loss: 0.0210 - val_accuracy: 0.9720\n", | |
| "Epoch 456/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0105 - accuracy: 0.9903 - val_loss: 0.0139 - val_accuracy: 0.9880\n", | |
| "Epoch 457/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0108 - accuracy: 0.9914 - val_loss: 0.0185 - val_accuracy: 0.9827\n", | |
| "Epoch 458/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0092 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9907\n", | |
| "Epoch 459/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0058 - accuracy: 0.9934 - val_loss: 0.0131 - val_accuracy: 0.9880\n", | |
| "Epoch 460/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9943 - val_loss: 0.0130 - val_accuracy: 0.9827\n", | |
| "Epoch 461/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0058 - accuracy: 0.9934 - val_loss: 0.0106 - val_accuracy: 0.9840\n", | |
| "Epoch 462/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9946 - val_loss: 0.0100 - val_accuracy: 0.9880\n", | |
| "Epoch 463/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9923 - val_loss: 0.0146 - val_accuracy: 0.9880\n", | |
| "Epoch 464/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0064 - accuracy: 0.9920 - val_loss: 0.0297 - val_accuracy: 0.9813\n", | |
| "Epoch 465/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9940 - val_loss: 0.0103 - val_accuracy: 0.9840\n", | |
| "Epoch 466/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0053 - accuracy: 0.9954 - val_loss: 0.0130 - val_accuracy: 0.9827\n", | |
| "Epoch 467/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9946 - val_loss: 0.0144 - val_accuracy: 0.9880\n", | |
| "Epoch 468/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0064 - accuracy: 0.9931 - val_loss: 0.0095 - val_accuracy: 0.9867\n", | |
| "Epoch 469/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9934 - val_loss: 0.0120 - val_accuracy: 0.9880\n", | |
| "Epoch 470/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0062 - accuracy: 0.9937 - val_loss: 0.0137 - val_accuracy: 0.9880\n", | |
| "Epoch 471/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9940 - val_loss: 0.0191 - val_accuracy: 0.9827\n", | |
| "Epoch 472/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9949 - val_loss: 0.1606 - val_accuracy: 0.8147\n", | |
| "Epoch 473/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0120 - accuracy: 0.9889 - val_loss: 0.0125 - val_accuracy: 0.9827\n", | |
| "Epoch 474/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9949 - val_loss: 0.0116 - val_accuracy: 0.9880\n", | |
| "Epoch 475/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9943 - val_loss: 0.0090 - val_accuracy: 0.9893\n", | |
| "Epoch 476/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0053 - accuracy: 0.9949 - val_loss: 0.0097 - val_accuracy: 0.9867\n", | |
| "Epoch 477/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9940 - val_loss: 0.0097 - val_accuracy: 0.9867\n", | |
| "Epoch 478/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0053 - accuracy: 0.9951 - val_loss: 0.0136 - val_accuracy: 0.9880\n", | |
| "Epoch 479/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0099 - accuracy: 0.9909 - val_loss: 0.0120 - val_accuracy: 0.9880\n", | |
| "Epoch 480/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0056 - accuracy: 0.9949 - val_loss: 0.0095 - val_accuracy: 0.9880\n", | |
| "Epoch 481/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0056 - accuracy: 0.9949 - val_loss: 0.0137 - val_accuracy: 0.9867\n", | |
| "Epoch 482/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0054 - accuracy: 0.9949 - val_loss: 0.0282 - val_accuracy: 0.9653\n", | |
| "Epoch 483/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0070 - accuracy: 0.9940 - val_loss: 0.0122 - val_accuracy: 0.9853\n", | |
| "Epoch 484/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0065 - accuracy: 0.9937 - val_loss: 0.0306 - val_accuracy: 0.9640\n", | |
| "Epoch 485/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0068 - accuracy: 0.9934 - val_loss: 0.0097 - val_accuracy: 0.9867\n", | |
| "Epoch 486/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9943 - val_loss: 0.0085 - val_accuracy: 0.9880\n", | |
| "Epoch 487/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0056 - accuracy: 0.9949 - val_loss: 0.0104 - val_accuracy: 0.9867\n", | |
| "Epoch 488/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9954 - val_loss: 0.0083 - val_accuracy: 0.9880\n", | |
| "Epoch 489/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9949 - val_loss: 0.0081 - val_accuracy: 0.9880\n", | |
| "Epoch 490/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9954 - val_loss: 0.0180 - val_accuracy: 0.9840\n", | |
| "Epoch 491/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0056 - accuracy: 0.9943 - val_loss: 0.0093 - val_accuracy: 0.9880\n", | |
| "Epoch 492/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0047 - accuracy: 0.9951 - val_loss: 0.0868 - val_accuracy: 0.8880\n", | |
| "Epoch 493/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0077 - accuracy: 0.9909 - val_loss: 0.0093 - val_accuracy: 0.9893\n", | |
| "Epoch 494/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0068 - accuracy: 0.9943 - val_loss: 0.0089 - val_accuracy: 0.9853\n", | |
| "Epoch 495/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9946 - val_loss: 0.0147 - val_accuracy: 0.9880\n", | |
| "Epoch 496/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9957 - val_loss: 0.0087 - val_accuracy: 0.9920\n", | |
| "Epoch 497/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9937 - val_loss: 0.0192 - val_accuracy: 0.9827\n", | |
| "Epoch 498/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9937 - val_loss: 0.0137 - val_accuracy: 0.9880\n", | |
| "Epoch 499/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9934 - val_loss: 0.0180 - val_accuracy: 0.9853\n", | |
| "Epoch 500/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9940 - val_loss: 0.0104 - val_accuracy: 0.9880\n", | |
| "Epoch 501/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9940 - val_loss: 0.0098 - val_accuracy: 0.9813\n", | |
| "Epoch 502/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9954 - val_loss: 0.0140 - val_accuracy: 0.9867\n", | |
| "Epoch 503/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0058 - accuracy: 0.9951 - val_loss: 0.0289 - val_accuracy: 0.9693\n", | |
| "Epoch 504/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9940 - val_loss: 0.0541 - val_accuracy: 0.9253\n", | |
| "Epoch 505/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0152 - accuracy: 0.9857 - val_loss: 0.0307 - val_accuracy: 0.9627\n", | |
| "Epoch 506/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0109 - accuracy: 0.9894 - val_loss: 0.0146 - val_accuracy: 0.9840\n", | |
| "Epoch 507/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0102 - accuracy: 0.9909 - val_loss: 0.0132 - val_accuracy: 0.9867\n", | |
| "Epoch 508/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0103 - accuracy: 0.9900 - val_loss: 0.0132 - val_accuracy: 0.9867\n", | |
| "Epoch 509/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0106 - accuracy: 0.9903 - val_loss: 0.0298 - val_accuracy: 0.9667\n", | |
| "Epoch 510/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9903 - val_loss: 0.0138 - val_accuracy: 0.9880\n", | |
| "Epoch 511/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9900 - val_loss: 0.0104 - val_accuracy: 0.9880\n", | |
| "Epoch 512/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0084 - accuracy: 0.9909 - val_loss: 0.0154 - val_accuracy: 0.9880\n", | |
| "Epoch 513/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0100 - accuracy: 0.9911 - val_loss: 0.0136 - val_accuracy: 0.9880\n", | |
| "Epoch 514/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0072 - accuracy: 0.9940 - val_loss: 0.0118 - val_accuracy: 0.9867\n", | |
| "Epoch 515/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0054 - accuracy: 0.9949 - val_loss: 0.0092 - val_accuracy: 0.9867\n", | |
| "Epoch 516/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9954 - val_loss: 0.0684 - val_accuracy: 0.9133\n", | |
| "Epoch 517/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9923 - val_loss: 0.0162 - val_accuracy: 0.9800\n", | |
| "Epoch 518/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0047 - accuracy: 0.9957 - val_loss: 0.0124 - val_accuracy: 0.9827\n", | |
| "Epoch 519/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9946 - val_loss: 0.0944 - val_accuracy: 0.8760\n", | |
| "Epoch 520/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9960 - val_loss: 0.0136 - val_accuracy: 0.9880\n", | |
| "Epoch 521/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9957 - val_loss: 0.0145 - val_accuracy: 0.9880\n", | |
| "Epoch 522/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9954 - val_loss: 0.0148 - val_accuracy: 0.9880\n", | |
| "Epoch 523/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0050 - accuracy: 0.9957 - val_loss: 0.0094 - val_accuracy: 0.9813\n", | |
| "Epoch 524/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0047 - accuracy: 0.9957 - val_loss: 0.0096 - val_accuracy: 0.9867\n", | |
| "Epoch 525/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9957 - val_loss: 0.0146 - val_accuracy: 0.9827\n", | |
| "Epoch 526/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9937 - val_loss: 0.0111 - val_accuracy: 0.9880\n", | |
| "Epoch 527/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9949 - val_loss: 0.0123 - val_accuracy: 0.9787\n", | |
| "Epoch 528/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9937 - val_loss: 0.0123 - val_accuracy: 0.9853\n", | |
| "Epoch 529/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0056 - accuracy: 0.9934 - val_loss: 0.0576 - val_accuracy: 0.9307\n", | |
| "Epoch 530/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9943 - val_loss: 0.0139 - val_accuracy: 0.9853\n", | |
| "Epoch 531/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0046 - accuracy: 0.9957 - val_loss: 0.0105 - val_accuracy: 0.9893\n", | |
| "Epoch 532/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9937 - val_loss: 0.0245 - val_accuracy: 0.9693\n", | |
| "Epoch 533/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0049 - accuracy: 0.9954 - val_loss: 0.0086 - val_accuracy: 0.9853\n", | |
| "Epoch 534/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0050 - accuracy: 0.9940 - val_loss: 0.0344 - val_accuracy: 0.9667\n", | |
| "Epoch 535/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9954 - val_loss: 0.0082 - val_accuracy: 0.9920\n", | |
| "Epoch 536/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9937 - val_loss: 0.0085 - val_accuracy: 0.9920\n", | |
| "Epoch 537/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0050 - accuracy: 0.9957 - val_loss: 0.0118 - val_accuracy: 0.9840\n", | |
| "Epoch 538/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9954 - val_loss: 0.0138 - val_accuracy: 0.9853\n", | |
| "Epoch 539/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0053 - accuracy: 0.9946 - val_loss: 0.0100 - val_accuracy: 0.9880\n", | |
| "Epoch 540/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9827\n", | |
| "Epoch 541/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9951 - val_loss: 0.0122 - val_accuracy: 0.9800\n", | |
| "Epoch 542/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0062 - accuracy: 0.9943 - val_loss: 0.0120 - val_accuracy: 0.9867\n", | |
| "Epoch 543/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9954 - val_loss: 0.0081 - val_accuracy: 0.9880\n", | |
| "Epoch 544/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0049 - accuracy: 0.9951 - val_loss: 0.0112 - val_accuracy: 0.9867\n", | |
| "Epoch 545/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9951 - val_loss: 0.2177 - val_accuracy: 0.7653\n", | |
| "Epoch 546/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0143 - accuracy: 0.9880 - val_loss: 0.0140 - val_accuracy: 0.9880\n", | |
| "Epoch 547/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0109 - accuracy: 0.9906 - val_loss: 0.0140 - val_accuracy: 0.9880\n", | |
| "Epoch 548/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0104 - accuracy: 0.9914 - val_loss: 0.0134 - val_accuracy: 0.9880\n", | |
| "Epoch 549/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0104 - accuracy: 0.9900 - val_loss: 0.0135 - val_accuracy: 0.9867\n", | |
| "Epoch 550/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0103 - accuracy: 0.9906 - val_loss: 0.0130 - val_accuracy: 0.9880\n", | |
| "Epoch 551/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0102 - accuracy: 0.9909 - val_loss: 0.0133 - val_accuracy: 0.9880\n", | |
| "Epoch 552/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0098 - accuracy: 0.9911 - val_loss: 0.0132 - val_accuracy: 0.9893\n", | |
| "Epoch 553/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0099 - accuracy: 0.9911 - val_loss: 0.0135 - val_accuracy: 0.9880\n", | |
| "Epoch 554/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0100 - accuracy: 0.9909 - val_loss: 0.0136 - val_accuracy: 0.9880\n", | |
| "Epoch 555/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0098 - accuracy: 0.9917 - val_loss: 0.0132 - val_accuracy: 0.9880\n", | |
| "Epoch 556/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0101 - accuracy: 0.9911 - val_loss: 0.0132 - val_accuracy: 0.9893\n", | |
| "Epoch 557/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9909 - val_loss: 0.0132 - val_accuracy: 0.9880\n", | |
| "Epoch 558/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0094 - accuracy: 0.9917 - val_loss: 0.0128 - val_accuracy: 0.9893\n", | |
| "Epoch 559/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0102 - accuracy: 0.9906 - val_loss: 0.0130 - val_accuracy: 0.9880\n", | |
| "Epoch 560/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9911 - val_loss: 0.0142 - val_accuracy: 0.9880\n", | |
| "Epoch 561/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0098 - accuracy: 0.9914 - val_loss: 0.0358 - val_accuracy: 0.9627\n", | |
| "Epoch 562/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0106 - accuracy: 0.9900 - val_loss: 0.0132 - val_accuracy: 0.9880\n", | |
| "Epoch 563/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0095 - accuracy: 0.9911 - val_loss: 0.0132 - val_accuracy: 0.9893\n", | |
| "Epoch 564/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0095 - accuracy: 0.9914 - val_loss: 0.0131 - val_accuracy: 0.9880\n", | |
| "Epoch 565/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0096 - accuracy: 0.9911 - val_loss: 0.0138 - val_accuracy: 0.9867\n", | |
| "Epoch 566/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0066 - accuracy: 0.9934 - val_loss: 0.0110 - val_accuracy: 0.9893\n", | |
| "Epoch 567/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 0.9957 - val_loss: 0.0096 - val_accuracy: 0.9867\n", | |
| "Epoch 568/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0046 - accuracy: 0.9943 - val_loss: 0.0116 - val_accuracy: 0.9813\n", | |
| "Epoch 569/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9963 - val_loss: 0.0110 - val_accuracy: 0.9867\n", | |
| "Epoch 570/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9960 - val_loss: 0.0091 - val_accuracy: 0.9853\n", | |
| "Epoch 571/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0044 - accuracy: 0.9957 - val_loss: 0.0100 - val_accuracy: 0.9867\n", | |
| "Epoch 572/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9943 - val_loss: 0.0138 - val_accuracy: 0.9813\n", | |
| "Epoch 573/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0046 - accuracy: 0.9951 - val_loss: 0.0121 - val_accuracy: 0.9827\n", | |
| "Epoch 574/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9949 - val_loss: 0.0141 - val_accuracy: 0.9880\n", | |
| "Epoch 575/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0038 - accuracy: 0.9969 - val_loss: 0.0093 - val_accuracy: 0.9867\n", | |
| "Epoch 576/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9949 - val_loss: 0.0086 - val_accuracy: 0.9893\n", | |
| "Epoch 577/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0060 - accuracy: 0.9946 - val_loss: 0.0156 - val_accuracy: 0.9840\n", | |
| "Epoch 578/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0079 - accuracy: 0.9931 - val_loss: 0.0082 - val_accuracy: 0.9867\n", | |
| "Epoch 579/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0046 - accuracy: 0.9963 - val_loss: 0.0077 - val_accuracy: 0.9880\n", | |
| "Epoch 580/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0048 - accuracy: 0.9960 - val_loss: 0.0098 - val_accuracy: 0.9867\n", | |
| "Epoch 581/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0041 - accuracy: 0.9966 - val_loss: 0.0100 - val_accuracy: 0.9867\n", | |
| "Epoch 582/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9960 - val_loss: 0.0252 - val_accuracy: 0.9813\n", | |
| "Epoch 583/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0049 - accuracy: 0.9957 - val_loss: 0.0200 - val_accuracy: 0.9707\n", | |
| "Epoch 584/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9940 - val_loss: 0.0075 - val_accuracy: 0.9933\n", | |
| "Epoch 585/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9960 - val_loss: 0.0102 - val_accuracy: 0.9853\n", | |
| "Epoch 586/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0044 - accuracy: 0.9954 - val_loss: 0.0085 - val_accuracy: 0.9880\n", | |
| "Epoch 587/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0043 - accuracy: 0.9960 - val_loss: 0.0079 - val_accuracy: 0.9867\n", | |
| "Epoch 588/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0054 - accuracy: 0.9940 - val_loss: 0.0174 - val_accuracy: 0.9840\n", | |
| "Epoch 589/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0050 - accuracy: 0.9963 - val_loss: 0.0094 - val_accuracy: 0.9880\n", | |
| "Epoch 590/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0046 - accuracy: 0.9963 - val_loss: 0.0091 - val_accuracy: 0.9867\n", | |
| "Epoch 591/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0043 - accuracy: 0.9949 - val_loss: 0.0087 - val_accuracy: 0.9867\n", | |
| "Epoch 592/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0043 - accuracy: 0.9960 - val_loss: 0.0151 - val_accuracy: 0.9880\n", | |
| "Epoch 593/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9960 - val_loss: 0.0077 - val_accuracy: 0.9880\n", | |
| "Epoch 594/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9966 - val_loss: 0.0145 - val_accuracy: 0.9880\n", | |
| "Epoch 595/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0046 - accuracy: 0.9951 - val_loss: 0.0090 - val_accuracy: 0.9907\n", | |
| "Epoch 596/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9966 - val_loss: 0.0152 - val_accuracy: 0.9880\n", | |
| "Epoch 597/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0038 - accuracy: 0.9963 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 598/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9963 - val_loss: 0.0092 - val_accuracy: 0.9880\n", | |
| "Epoch 599/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0041 - accuracy: 0.9951 - val_loss: 0.0090 - val_accuracy: 0.9853\n", | |
| "Epoch 600/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9946 - val_loss: 0.0082 - val_accuracy: 0.9907\n", | |
| "Epoch 601/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0041 - accuracy: 0.9960 - val_loss: 0.0089 - val_accuracy: 0.9907\n", | |
| "Epoch 602/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 0.9960 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 603/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9963 - val_loss: 0.0134 - val_accuracy: 0.9827\n", | |
| "Epoch 604/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0038 - accuracy: 0.9963 - val_loss: 0.0082 - val_accuracy: 0.9920\n", | |
| "Epoch 605/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0050 - accuracy: 0.9946 - val_loss: 0.0116 - val_accuracy: 0.9880\n", | |
| "Epoch 606/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0085 - val_accuracy: 0.9880\n", | |
| "Epoch 607/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9966 - val_loss: 0.0104 - val_accuracy: 0.9867\n", | |
| "Epoch 608/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9969 - val_loss: 0.0142 - val_accuracy: 0.9880\n", | |
| "Epoch 609/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9966 - val_loss: 0.0092 - val_accuracy: 0.9853\n", | |
| "Epoch 610/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0044 - accuracy: 0.9957 - val_loss: 0.0112 - val_accuracy: 0.9867\n", | |
| "Epoch 611/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9963 - val_loss: 0.0082 - val_accuracy: 0.9893\n", | |
| "Epoch 612/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0047 - accuracy: 0.9954 - val_loss: 0.0107 - val_accuracy: 0.9867\n", | |
| "Epoch 613/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9969 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 614/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9974 - val_loss: 0.0122 - val_accuracy: 0.9880\n", | |
| "Epoch 615/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9963 - val_loss: 0.0099 - val_accuracy: 0.9867\n", | |
| "Epoch 616/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0053 - accuracy: 0.9957 - val_loss: 0.0082 - val_accuracy: 0.9880\n", | |
| "Epoch 617/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9963 - val_loss: 0.1745 - val_accuracy: 0.8200\n", | |
| "Epoch 618/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0136 - accuracy: 0.9886 - val_loss: 0.0140 - val_accuracy: 0.9880\n", | |
| "Epoch 619/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0105 - accuracy: 0.9906 - val_loss: 0.0133 - val_accuracy: 0.9880\n", | |
| "Epoch 620/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0095 - accuracy: 0.9911 - val_loss: 0.0127 - val_accuracy: 0.9880\n", | |
| "Epoch 621/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0099 - accuracy: 0.9909 - val_loss: 0.0129 - val_accuracy: 0.9893\n", | |
| "Epoch 622/3000\n", | |
| "110/110 [==============================] - 1s 5ms/step - loss: 0.0095 - accuracy: 0.9911 - val_loss: 0.0144 - val_accuracy: 0.9867\n", | |
| "Epoch 623/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9911 - val_loss: 0.0129 - val_accuracy: 0.9867\n", | |
| "Epoch 624/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0102 - accuracy: 0.9903 - val_loss: 0.0127 - val_accuracy: 0.9893\n", | |
| "Epoch 625/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0094 - accuracy: 0.9911 - val_loss: 0.0126 - val_accuracy: 0.9893\n", | |
| "Epoch 626/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9943 - val_loss: 0.0084 - val_accuracy: 0.9867\n", | |
| "Epoch 627/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9969 - val_loss: 0.0113 - val_accuracy: 0.9880\n", | |
| "Epoch 628/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0044 - accuracy: 0.9957 - val_loss: 0.0108 - val_accuracy: 0.9853\n", | |
| "Epoch 629/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0038 - accuracy: 0.9963 - val_loss: 0.1224 - val_accuracy: 0.8573\n", | |
| "Epoch 630/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0054 - accuracy: 0.9954 - val_loss: 0.0123 - val_accuracy: 0.9827\n", | |
| "Epoch 631/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9966 - val_loss: 0.0113 - val_accuracy: 0.9893\n", | |
| "Epoch 632/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9980 - val_loss: 0.0106 - val_accuracy: 0.9880\n", | |
| "Epoch 633/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9969 - val_loss: 0.0150 - val_accuracy: 0.9867\n", | |
| "Epoch 634/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9949 - val_loss: 0.0098 - val_accuracy: 0.9867\n", | |
| "Epoch 635/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9963 - val_loss: 0.0104 - val_accuracy: 0.9880\n", | |
| "Epoch 636/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9963 - val_loss: 0.0477 - val_accuracy: 0.9347\n", | |
| "Epoch 637/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0049 - accuracy: 0.9951 - val_loss: 0.0093 - val_accuracy: 0.9880\n", | |
| "Epoch 638/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9969 - val_loss: 0.0091 - val_accuracy: 0.9867\n", | |
| "Epoch 639/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9951 - val_loss: 0.0096 - val_accuracy: 0.9867\n", | |
| "Epoch 640/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0046 - accuracy: 0.9963 - val_loss: 0.0074 - val_accuracy: 0.9920\n", | |
| "Epoch 641/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9974 - val_loss: 0.0074 - val_accuracy: 0.9880\n", | |
| "Epoch 642/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9960 - val_loss: 0.0083 - val_accuracy: 0.9907\n", | |
| "Epoch 643/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9971 - val_loss: 0.0129 - val_accuracy: 0.9880\n", | |
| "Epoch 644/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0041 - accuracy: 0.9969 - val_loss: 0.0097 - val_accuracy: 0.9880\n", | |
| "Epoch 645/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0083 - val_accuracy: 0.9893\n", | |
| "Epoch 646/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9969 - val_loss: 0.0085 - val_accuracy: 0.9867\n", | |
| "Epoch 647/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9974 - val_loss: 0.0106 - val_accuracy: 0.9880\n", | |
| "Epoch 648/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9960 - val_loss: 0.0694 - val_accuracy: 0.9187\n", | |
| "Epoch 649/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0038 - accuracy: 0.9974 - val_loss: 0.0093 - val_accuracy: 0.9853\n", | |
| "Epoch 650/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9977 - val_loss: 0.0070 - val_accuracy: 0.9893\n", | |
| "Epoch 651/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9954 - val_loss: 0.0104 - val_accuracy: 0.9880\n", | |
| "Epoch 652/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9957 - val_loss: 0.0085 - val_accuracy: 0.9920\n", | |
| "Epoch 653/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9957 - val_loss: 0.0115 - val_accuracy: 0.9827\n", | |
| "Epoch 654/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9963 - val_loss: 0.0121 - val_accuracy: 0.9827\n", | |
| "Epoch 655/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0057 - accuracy: 0.9951 - val_loss: 0.0179 - val_accuracy: 0.9787\n", | |
| "Epoch 656/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0058 - accuracy: 0.9943 - val_loss: 0.0266 - val_accuracy: 0.9773\n", | |
| "Epoch 657/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9974 - val_loss: 0.0092 - val_accuracy: 0.9867\n", | |
| "Epoch 658/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0029 - accuracy: 0.9980 - val_loss: 0.0144 - val_accuracy: 0.9800\n", | |
| "Epoch 659/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9957 - val_loss: 0.0172 - val_accuracy: 0.9840\n", | |
| "Epoch 660/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0043 - accuracy: 0.9951 - val_loss: 0.0087 - val_accuracy: 0.9867\n", | |
| "Epoch 661/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9966 - val_loss: 0.0084 - val_accuracy: 0.9893\n", | |
| "Epoch 662/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9971 - val_loss: 0.0079 - val_accuracy: 0.9933\n", | |
| "Epoch 663/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 0.0034 - accuracy: 0.9969 - val_loss: 0.0552 - val_accuracy: 0.9293\n", | |
| "Epoch 664/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0033 - accuracy: 0.9963 - val_loss: 0.0099 - val_accuracy: 0.9893\n", | |
| "Epoch 665/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9969 - val_loss: 0.0070 - val_accuracy: 0.9893\n", | |
| "Epoch 666/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9957 - val_loss: 0.0139 - val_accuracy: 0.9880\n", | |
| "Epoch 667/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9974 - val_loss: 0.0147 - val_accuracy: 0.9880\n", | |
| "Epoch 668/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0080 - accuracy: 0.9920 - val_loss: 0.0135 - val_accuracy: 0.9853\n", | |
| "Epoch 669/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0096 - accuracy: 0.9906 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 670/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0041 - accuracy: 0.9966 - val_loss: 0.0111 - val_accuracy: 0.9827\n", | |
| "Epoch 671/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9977 - val_loss: 0.0088 - val_accuracy: 0.9880\n", | |
| "Epoch 672/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9974 - val_loss: 0.0099 - val_accuracy: 0.9880\n", | |
| "Epoch 673/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 0.9957 - val_loss: 0.0073 - val_accuracy: 0.9893\n", | |
| "Epoch 674/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0096 - accuracy: 0.9906 - val_loss: 0.0195 - val_accuracy: 0.9733\n", | |
| "Epoch 675/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0107 - accuracy: 0.9906 - val_loss: 0.0139 - val_accuracy: 0.9867\n", | |
| "Epoch 676/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0100 - accuracy: 0.9914 - val_loss: 0.0135 - val_accuracy: 0.9880\n", | |
| "Epoch 677/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9914 - val_loss: 0.0127 - val_accuracy: 0.9880\n", | |
| "Epoch 678/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9906 - val_loss: 0.0392 - val_accuracy: 0.9613\n", | |
| "Epoch 679/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9903 - val_loss: 0.0130 - val_accuracy: 0.9880\n", | |
| "Epoch 680/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0092 - accuracy: 0.9911 - val_loss: 0.0130 - val_accuracy: 0.9867\n", | |
| "Epoch 681/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0091 - accuracy: 0.9917 - val_loss: 0.0243 - val_accuracy: 0.9707\n", | |
| "Epoch 682/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0095 - accuracy: 0.9911 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 683/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0092 - accuracy: 0.9914 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 684/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9911 - val_loss: 0.0128 - val_accuracy: 0.9867\n", | |
| "Epoch 685/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 0.0093 - accuracy: 0.9917 - val_loss: 0.0137 - val_accuracy: 0.9880\n", | |
| "Epoch 686/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9909 - val_loss: 0.0125 - val_accuracy: 0.9893\n", | |
| "Epoch 687/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9914 - val_loss: 0.0126 - val_accuracy: 0.9867\n", | |
| "Epoch 688/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9909 - val_loss: 0.0125 - val_accuracy: 0.9880\n", | |
| "Epoch 689/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0090 - accuracy: 0.9914 - val_loss: 0.0155 - val_accuracy: 0.9880\n", | |
| "Epoch 690/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0090 - accuracy: 0.9914 - val_loss: 0.0724 - val_accuracy: 0.9173\n", | |
| "Epoch 691/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0085 - accuracy: 0.9920 - val_loss: 0.0087 - val_accuracy: 0.9880\n", | |
| "Epoch 692/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0078 - accuracy: 0.9923 - val_loss: 0.0137 - val_accuracy: 0.9880\n", | |
| "Epoch 693/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0092 - accuracy: 0.9914 - val_loss: 0.0142 - val_accuracy: 0.9893\n", | |
| "Epoch 694/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9949 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 695/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9969 - val_loss: 0.0176 - val_accuracy: 0.9787\n", | |
| "Epoch 696/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9957 - val_loss: 0.0072 - val_accuracy: 0.9893\n", | |
| "Epoch 697/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9960 - val_loss: 0.0130 - val_accuracy: 0.9880\n", | |
| "Epoch 698/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0100 - val_accuracy: 0.9867\n", | |
| "Epoch 699/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0075 - val_accuracy: 0.9947\n", | |
| "Epoch 700/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0063 - accuracy: 0.9934 - val_loss: 0.0136 - val_accuracy: 0.9880\n", | |
| "Epoch 701/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9980 - val_loss: 0.0094 - val_accuracy: 0.9840\n", | |
| "Epoch 702/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9969 - val_loss: 0.0066 - val_accuracy: 0.9933\n", | |
| "Epoch 703/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9957 - val_loss: 0.0127 - val_accuracy: 0.9813\n", | |
| "Epoch 704/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9966 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 705/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9969 - val_loss: 0.0124 - val_accuracy: 0.9880\n", | |
| "Epoch 706/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9969 - val_loss: 0.0121 - val_accuracy: 0.9853\n", | |
| "Epoch 707/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9960 - val_loss: 0.0135 - val_accuracy: 0.9880\n", | |
| "Epoch 708/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9969 - val_loss: 0.0491 - val_accuracy: 0.9347\n", | |
| "Epoch 709/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9951 - val_loss: 0.0071 - val_accuracy: 0.9920\n", | |
| "Epoch 710/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9946 - val_loss: 0.0110 - val_accuracy: 0.9880\n", | |
| "Epoch 711/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0551 - val_accuracy: 0.9267\n", | |
| "Epoch 712/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9960 - val_loss: 0.0074 - val_accuracy: 0.9920\n", | |
| "Epoch 713/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9980 - val_loss: 0.0091 - val_accuracy: 0.9867\n", | |
| "Epoch 714/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9980 - val_loss: 0.0101 - val_accuracy: 0.9867\n", | |
| "Epoch 715/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9974 - val_loss: 0.0091 - val_accuracy: 0.9867\n", | |
| "Epoch 716/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9957 - val_loss: 0.0069 - val_accuracy: 0.9893\n", | |
| "Epoch 717/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9960 - val_loss: 0.0085 - val_accuracy: 0.9867\n", | |
| "Epoch 718/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0033 - accuracy: 0.9969 - val_loss: 0.0082 - val_accuracy: 0.9880\n", | |
| "Epoch 719/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9977 - val_loss: 0.0099 - val_accuracy: 0.9867\n", | |
| "Epoch 720/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9966 - val_loss: 0.0077 - val_accuracy: 0.9893\n", | |
| "Epoch 721/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9971 - val_loss: 0.0111 - val_accuracy: 0.9853\n", | |
| "Epoch 722/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9969 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 723/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9977 - val_loss: 0.0070 - val_accuracy: 0.9947\n", | |
| "Epoch 724/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9977 - val_loss: 0.0095 - val_accuracy: 0.9880\n", | |
| "Epoch 725/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9971 - val_loss: 0.0069 - val_accuracy: 0.9933\n", | |
| "Epoch 726/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9977 - val_loss: 0.0102 - val_accuracy: 0.9827\n", | |
| "Epoch 727/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0148 - val_accuracy: 0.9840\n", | |
| "Epoch 728/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9966 - val_loss: 0.0079 - val_accuracy: 0.9880\n", | |
| "Epoch 729/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9971 - val_loss: 0.0074 - val_accuracy: 0.9880\n", | |
| "Epoch 730/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0029 - accuracy: 0.9971 - val_loss: 0.0073 - val_accuracy: 0.9933\n", | |
| "Epoch 731/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0039 - accuracy: 0.9960 - val_loss: 0.0099 - val_accuracy: 0.9853\n", | |
| "Epoch 732/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9974 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 733/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9983 - val_loss: 0.0093 - val_accuracy: 0.9880\n", | |
| "Epoch 734/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9977 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 735/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9983 - val_loss: 0.0072 - val_accuracy: 0.9920\n", | |
| "Epoch 736/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9966 - val_loss: 0.0071 - val_accuracy: 0.9933\n", | |
| "Epoch 737/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9963 - val_loss: 0.0126 - val_accuracy: 0.9880\n", | |
| "Epoch 738/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9977 - val_loss: 0.0085 - val_accuracy: 0.9893\n", | |
| "Epoch 739/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0032 - accuracy: 0.9971 - val_loss: 0.0077 - val_accuracy: 0.9880\n", | |
| "Epoch 740/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0038 - accuracy: 0.9966 - val_loss: 0.0080 - val_accuracy: 0.9907\n", | |
| "Epoch 741/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0111 - accuracy: 0.9900 - val_loss: 0.0133 - val_accuracy: 0.9880\n", | |
| "Epoch 742/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9917 - val_loss: 0.0126 - val_accuracy: 0.9893\n", | |
| "Epoch 743/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9920 - val_loss: 0.0126 - val_accuracy: 0.9880\n", | |
| "Epoch 744/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0090 - accuracy: 0.9914 - val_loss: 0.0126 - val_accuracy: 0.9880\n", | |
| "Epoch 745/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0089 - accuracy: 0.9914 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 746/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0111 - accuracy: 0.9889 - val_loss: 0.0126 - val_accuracy: 0.9880\n", | |
| "Epoch 747/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0089 - accuracy: 0.9917 - val_loss: 0.0147 - val_accuracy: 0.9853\n", | |
| "Epoch 748/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0089 - accuracy: 0.9917 - val_loss: 0.0185 - val_accuracy: 0.9813\n", | |
| "Epoch 749/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0079 - accuracy: 0.9926 - val_loss: 0.0117 - val_accuracy: 0.9867\n", | |
| "Epoch 750/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9971 - val_loss: 0.0097 - val_accuracy: 0.9933\n", | |
| "Epoch 751/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9963 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 752/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9980 - val_loss: 0.0138 - val_accuracy: 0.9827\n", | |
| "Epoch 753/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0031 - accuracy: 0.9974 - val_loss: 0.0076 - val_accuracy: 0.9880\n", | |
| "Epoch 754/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9969 - val_loss: 0.0103 - val_accuracy: 0.9880\n", | |
| "Epoch 755/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9974 - val_loss: 0.0105 - val_accuracy: 0.9893\n", | |
| "Epoch 756/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0062 - accuracy: 0.9937 - val_loss: 0.0128 - val_accuracy: 0.9893\n", | |
| "Epoch 757/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9969 - val_loss: 0.0073 - val_accuracy: 0.9933\n", | |
| "Epoch 758/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9969 - val_loss: 0.0131 - val_accuracy: 0.9893\n", | |
| "Epoch 759/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9983 - val_loss: 0.0070 - val_accuracy: 0.9920\n", | |
| "Epoch 760/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9974 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 761/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9963 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 762/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9974 - val_loss: 0.0077 - val_accuracy: 0.9933\n", | |
| "Epoch 763/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9977 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 764/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9969 - val_loss: 0.0116 - val_accuracy: 0.9853\n", | |
| "Epoch 765/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0029 - accuracy: 0.9977 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 766/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9977 - val_loss: 0.0073 - val_accuracy: 0.9893\n", | |
| "Epoch 767/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9980 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 768/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9966 - val_loss: 0.0185 - val_accuracy: 0.9760\n", | |
| "Epoch 769/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9969 - val_loss: 0.0066 - val_accuracy: 0.9933\n", | |
| "Epoch 770/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0066 - accuracy: 0.9937 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 771/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9974 - val_loss: 0.0118 - val_accuracy: 0.9880\n", | |
| "Epoch 772/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9960 - val_loss: 0.0112 - val_accuracy: 0.9840\n", | |
| "Epoch 773/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9969 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 774/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9980 - val_loss: 0.0081 - val_accuracy: 0.9880\n", | |
| "Epoch 775/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9971 - val_loss: 0.0064 - val_accuracy: 0.9933\n", | |
| "Epoch 776/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9986 - val_loss: 0.0162 - val_accuracy: 0.9800\n", | |
| "Epoch 777/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9991 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 778/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9963 - val_loss: 0.0092 - val_accuracy: 0.9880\n", | |
| "Epoch 779/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9960 - val_loss: 0.0086 - val_accuracy: 0.9880\n", | |
| "Epoch 780/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0022 - accuracy: 0.9980 - val_loss: 0.0069 - val_accuracy: 0.9933\n", | |
| "Epoch 781/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9980 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 782/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0074 - val_accuracy: 0.9947\n", | |
| "Epoch 783/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9977 - val_loss: 0.0095 - val_accuracy: 0.9880\n", | |
| "Epoch 784/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0071 - val_accuracy: 0.9893\n", | |
| "Epoch 785/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9977 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 786/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9969 - val_loss: 0.0842 - val_accuracy: 0.8787\n", | |
| "Epoch 787/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9940 - val_loss: 0.0084 - val_accuracy: 0.9880\n", | |
| "Epoch 788/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9969 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 789/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0029 - accuracy: 0.9971 - val_loss: 0.0424 - val_accuracy: 0.9493\n", | |
| "Epoch 790/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9969 - val_loss: 0.0117 - val_accuracy: 0.9867\n", | |
| "Epoch 791/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9971 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 792/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9977 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 793/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 0.9957 - val_loss: 0.0077 - val_accuracy: 0.9880\n", | |
| "Epoch 794/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9980 - val_loss: 0.0078 - val_accuracy: 0.9880\n", | |
| "Epoch 795/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9971 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 796/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9971 - val_loss: 0.0069 - val_accuracy: 0.9933\n", | |
| "Epoch 797/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9974 - val_loss: 0.0162 - val_accuracy: 0.9880\n", | |
| "Epoch 798/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0037 - accuracy: 0.9969 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 799/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9966 - val_loss: 0.0067 - val_accuracy: 0.9933\n", | |
| "Epoch 800/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9966 - val_loss: 0.0160 - val_accuracy: 0.9853\n", | |
| "Epoch 801/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9971 - val_loss: 0.0171 - val_accuracy: 0.9840\n", | |
| "Epoch 802/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9957 - val_loss: 0.0116 - val_accuracy: 0.9880\n", | |
| "Epoch 803/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0054 - accuracy: 0.9943 - val_loss: 0.0130 - val_accuracy: 0.9880\n", | |
| "Epoch 804/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0091 - accuracy: 0.9911 - val_loss: 0.0151 - val_accuracy: 0.9840\n", | |
| "Epoch 805/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9971 - val_loss: 0.0077 - val_accuracy: 0.9920\n", | |
| "Epoch 806/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0058 - accuracy: 0.9946 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 807/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0089 - accuracy: 0.9917 - val_loss: 0.0130 - val_accuracy: 0.9880\n", | |
| "Epoch 808/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0038 - accuracy: 0.9963 - val_loss: 0.0081 - val_accuracy: 0.9920\n", | |
| "Epoch 809/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9966 - val_loss: 0.0062 - val_accuracy: 0.9960\n", | |
| "Epoch 810/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0029 - accuracy: 0.9969 - val_loss: 0.0094 - val_accuracy: 0.9893\n", | |
| "Epoch 811/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9974 - val_loss: 0.0097 - val_accuracy: 0.9880\n", | |
| "Epoch 812/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0072 - val_accuracy: 0.9920\n", | |
| "Epoch 813/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0033 - accuracy: 0.9966 - val_loss: 0.0136 - val_accuracy: 0.9813\n", | |
| "Epoch 814/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9980 - val_loss: 0.0107 - val_accuracy: 0.9880\n", | |
| "Epoch 815/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9977 - val_loss: 0.0086 - val_accuracy: 0.9893\n", | |
| "Epoch 816/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9971 - val_loss: 0.0124 - val_accuracy: 0.9880\n", | |
| "Epoch 817/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0022 - accuracy: 0.9980 - val_loss: 0.0081 - val_accuracy: 0.9880\n", | |
| "Epoch 818/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0029 - accuracy: 0.9966 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 819/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9980 - val_loss: 0.0085 - val_accuracy: 0.9893\n", | |
| "Epoch 820/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9974 - val_loss: 0.0115 - val_accuracy: 0.9840\n", | |
| "Epoch 821/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0086 - val_accuracy: 0.9880\n", | |
| "Epoch 822/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0422 - val_accuracy: 0.9427\n", | |
| "Epoch 823/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9977 - val_loss: 0.0069 - val_accuracy: 0.9960\n", | |
| "Epoch 824/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9974 - val_loss: 0.0082 - val_accuracy: 0.9907\n", | |
| "Epoch 825/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9983 - val_loss: 0.0101 - val_accuracy: 0.9880\n", | |
| "Epoch 826/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9977 - val_loss: 0.0108 - val_accuracy: 0.9880\n", | |
| "Epoch 827/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0132 - val_accuracy: 0.9880\n", | |
| "Epoch 828/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0027 - accuracy: 0.9974 - val_loss: 0.0955 - val_accuracy: 0.8960\n", | |
| "Epoch 829/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0027 - accuracy: 0.9989 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 830/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9986 - val_loss: 0.0071 - val_accuracy: 0.9947\n", | |
| "Epoch 831/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9960 - val_loss: 0.0383 - val_accuracy: 0.9640\n", | |
| "Epoch 832/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0089 - accuracy: 0.9914 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 833/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9980 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 834/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9966 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 835/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9983 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 836/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9980 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 837/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9989 - val_loss: 0.0084 - val_accuracy: 0.9880\n", | |
| "Epoch 838/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 0.9951 - val_loss: 0.0126 - val_accuracy: 0.9893\n", | |
| "Epoch 839/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0032 - accuracy: 0.9974 - val_loss: 0.0080 - val_accuracy: 0.9893\n", | |
| "Epoch 840/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9974 - val_loss: 0.0095 - val_accuracy: 0.9880\n", | |
| "Epoch 841/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9977 - val_loss: 0.0069 - val_accuracy: 0.9893\n", | |
| "Epoch 842/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9977 - val_loss: 0.0078 - val_accuracy: 0.9920\n", | |
| "Epoch 843/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9977 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 844/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9980 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 845/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9980 - val_loss: 0.0084 - val_accuracy: 0.9893\n", | |
| "Epoch 846/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9980 - val_loss: 0.0076 - val_accuracy: 0.9907\n", | |
| "Epoch 847/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9980 - val_loss: 0.0070 - val_accuracy: 0.9920\n", | |
| "Epoch 848/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9986 - val_loss: 0.0064 - val_accuracy: 0.9933\n", | |
| "Epoch 849/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9974 - val_loss: 0.0090 - val_accuracy: 0.9880\n", | |
| "Epoch 850/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9966 - val_loss: 0.0093 - val_accuracy: 0.9880\n", | |
| "Epoch 851/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9980 - val_loss: 0.0068 - val_accuracy: 0.9933\n", | |
| "Epoch 852/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9977 - val_loss: 0.0062 - val_accuracy: 0.9933\n", | |
| "Epoch 853/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0022 - accuracy: 0.9989 - val_loss: 0.0483 - val_accuracy: 0.9453\n", | |
| "Epoch 854/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0024 - accuracy: 0.9974 - val_loss: 0.0084 - val_accuracy: 0.9907\n", | |
| "Epoch 855/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 0.0021 - accuracy: 0.9983 - val_loss: 0.0084 - val_accuracy: 0.9880\n", | |
| "Epoch 856/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9991 - val_loss: 0.0073 - val_accuracy: 0.9893\n", | |
| "Epoch 857/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9983 - val_loss: 0.0125 - val_accuracy: 0.9853\n", | |
| "Epoch 858/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9974 - val_loss: 0.0067 - val_accuracy: 0.9947\n", | |
| "Epoch 859/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0022 - accuracy: 0.9980 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 860/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0052 - accuracy: 0.9963 - val_loss: 0.0200 - val_accuracy: 0.9707\n", | |
| "Epoch 861/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0071 - accuracy: 0.9934 - val_loss: 0.0097 - val_accuracy: 0.9893\n", | |
| "Epoch 862/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9980 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 863/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0022 - accuracy: 0.9983 - val_loss: 0.0079 - val_accuracy: 0.9893\n", | |
| "Epoch 864/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0018 - accuracy: 0.9986 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 865/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9980 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 866/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9980 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 867/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9983 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 868/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9983 - val_loss: 0.0083 - val_accuracy: 0.9880\n", | |
| "Epoch 869/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9980 - val_loss: 0.0114 - val_accuracy: 0.9880\n", | |
| "Epoch 870/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9983 - val_loss: 0.0073 - val_accuracy: 0.9920\n", | |
| "Epoch 871/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0019 - accuracy: 0.9989 - val_loss: 0.0091 - val_accuracy: 0.9893\n", | |
| "Epoch 872/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0022 - accuracy: 0.9991 - val_loss: 0.0095 - val_accuracy: 0.9880\n", | |
| "Epoch 873/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0061 - val_accuracy: 0.9960\n", | |
| "Epoch 874/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0019 - accuracy: 0.9989 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 875/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0022 - accuracy: 0.9986 - val_loss: 0.0097 - val_accuracy: 0.9880\n", | |
| "Epoch 876/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0018 - accuracy: 0.9991 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 877/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0076 - val_accuracy: 0.9893\n", | |
| "Epoch 878/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0097 - val_accuracy: 0.9880\n", | |
| "Epoch 879/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0026 - accuracy: 0.9980 - val_loss: 0.0063 - val_accuracy: 0.9933\n", | |
| "Epoch 880/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9989 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 881/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0132 - val_accuracy: 0.9893\n", | |
| "Epoch 882/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0027 - accuracy: 0.9983 - val_loss: 0.0070 - val_accuracy: 0.9920\n", | |
| "Epoch 883/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9963 - val_loss: 0.0076 - val_accuracy: 0.9907\n", | |
| "Epoch 884/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9997 - val_loss: 0.0136 - val_accuracy: 0.9840\n", | |
| "Epoch 885/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9971 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 886/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9983 - val_loss: 0.0083 - val_accuracy: 0.9907\n", | |
| "Epoch 887/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9983 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 888/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9986 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 889/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9991 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 890/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 891/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9974 - val_loss: 0.0077 - val_accuracy: 0.9893\n", | |
| "Epoch 892/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9971 - val_loss: 0.0373 - val_accuracy: 0.9573\n", | |
| "Epoch 893/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0096 - accuracy: 0.9906 - val_loss: 0.0133 - val_accuracy: 0.9880\n", | |
| "Epoch 894/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0092 - accuracy: 0.9906 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 895/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0024 - accuracy: 0.9983 - val_loss: 0.0090 - val_accuracy: 0.9893\n", | |
| "Epoch 896/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9980 - val_loss: 0.0382 - val_accuracy: 0.9560\n", | |
| "Epoch 897/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0036 - accuracy: 0.9980 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 898/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0022 - accuracy: 0.9971 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 899/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 900/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 901/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9991 - val_loss: 0.0094 - val_accuracy: 0.9893\n", | |
| "Epoch 902/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9974 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 903/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9983 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 904/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9974 - val_loss: 0.0130 - val_accuracy: 0.9893\n", | |
| "Epoch 905/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9983 - val_loss: 0.0156 - val_accuracy: 0.9747\n", | |
| "Epoch 906/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0023 - accuracy: 0.9983 - val_loss: 0.0088 - val_accuracy: 0.9907\n", | |
| "Epoch 907/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9980 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 908/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0082 - accuracy: 0.9929 - val_loss: 0.0127 - val_accuracy: 0.9880\n", | |
| "Epoch 909/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9960 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 910/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9983 - val_loss: 0.0594 - val_accuracy: 0.9253\n", | |
| "Epoch 911/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9980 - val_loss: 0.0095 - val_accuracy: 0.9893\n", | |
| "Epoch 912/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0086 - val_accuracy: 0.9880\n", | |
| "Epoch 913/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0076 - val_accuracy: 0.9893\n", | |
| "Epoch 914/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9983 - val_loss: 0.0128 - val_accuracy: 0.9880\n", | |
| "Epoch 915/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0042 - accuracy: 0.9966 - val_loss: 0.0304 - val_accuracy: 0.9560\n", | |
| "Epoch 916/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0034 - accuracy: 0.9971 - val_loss: 0.0081 - val_accuracy: 0.9893\n", | |
| "Epoch 917/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0098 - val_accuracy: 0.9827\n", | |
| "Epoch 918/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9991 - val_loss: 0.0065 - val_accuracy: 0.9933\n", | |
| "Epoch 919/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9983 - val_loss: 0.0080 - val_accuracy: 0.9880\n", | |
| "Epoch 920/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 921/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9983 - val_loss: 0.0070 - val_accuracy: 0.9893\n", | |
| "Epoch 922/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9989 - val_loss: 0.0088 - val_accuracy: 0.9893\n", | |
| "Epoch 923/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9986 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 924/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 925/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 926/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9986 - val_loss: 0.0068 - val_accuracy: 0.9893\n", | |
| "Epoch 927/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 928/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9989 - val_loss: 0.0118 - val_accuracy: 0.9840\n", | |
| "Epoch 929/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9980 - val_loss: 0.0089 - val_accuracy: 0.9880\n", | |
| "Epoch 930/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 0.0067 - val_accuracy: 0.9907\n", | |
| "Epoch 931/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0325 - val_accuracy: 0.9560\n", | |
| "Epoch 932/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9983 - val_loss: 0.0059 - val_accuracy: 0.9947\n", | |
| "Epoch 933/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9989 - val_loss: 0.0098 - val_accuracy: 0.9907\n", | |
| "Epoch 934/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0017 - accuracy: 0.9991 - val_loss: 0.0099 - val_accuracy: 0.9893\n", | |
| "Epoch 935/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0059 - val_accuracy: 0.9960\n", | |
| "Epoch 936/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0023 - accuracy: 0.9974 - val_loss: 0.0067 - val_accuracy: 0.9947\n", | |
| "Epoch 937/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9977 - val_loss: 0.0060 - val_accuracy: 0.9947\n", | |
| "Epoch 938/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 939/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0087 - val_accuracy: 0.9907\n", | |
| "Epoch 940/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9989 - val_loss: 0.0088 - val_accuracy: 0.9907\n", | |
| "Epoch 941/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9989 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 942/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0084 - val_accuracy: 0.9907\n", | |
| "Epoch 943/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9986 - val_loss: 0.0114 - val_accuracy: 0.9867\n", | |
| "Epoch 944/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9989 - val_loss: 0.0096 - val_accuracy: 0.9853\n", | |
| "Epoch 945/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0260 - val_accuracy: 0.9667\n", | |
| "Epoch 946/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9974 - val_loss: 0.0545 - val_accuracy: 0.9347\n", | |
| "Epoch 947/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9974 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 948/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0114 - val_accuracy: 0.9880\n", | |
| "Epoch 949/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9986 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 950/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 951/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 952/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9991 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 953/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 954/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 955/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0114 - val_accuracy: 0.9827\n", | |
| "Epoch 956/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9986 - val_loss: 0.0107 - val_accuracy: 0.9880\n", | |
| "Epoch 957/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0031 - accuracy: 0.9966 - val_loss: 0.0074 - val_accuracy: 0.9933\n", | |
| "Epoch 958/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9989 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 959/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9980 - val_loss: 0.0076 - val_accuracy: 0.9907\n", | |
| "Epoch 960/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9983 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 961/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0062 - val_accuracy: 0.9907\n", | |
| "Epoch 962/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9986 - val_loss: 0.0082 - val_accuracy: 0.9893\n", | |
| "Epoch 963/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0578 - val_accuracy: 0.9320\n", | |
| "Epoch 964/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0040 - accuracy: 0.9966 - val_loss: 0.0085 - val_accuracy: 0.9893\n", | |
| "Epoch 965/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0104 - val_accuracy: 0.9867\n", | |
| "Epoch 966/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0133 - val_accuracy: 0.9880\n", | |
| "Epoch 967/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0026 - accuracy: 0.9977 - val_loss: 0.0083 - val_accuracy: 0.9907\n", | |
| "Epoch 968/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9989 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 969/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0080 - val_accuracy: 0.9907\n", | |
| "Epoch 970/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 971/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0064 - val_accuracy: 0.9933\n", | |
| "Epoch 972/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0059 - val_accuracy: 0.9947\n", | |
| "Epoch 973/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0070 - val_accuracy: 0.9893\n", | |
| "Epoch 974/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0077 - val_accuracy: 0.9947\n", | |
| "Epoch 975/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 976/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0085 - val_accuracy: 0.9893\n", | |
| "Epoch 977/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.0085 - val_accuracy: 0.9907\n", | |
| "Epoch 978/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 979/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 980/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0019 - accuracy: 0.9991 - val_loss: 0.0094 - val_accuracy: 0.9893\n", | |
| "Epoch 981/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 982/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9983 - val_loss: 0.0104 - val_accuracy: 0.9893\n", | |
| "Epoch 983/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0030 - accuracy: 0.9980 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 984/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0394 - val_accuracy: 0.9533\n", | |
| "Epoch 985/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0087 - val_accuracy: 0.9907\n", | |
| "Epoch 986/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 987/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0021 - accuracy: 0.9980 - val_loss: 0.0311 - val_accuracy: 0.9640\n", | |
| "Epoch 988/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9949 - val_loss: 0.0104 - val_accuracy: 0.9893\n", | |
| "Epoch 989/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0094 - val_accuracy: 0.9907\n", | |
| "Epoch 990/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0082 - val_accuracy: 0.9907\n", | |
| "Epoch 991/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 992/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0083 - val_accuracy: 0.9880\n", | |
| "Epoch 993/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 994/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9991 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 995/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9991 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 996/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0101 - val_accuracy: 0.9907\n", | |
| "Epoch 997/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9947\n", | |
| "Epoch 998/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9989 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 999/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1000/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0125 - val_accuracy: 0.9867\n", | |
| "Epoch 1001/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1002/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1003/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0080 - val_accuracy: 0.9893\n", | |
| "Epoch 1004/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0056 - val_accuracy: 0.9960\n", | |
| "Epoch 1005/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0085 - val_accuracy: 0.9893\n", | |
| "Epoch 1006/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9947\n", | |
| "Epoch 1007/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1008/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9989 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1009/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0085 - val_accuracy: 0.9907\n", | |
| "Epoch 1010/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0104 - val_accuracy: 0.9880\n", | |
| "Epoch 1011/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1012/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0010 - accuracy: 0.9994 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1013/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0010 - accuracy: 0.9994 - val_loss: 0.0061 - val_accuracy: 0.9947\n", | |
| "Epoch 1014/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0076 - accuracy: 0.9929 - val_loss: 0.0128 - val_accuracy: 0.9867\n", | |
| "Epoch 1015/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0055 - accuracy: 0.9946 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 1016/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 1017/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0054 - val_accuracy: 0.9960\n", | |
| "Epoch 1018/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1019/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.9991 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1020/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 1021/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1022/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9989 - val_loss: 0.0064 - val_accuracy: 0.9947\n", | |
| "Epoch 1023/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0019 - accuracy: 0.9989 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1024/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1025/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0083 - val_accuracy: 0.9893\n", | |
| "Epoch 1026/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1027/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0058 - accuracy: 0.9951 - val_loss: 0.0108 - val_accuracy: 0.9893\n", | |
| "Epoch 1028/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9986 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1029/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0119 - val_accuracy: 0.9867\n", | |
| "Epoch 1030/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0018 - accuracy: 0.9983 - val_loss: 0.0121 - val_accuracy: 0.9893\n", | |
| "Epoch 1031/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0071 - val_accuracy: 0.9893\n", | |
| "Epoch 1032/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1033/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1034/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9989 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1035/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1036/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9880\n", | |
| "Epoch 1037/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9986 - val_loss: 0.0063 - val_accuracy: 0.9907\n", | |
| "Epoch 1038/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0355 - val_accuracy: 0.9587\n", | |
| "Epoch 1039/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0083 - val_accuracy: 0.9893\n", | |
| "Epoch 1040/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.9994 - val_loss: 0.0111 - val_accuracy: 0.9880\n", | |
| "Epoch 1041/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0085 - val_accuracy: 0.9920\n", | |
| "Epoch 1042/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0076 - val_accuracy: 0.9907\n", | |
| "Epoch 1043/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1044/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9994 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1045/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9986 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1046/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0116 - val_accuracy: 0.9880\n", | |
| "Epoch 1047/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9994 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1048/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.3448e-04 - accuracy: 0.9994 - val_loss: 0.0120 - val_accuracy: 0.9853\n", | |
| "Epoch 1049/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0054 - val_accuracy: 0.9960\n", | |
| "Epoch 1050/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1051/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1052/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.3840e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1053/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9991 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1054/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9947\n", | |
| "Epoch 1055/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0142 - val_accuracy: 0.9853\n", | |
| "Epoch 1056/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0018 - accuracy: 0.9983 - val_loss: 0.0088 - val_accuracy: 0.9907\n", | |
| "Epoch 1057/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0062 - val_accuracy: 0.9947\n", | |
| "Epoch 1058/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1059/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9991 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1060/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.8702e-04 - accuracy: 0.9994 - val_loss: 0.0064 - val_accuracy: 0.9933\n", | |
| "Epoch 1061/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.3789e-04 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 1062/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9986 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1063/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0091 - val_accuracy: 0.9920\n", | |
| "Epoch 1064/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0076 - val_accuracy: 0.9893\n", | |
| "Epoch 1065/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 1066/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1067/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.2618e-04 - accuracy: 0.9997 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 1068/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0101 - val_accuracy: 0.9907\n", | |
| "Epoch 1069/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 1070/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0075 - accuracy: 0.9934 - val_loss: 0.0132 - val_accuracy: 0.9853\n", | |
| "Epoch 1071/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0085 - accuracy: 0.9917 - val_loss: 0.0133 - val_accuracy: 0.9893\n", | |
| "Epoch 1072/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0076 - accuracy: 0.9926 - val_loss: 0.0084 - val_accuracy: 0.9893\n", | |
| "Epoch 1073/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0088 - val_accuracy: 0.9907\n", | |
| "Epoch 1074/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.7156e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9960\n", | |
| "Epoch 1075/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.7491e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 1076/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.9994 - val_loss: 0.0053 - val_accuracy: 0.9960\n", | |
| "Epoch 1077/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.7297e-04 - accuracy: 0.9994 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1078/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.4460e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1079/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0091 - val_accuracy: 0.9893\n", | |
| "Epoch 1080/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9983 - val_loss: 0.0071 - val_accuracy: 0.9947\n", | |
| "Epoch 1081/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.4275e-04 - accuracy: 0.9997 - val_loss: 0.0106 - val_accuracy: 0.9907\n", | |
| "Epoch 1082/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9989 - val_loss: 0.0054 - val_accuracy: 0.9960\n", | |
| "Epoch 1083/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4196e-04 - accuracy: 0.9994 - val_loss: 0.0095 - val_accuracy: 0.9920\n", | |
| "Epoch 1084/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0085 - val_accuracy: 0.9893\n", | |
| "Epoch 1085/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0010 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1086/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.1689e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1087/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.7715e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1088/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 1089/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9991 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1090/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 1091/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.6946e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9960\n", | |
| "Epoch 1092/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0073 - val_accuracy: 0.9893\n", | |
| "Epoch 1093/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0064 - val_accuracy: 0.9947\n", | |
| "Epoch 1094/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1095/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0020 - accuracy: 0.9983 - val_loss: 0.0071 - val_accuracy: 0.9893\n", | |
| "Epoch 1096/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.2484e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1097/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.4278e-04 - accuracy: 0.9997 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1098/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.4644e-04 - accuracy: 0.9997 - val_loss: 0.0357 - val_accuracy: 0.9600\n", | |
| "Epoch 1099/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1100/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 1101/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9960\n", | |
| "Epoch 1102/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1103/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2003e-04 - accuracy: 0.9997 - val_loss: 0.0090 - val_accuracy: 0.9893\n", | |
| "Epoch 1104/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0024 - accuracy: 0.9980 - val_loss: 0.0086 - val_accuracy: 0.9893\n", | |
| "Epoch 1105/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0057 - val_accuracy: 0.9947\n", | |
| "Epoch 1106/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0014 - accuracy: 0.9994 - val_loss: 0.0103 - val_accuracy: 0.9893\n", | |
| "Epoch 1107/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0086 - val_accuracy: 0.9893\n", | |
| "Epoch 1108/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.1622e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1109/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.7992e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9933\n", | |
| "Epoch 1110/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.5604e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1111/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.5303e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1112/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0125 - val_accuracy: 0.9853\n", | |
| "Epoch 1113/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0093 - val_accuracy: 0.9893\n", | |
| "Epoch 1114/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.0652e-04 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1115/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.7834e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1116/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.4443e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1117/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4140e-04 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 0.9880\n", | |
| "Epoch 1118/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1119/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2725e-04 - accuracy: 0.9997 - val_loss: 0.0086 - val_accuracy: 0.9907\n", | |
| "Epoch 1120/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0101 - val_accuracy: 0.9907\n", | |
| "Epoch 1121/3000\n", | |
| "110/110 [==============================] - 2s 14ms/step - loss: 7.5594e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1122/3000\n", | |
| "110/110 [==============================] - 2s 18ms/step - loss: 0.0010 - accuracy: 0.9991 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1123/3000\n", | |
| "110/110 [==============================] - 2s 16ms/step - loss: 0.0010 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1124/3000\n", | |
| "110/110 [==============================] - 2s 16ms/step - loss: 0.0012 - accuracy: 0.9986 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1125/3000\n", | |
| "110/110 [==============================] - 1s 10ms/step - loss: 0.0011 - accuracy: 0.9994 - val_loss: 0.0067 - val_accuracy: 0.9907\n", | |
| "Epoch 1126/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.4263e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9907\n", | |
| "Epoch 1127/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.6603e-04 - accuracy: 0.9994 - val_loss: 0.0097 - val_accuracy: 0.9893\n", | |
| "Epoch 1128/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2480e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9933\n", | |
| "Epoch 1129/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4422e-04 - accuracy: 0.9994 - val_loss: 0.0108 - val_accuracy: 0.9853\n", | |
| "Epoch 1130/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.3541e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9933\n", | |
| "Epoch 1131/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.1282e-04 - accuracy: 0.9997 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1132/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.3708e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9960\n", | |
| "Epoch 1133/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.0878e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1134/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9991 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 1135/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9991 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1136/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 5.1351e-04 - accuracy: 1.0000 - val_loss: 0.0082 - val_accuracy: 0.9893\n", | |
| "Epoch 1137/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.9885e-04 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1138/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.3068e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9907\n", | |
| "Epoch 1139/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.8579e-04 - accuracy: 0.9997 - val_loss: 0.0126 - val_accuracy: 0.9853\n", | |
| "Epoch 1140/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4159e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1141/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.2031e-04 - accuracy: 0.9994 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1142/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.9994 - val_loss: 0.0106 - val_accuracy: 0.9907\n", | |
| "Epoch 1143/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.9676e-04 - accuracy: 0.9994 - val_loss: 0.0149 - val_accuracy: 0.9827\n", | |
| "Epoch 1144/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.6705e-04 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1145/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.9991 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1146/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.0284e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1147/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.0491e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1148/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.6523e-04 - accuracy: 0.9997 - val_loss: 0.0090 - val_accuracy: 0.9880\n", | |
| "Epoch 1149/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.6613e-04 - accuracy: 0.9997 - val_loss: 0.0091 - val_accuracy: 0.9853\n", | |
| "Epoch 1150/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.9438e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 1151/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.8537e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1152/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.7800e-04 - accuracy: 0.9991 - val_loss: 0.0067 - val_accuracy: 0.9947\n", | |
| "Epoch 1153/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 0.0013 - accuracy: 0.9991 - val_loss: 0.0151 - val_accuracy: 0.9867\n", | |
| "Epoch 1154/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.1131e-04 - accuracy: 0.9997 - val_loss: 0.0052 - val_accuracy: 0.9960\n", | |
| "Epoch 1155/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.5047e-04 - accuracy: 1.0000 - val_loss: 0.0080 - val_accuracy: 0.9893\n", | |
| "Epoch 1156/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.3891e-04 - accuracy: 0.9997 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1157/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.2191e-04 - accuracy: 0.9991 - val_loss: 0.0072 - val_accuracy: 0.9920\n", | |
| "Epoch 1158/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2205e-04 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9893\n", | |
| "Epoch 1159/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.7182e-04 - accuracy: 0.9997 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1160/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.4362e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1161/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.5387e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1162/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.9701e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1163/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1164/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.4042e-04 - accuracy: 0.9997 - val_loss: 0.0073 - val_accuracy: 0.9893\n", | |
| "Epoch 1165/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4688e-04 - accuracy: 0.9994 - val_loss: 0.0072 - val_accuracy: 0.9893\n", | |
| "Epoch 1166/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2255e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1167/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.6246e-04 - accuracy: 0.9997 - val_loss: 0.0087 - val_accuracy: 0.9893\n", | |
| "Epoch 1168/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.0745e-04 - accuracy: 0.9997 - val_loss: 0.0072 - val_accuracy: 0.9893\n", | |
| "Epoch 1169/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.1104e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9907\n", | |
| "Epoch 1170/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 5.7471e-04 - accuracy: 0.9997 - val_loss: 0.0090 - val_accuracy: 0.9893\n", | |
| "Epoch 1171/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.4457e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1172/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.7844e-04 - accuracy: 0.9994 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1173/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8994e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1174/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.1286e-04 - accuracy: 0.9994 - val_loss: 0.0052 - val_accuracy: 0.9933\n", | |
| "Epoch 1175/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.7187e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9947\n", | |
| "Epoch 1176/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.6644e-04 - accuracy: 1.0000 - val_loss: 0.0127 - val_accuracy: 0.9880\n", | |
| "Epoch 1177/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.6823e-04 - accuracy: 1.0000 - val_loss: 0.0089 - val_accuracy: 0.9907\n", | |
| "Epoch 1178/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.8154e-04 - accuracy: 0.9994 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 1179/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.9308e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9920\n", | |
| "Epoch 1180/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2293e-04 - accuracy: 0.9997 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1181/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2080e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1182/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4798e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1183/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.8903e-04 - accuracy: 0.9994 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1184/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4090e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1185/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.1478e-04 - accuracy: 1.0000 - val_loss: 0.0090 - val_accuracy: 0.9893\n", | |
| "Epoch 1186/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2990e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1187/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.3769e-04 - accuracy: 0.9997 - val_loss: 0.0092 - val_accuracy: 0.9907\n", | |
| "Epoch 1188/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0053 - val_accuracy: 0.9960\n", | |
| "Epoch 1189/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9994 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1190/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.4639e-04 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 1191/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8725e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1192/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.5509e-04 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9880\n", | |
| "Epoch 1193/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.9013e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1194/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6572e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1195/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0017 - accuracy: 0.9989 - val_loss: 0.0077 - val_accuracy: 0.9893\n", | |
| "Epoch 1196/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.1845e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1197/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2791e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1198/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.6378e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1199/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.6360e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1200/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4435e-04 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1201/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 5.7998e-04 - accuracy: 0.9997 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 1202/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.7692e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1203/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.5447e-04 - accuracy: 0.9994 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1204/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.4154e-04 - accuracy: 0.9994 - val_loss: 0.0088 - val_accuracy: 0.9907\n", | |
| "Epoch 1205/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 9.0492e-04 - accuracy: 0.9991 - val_loss: 0.0093 - val_accuracy: 0.9893\n", | |
| "Epoch 1206/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 8.8620e-04 - accuracy: 0.9997 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 1207/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.9178e-04 - accuracy: 0.9994 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1208/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 8.9013e-04 - accuracy: 0.9994 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1209/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1210/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.8959e-04 - accuracy: 0.9994 - val_loss: 0.0219 - val_accuracy: 0.9693\n", | |
| "Epoch 1211/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.9579e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1212/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.8598e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9907\n", | |
| "Epoch 1213/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4433e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1214/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.6838e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9907\n", | |
| "Epoch 1215/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.7506e-04 - accuracy: 0.9997 - val_loss: 0.0096 - val_accuracy: 0.9907\n", | |
| "Epoch 1216/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.9989 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1217/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7804e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1218/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6931e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1219/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1309e-04 - accuracy: 0.9994 - val_loss: 0.0056 - val_accuracy: 0.9947\n", | |
| "Epoch 1220/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.5279e-04 - accuracy: 0.9991 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1221/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.5525e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1222/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4003e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1223/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.5673e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1224/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2174e-04 - accuracy: 0.9994 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1225/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4034e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1226/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6602e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1227/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2076e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1228/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7238e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1229/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4744e-04 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9880\n", | |
| "Epoch 1230/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.5710e-04 - accuracy: 0.9994 - val_loss: 0.0071 - val_accuracy: 0.9920\n", | |
| "Epoch 1231/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 7.4193e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1232/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.7442e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1233/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.3892e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1234/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7692e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9960\n", | |
| "Epoch 1235/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.6771e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1236/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1187e-04 - accuracy: 1.0000 - val_loss: 0.0085 - val_accuracy: 0.9907\n", | |
| "Epoch 1237/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.0022e-04 - accuracy: 0.9994 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1238/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6836e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1239/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2554e-04 - accuracy: 0.9997 - val_loss: 0.0052 - val_accuracy: 0.9933\n", | |
| "Epoch 1240/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.2179e-04 - accuracy: 0.9991 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1241/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4223e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1242/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.9596e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1243/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.7198e-04 - accuracy: 0.9994 - val_loss: 0.0065 - val_accuracy: 0.9893\n", | |
| "Epoch 1244/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.1401e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1245/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.5911e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1246/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.0144e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1247/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8500e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1248/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.3414e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1249/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.6526e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 1250/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1570e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1251/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7331e-04 - accuracy: 1.0000 - val_loss: 0.0080 - val_accuracy: 0.9893\n", | |
| "Epoch 1252/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.7182e-04 - accuracy: 0.9991 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1253/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.8742e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1254/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.9233e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1255/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.9965e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1256/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.4306e-04 - accuracy: 0.9997 - val_loss: 0.0051 - val_accuracy: 0.9933\n", | |
| "Epoch 1257/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2299e-04 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 0.9933\n", | |
| "Epoch 1258/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.2804e-04 - accuracy: 0.9997 - val_loss: 0.0148 - val_accuracy: 0.9827\n", | |
| "Epoch 1259/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.7685e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1260/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4894e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1261/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.0043e-04 - accuracy: 0.9997 - val_loss: 0.0077 - val_accuracy: 0.9893\n", | |
| "Epoch 1262/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.1739e-04 - accuracy: 0.9994 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 1263/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7680e-04 - accuracy: 0.9997 - val_loss: 0.0108 - val_accuracy: 0.9907\n", | |
| "Epoch 1264/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1785e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1265/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6227e-04 - accuracy: 0.9997 - val_loss: 0.0079 - val_accuracy: 0.9920\n", | |
| "Epoch 1266/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.5818e-04 - accuracy: 0.9991 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1267/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6468e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1268/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 6.4896e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1269/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8911e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1270/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2347e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9947\n", | |
| "Epoch 1271/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.9786e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9960\n", | |
| "Epoch 1272/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4206e-04 - accuracy: 0.9997 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 1273/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.0480e-04 - accuracy: 0.9994 - val_loss: 0.0076 - val_accuracy: 0.9893\n", | |
| "Epoch 1274/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9991 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 1275/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.4708e-04 - accuracy: 0.9994 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1276/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6888e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1277/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8626e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1278/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.3242e-04 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 0.9867\n", | |
| "Epoch 1279/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6014e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 1280/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.5450e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9920\n", | |
| "Epoch 1281/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.9989 - val_loss: 0.0065 - val_accuracy: 0.9947\n", | |
| "Epoch 1282/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7288e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1283/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.0696e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1284/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7479e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1285/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4750e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1286/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2874e-04 - accuracy: 0.9997 - val_loss: 0.0096 - val_accuracy: 0.9840\n", | |
| "Epoch 1287/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.0276e-04 - accuracy: 0.9994 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1288/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6544e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1289/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.2183e-04 - accuracy: 0.9991 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1290/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 5.8157e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1291/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 6.0785e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1292/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4335e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1293/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2213e-04 - accuracy: 0.9997 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 1294/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8773e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1295/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.3814e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1296/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 5.5238e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1297/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8835e-04 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9960\n", | |
| "Epoch 1298/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.6419e-04 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1299/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2158e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1300/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.3322e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1301/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7181e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1302/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7390e-04 - accuracy: 1.0000 - val_loss: 0.0087 - val_accuracy: 0.9880\n", | |
| "Epoch 1303/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8189e-04 - accuracy: 0.9997 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1304/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7886e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 1305/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.6580e-04 - accuracy: 0.9997 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 1306/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.4856e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1307/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.5725e-04 - accuracy: 0.9997 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 1308/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8711e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1309/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.7300e-04 - accuracy: 0.9989 - val_loss: 0.0053 - val_accuracy: 0.9920\n", | |
| "Epoch 1310/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.8872e-04 - accuracy: 0.9994 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1311/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1433e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9920\n", | |
| "Epoch 1312/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8431e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9960\n", | |
| "Epoch 1313/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5015e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9907\n", | |
| "Epoch 1314/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.6208e-04 - accuracy: 1.0000 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1315/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4664e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1316/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4481e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1317/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0023 - accuracy: 0.9974 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1318/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9088e-04 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 1319/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7698e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1320/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6362e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1321/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 7.1829e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1322/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9562e-04 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 1323/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.3604e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1324/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7852e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1325/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9089e-04 - accuracy: 1.0000 - val_loss: 0.0103 - val_accuracy: 0.9920\n", | |
| "Epoch 1326/3000\n", | |
| "110/110 [==============================] - 1s 6ms/step - loss: 6.1733e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1327/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1659e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 1328/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1375e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1329/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.0034e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9907\n", | |
| "Epoch 1330/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.5171e-04 - accuracy: 0.9997 - val_loss: 0.0081 - val_accuracy: 0.9907\n", | |
| "Epoch 1331/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8645e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9907\n", | |
| "Epoch 1332/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.3253e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1333/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4456e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1334/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1304e-04 - accuracy: 0.9994 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1335/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.2531e-04 - accuracy: 1.0000 - val_loss: 0.0141 - val_accuracy: 0.9787\n", | |
| "Epoch 1336/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1337/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7958e-04 - accuracy: 0.9997 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 1338/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7668e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1339/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8253e-04 - accuracy: 0.9997 - val_loss: 0.0141 - val_accuracy: 0.9840\n", | |
| "Epoch 1340/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.7013e-04 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1341/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6471e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1342/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.9087e-04 - accuracy: 0.9991 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1343/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5654e-04 - accuracy: 1.0000 - val_loss: 0.0087 - val_accuracy: 0.9880\n", | |
| "Epoch 1344/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.5004e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1345/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7974e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1346/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6692e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1347/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8597e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1348/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 9.1150e-04 - accuracy: 0.9994 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1349/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.3876e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1350/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4392e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9907\n", | |
| "Epoch 1351/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.3889e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1352/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8523e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1353/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7770e-04 - accuracy: 1.0000 - val_loss: 0.0071 - val_accuracy: 0.9920\n", | |
| "Epoch 1354/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.9989 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1355/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.6097e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1356/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9056e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1357/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.0771e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1358/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6248e-04 - accuracy: 0.9991 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1359/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.9322e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1360/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7052e-04 - accuracy: 0.9997 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1361/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.9309e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1362/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.3432e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1363/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7179e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1364/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.0296e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1365/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2820e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1366/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 5.5642e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1367/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2094e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1368/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1206e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1369/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4565e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1370/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8469e-04 - accuracy: 0.9997 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 1371/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8951e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1372/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.5808e-04 - accuracy: 1.0000 - val_loss: 0.0074 - val_accuracy: 0.9893\n", | |
| "Epoch 1373/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.0828e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1374/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6214e-04 - accuracy: 1.0000 - val_loss: 0.0076 - val_accuracy: 0.9880\n", | |
| "Epoch 1375/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.3894e-04 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1376/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1377/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2525e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1378/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8555e-04 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 1379/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.6240e-04 - accuracy: 0.9994 - val_loss: 0.0049 - val_accuracy: 0.9947\n", | |
| "Epoch 1380/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.0953e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1381/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.1745e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1382/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2675e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1383/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1509e-04 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 1384/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1194e-04 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 0.9907\n", | |
| "Epoch 1385/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4462e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1386/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2770e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1387/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.6179e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1388/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7104e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1389/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4926e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 1390/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3655e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9893\n", | |
| "Epoch 1391/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9532e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1392/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6279e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1393/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5453e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1394/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.3960e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1395/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9592e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9907\n", | |
| "Epoch 1396/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4621e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1397/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.5893e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 1398/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0526e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 1399/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2706e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1400/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2146e-04 - accuracy: 0.9997 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 1401/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4591e-04 - accuracy: 0.9994 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1402/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9744e-04 - accuracy: 1.0000 - val_loss: 0.0086 - val_accuracy: 0.9893\n", | |
| "Epoch 1403/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1307e-04 - accuracy: 1.0000 - val_loss: 0.0097 - val_accuracy: 0.9867\n", | |
| "Epoch 1404/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.9921e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1405/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5361e-04 - accuracy: 1.0000 - val_loss: 0.0082 - val_accuracy: 0.9907\n", | |
| "Epoch 1406/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.7987e-04 - accuracy: 0.9994 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1407/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1641e-04 - accuracy: 0.9994 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1408/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5961e-04 - accuracy: 0.9997 - val_loss: 0.0078 - val_accuracy: 0.9880\n", | |
| "Epoch 1409/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5822e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1410/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.4838e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1411/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2883e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1412/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9865e-04 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 0.9893\n", | |
| "Epoch 1413/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9572e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1414/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.9673e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1415/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.9367e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1416/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.0420e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1417/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4894e-04 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 0.9907\n", | |
| "Epoch 1418/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4969e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 1419/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1467e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1420/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4028e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1421/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.5251e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1422/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5943e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1423/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5834e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1424/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1020e-04 - accuracy: 0.9994 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1425/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5977e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1426/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0019 - accuracy: 0.9980 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1427/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8058e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1428/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.5633e-04 - accuracy: 0.9997 - val_loss: 0.0062 - val_accuracy: 0.9933\n", | |
| "Epoch 1429/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1747e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1430/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8730e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1431/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4081e-04 - accuracy: 0.9997 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1432/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9600e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1433/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7028e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1434/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6529e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1435/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4360e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1436/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3005e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 1437/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5689e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9920\n", | |
| "Epoch 1438/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.0404e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 1439/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3111e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1440/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.9635e-04 - accuracy: 0.9997 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1441/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0785e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1442/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2936e-04 - accuracy: 0.9997 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1443/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1827e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1444/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2637e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1445/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5971e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1446/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7830e-04 - accuracy: 0.9997 - val_loss: 0.0067 - val_accuracy: 0.9907\n", | |
| "Epoch 1447/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6665e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1448/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4102e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1449/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8388e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1450/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7262e-04 - accuracy: 0.9997 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 1451/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8188e-04 - accuracy: 0.9997 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1452/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.7617e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1453/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7570e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1454/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8383e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1455/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2108e-04 - accuracy: 1.0000 - val_loss: 0.0081 - val_accuracy: 0.9907\n", | |
| "Epoch 1456/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7038e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1457/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4293e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1458/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1205e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1459/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5286e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9947\n", | |
| "Epoch 1460/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.7874e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9960\n", | |
| "Epoch 1461/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7586e-04 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 0.9920\n", | |
| "Epoch 1462/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9261e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1463/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4879e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1464/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1193e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9947\n", | |
| "Epoch 1465/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8946e-04 - accuracy: 0.9997 - val_loss: 0.0081 - val_accuracy: 0.9907\n", | |
| "Epoch 1466/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7879e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9907\n", | |
| "Epoch 1467/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7898e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1468/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4381e-04 - accuracy: 1.0000 - val_loss: 0.0070 - val_accuracy: 0.9920\n", | |
| "Epoch 1469/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 8.3544e-04 - accuracy: 0.9994 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1470/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.5278e-04 - accuracy: 1.0000 - val_loss: 0.0076 - val_accuracy: 0.9907\n", | |
| "Epoch 1471/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.9986 - val_loss: 0.0051 - val_accuracy: 0.9933\n", | |
| "Epoch 1472/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5560e-04 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 0.9907\n", | |
| "Epoch 1473/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4885e-04 - accuracy: 0.9997 - val_loss: 0.0049 - val_accuracy: 0.9947\n", | |
| "Epoch 1474/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2557e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1475/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9757e-04 - accuracy: 1.0000 - val_loss: 0.0087 - val_accuracy: 0.9853\n", | |
| "Epoch 1476/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5062e-04 - accuracy: 1.0000 - val_loss: 0.0110 - val_accuracy: 0.9827\n", | |
| "Epoch 1477/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.6555e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1478/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1702e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1479/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6290e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9867\n", | |
| "Epoch 1480/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.2904e-04 - accuracy: 0.9994 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1481/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1984e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1482/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.4045e-04 - accuracy: 0.9997 - val_loss: 0.0162 - val_accuracy: 0.9773\n", | |
| "Epoch 1483/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.2835e-04 - accuracy: 0.9994 - val_loss: 0.0237 - val_accuracy: 0.9747\n", | |
| "Epoch 1484/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 0.0012 - accuracy: 0.9989 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1485/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.0555e-04 - accuracy: 1.0000 - val_loss: 0.0071 - val_accuracy: 0.9907\n", | |
| "Epoch 1486/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0670e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1487/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4346e-04 - accuracy: 0.9994 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1488/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3644e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1489/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2433e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9920\n", | |
| "Epoch 1490/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.2426e-04 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1491/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2343e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 1492/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4264e-04 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1493/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6722e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9933\n", | |
| "Epoch 1494/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8474e-04 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 0.9880\n", | |
| "Epoch 1495/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.5373e-04 - accuracy: 1.0000 - val_loss: 0.0116 - val_accuracy: 0.9800\n", | |
| "Epoch 1496/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2823e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1497/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4120e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1498/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0618e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9907\n", | |
| "Epoch 1499/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1653e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1500/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8287e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9907\n", | |
| "Epoch 1501/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2080e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1502/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1842e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 1503/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0005e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1504/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6016e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1505/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7100e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9907\n", | |
| "Epoch 1506/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5935e-04 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 0.9973\n", | |
| "Epoch 1507/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.0165e-04 - accuracy: 0.9997 - val_loss: 0.0074 - val_accuracy: 0.9907\n", | |
| "Epoch 1508/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8850e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1509/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9886e-04 - accuracy: 0.9997 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1510/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.1188e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1511/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4603e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1512/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4886e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1513/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.1385e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1514/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2827e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1515/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8276e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1516/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9697e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1517/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1761e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1518/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7152e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1519/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7691e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1520/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2568e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1521/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6359e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1522/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5837e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1523/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4287e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1524/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4003e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1525/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4660e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1526/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5631e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1527/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9001e-04 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 0.9907\n", | |
| "Epoch 1528/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.8465e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1529/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5374e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 1530/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6450e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1531/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9605e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9907\n", | |
| "Epoch 1532/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9423e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1533/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4928e-04 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1534/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1361e-04 - accuracy: 1.0000 - val_loss: 0.0112 - val_accuracy: 0.9880\n", | |
| "Epoch 1535/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1232e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1536/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2771e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9920\n", | |
| "Epoch 1537/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3376e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1538/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6703e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1539/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9741e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1540/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7063e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1541/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4906e-04 - accuracy: 0.9997 - val_loss: 0.0069 - val_accuracy: 0.9920\n", | |
| "Epoch 1542/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6422e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1543/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5478e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1544/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0242e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9907\n", | |
| "Epoch 1545/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6360e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1546/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3988e-04 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 0.9960\n", | |
| "Epoch 1547/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7280e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1548/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5116e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 1549/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0292e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1550/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.8995e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1551/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3786e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1552/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6804e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1553/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0576e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1554/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7859e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1555/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8289e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1556/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2738e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 1557/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2749e-04 - accuracy: 0.9997 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1558/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3295e-04 - accuracy: 1.0000 - val_loss: 0.0094 - val_accuracy: 0.9840\n", | |
| "Epoch 1559/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2405e-04 - accuracy: 1.0000 - val_loss: 0.0087 - val_accuracy: 0.9853\n", | |
| "Epoch 1560/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 7.8662e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1561/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8442e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1562/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1660e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1563/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3187e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1564/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7194e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1565/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.3613e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1566/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4482e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1567/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8488e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1568/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6959e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1569/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3814e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1570/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.9951e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1571/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0638e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1572/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4939e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1573/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5468e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1574/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5866e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1575/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8034e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1576/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3192e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1577/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2505e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1578/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0591e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9920\n", | |
| "Epoch 1579/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6328e-04 - accuracy: 0.9997 - val_loss: 0.0070 - val_accuracy: 0.9893\n", | |
| "Epoch 1580/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.6281e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1581/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8166e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1582/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.6334e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1583/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.1035e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1584/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.0385e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1585/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7930e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1586/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.6723e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1587/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.0278e-04 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 0.9920\n", | |
| "Epoch 1588/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.4524e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1589/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3372e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1590/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0010e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9947\n", | |
| "Epoch 1591/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2690e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1592/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5833e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1593/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.7967e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9907\n", | |
| "Epoch 1594/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 5.0242e-04 - accuracy: 0.9994 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1595/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0957e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1596/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3464e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9907\n", | |
| "Epoch 1597/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9720e-04 - accuracy: 1.0000 - val_loss: 0.0081 - val_accuracy: 0.9907\n", | |
| "Epoch 1598/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4015e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1599/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7067e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1600/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.2032e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1601/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1295e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1602/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1159e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1603/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1319e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1604/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2688e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1605/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9381e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1606/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.0841e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9973\n", | |
| "Epoch 1607/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2902e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1608/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.2212e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1609/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6744e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1610/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8870e-04 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1611/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4051e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9907\n", | |
| "Epoch 1612/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1319e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1613/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1185e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1614/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0770e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1615/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3245e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1616/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4025e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1617/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.9715e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1618/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2470e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1619/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2820e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1620/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4160e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9933\n", | |
| "Epoch 1621/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2868e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 1622/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6119e-04 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 0.9947\n", | |
| "Epoch 1623/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3373e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1624/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.6307e-04 - accuracy: 0.9994 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1625/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4342e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1626/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1267e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1627/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.0984e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1628/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1950e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1629/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.7264e-04 - accuracy: 0.9994 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1630/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.0281e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1631/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2428e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1632/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1030e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1633/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6465e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1634/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.0512e-04 - accuracy: 1.0000 - val_loss: 0.0070 - val_accuracy: 0.9907\n", | |
| "Epoch 1635/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4244e-04 - accuracy: 1.0000 - val_loss: 0.0100 - val_accuracy: 0.9853\n", | |
| "Epoch 1636/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7687e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1637/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0384e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1638/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8770e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1639/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3253e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1640/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4224e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9893\n", | |
| "Epoch 1641/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7063e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1642/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5986e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9933\n", | |
| "Epoch 1643/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8845e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1644/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4073e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1645/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0350e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1646/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3122e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 1647/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.7031e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1648/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8547e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1649/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.9877e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1650/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8273e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1651/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.7991e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1652/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6455e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1653/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0094e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1654/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3025e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1655/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0849e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9933\n", | |
| "Epoch 1656/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1977e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1657/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8438e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1658/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4390e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1659/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3144e-04 - accuracy: 0.9997 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1660/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3061e-04 - accuracy: 1.0000 - val_loss: 0.0078 - val_accuracy: 0.9907\n", | |
| "Epoch 1661/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8565e-04 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 1662/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5892e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1663/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.9042e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9960\n", | |
| "Epoch 1664/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.9197e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1665/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9307e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1666/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7895e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1667/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4043e-04 - accuracy: 1.0000 - val_loss: 0.0184 - val_accuracy: 0.9787\n", | |
| "Epoch 1668/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.8252e-04 - accuracy: 1.0000 - val_loss: 0.0071 - val_accuracy: 0.9893\n", | |
| "Epoch 1669/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2417e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1670/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3228e-04 - accuracy: 1.0000 - val_loss: 0.0075 - val_accuracy: 0.9867\n", | |
| "Epoch 1671/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0057e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1672/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3961e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1673/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.7636e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1674/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2803e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1675/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6905e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1676/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8019e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1677/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5169e-04 - accuracy: 1.0000 - val_loss: 0.0212 - val_accuracy: 0.9800\n", | |
| "Epoch 1678/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4211e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1679/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.6046e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1680/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6928e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1681/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2419e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1682/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.6669e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1683/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 4.3271e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1684/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4758e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 1685/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7622e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1686/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5866e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1687/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5687e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9933\n", | |
| "Epoch 1688/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7620e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1689/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2578e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1690/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1768e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1691/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4887e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1692/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8386e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1693/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2230e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1694/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0538e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 1695/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2554e-04 - accuracy: 1.0000 - val_loss: 0.0103 - val_accuracy: 0.9867\n", | |
| "Epoch 1696/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5581e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1697/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9297e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1698/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7592e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1699/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3722e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1700/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.3672e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1701/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8896e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1702/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1337e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9933\n", | |
| "Epoch 1703/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.7716e-04 - accuracy: 1.0000 - val_loss: 0.0167 - val_accuracy: 0.9733\n", | |
| "Epoch 1704/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.8415e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1705/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 5.4486e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1706/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4040e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1707/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2799e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1708/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1618e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1709/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.8243e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1710/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9034e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1711/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3778e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1712/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5610e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1713/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5219e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 1714/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.9951e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1715/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0020e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1716/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8818e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1717/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.5452e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1718/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3729e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9933\n", | |
| "Epoch 1719/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6706e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1720/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9568e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1721/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4613e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1722/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9796e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1723/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.5840e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1724/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6804e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1725/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3051e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1726/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7348e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1727/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7668e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1728/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2321e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1729/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5209e-04 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 0.9907\n", | |
| "Epoch 1730/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9677e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1731/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0033e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1732/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6283e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9960\n", | |
| "Epoch 1733/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0099e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1734/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5939e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1735/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.3369e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1736/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2692e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1737/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.7535e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1738/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9938e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1739/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3972e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1740/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8976e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 1741/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4204e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1742/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1313e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1743/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8085e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1744/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0662e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1745/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2865e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1746/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8521e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9947\n", | |
| "Epoch 1747/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5480e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1748/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4371e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1749/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6798e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1750/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2771e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1751/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1206e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1752/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8535e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1753/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5471e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1754/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.9543e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1755/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.2440e-04 - accuracy: 1.0000 - val_loss: 0.0079 - val_accuracy: 0.9907\n", | |
| "Epoch 1756/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3095e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1757/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 6.5889e-04 - accuracy: 0.9994 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1758/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7913e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1759/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3219e-04 - accuracy: 0.9997 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1760/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4083e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1761/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7777e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1762/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4317e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1763/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8656e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1764/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3411e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1765/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.6105e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1766/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6235e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1767/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8901e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1768/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6735e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1769/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2368e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1770/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0861e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1771/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8046e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1772/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.0420e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1773/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8953e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1774/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8309e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1775/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.5220e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1776/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0572e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1777/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6142e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1778/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2214e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1779/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.1808e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9947\n", | |
| "Epoch 1780/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.8221e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1781/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6525e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1782/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1505e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1783/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2828e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1784/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5394e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1785/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4943e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1786/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3883e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1787/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.0457e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1788/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6836e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1789/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3220e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1790/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3747e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1791/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1673e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1792/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8136e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1793/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.0887e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1794/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7819e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1795/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0077e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1796/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.6762e-04 - accuracy: 0.9997 - val_loss: 0.0075 - val_accuracy: 0.9920\n", | |
| "Epoch 1797/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8746e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1798/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8475e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1799/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.9974e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1800/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5686e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1801/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2777e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9933\n", | |
| "Epoch 1802/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.9669e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1803/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6463e-04 - accuracy: 1.0000 - val_loss: 0.0068 - val_accuracy: 0.9920\n", | |
| "Epoch 1804/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0859e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1805/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5740e-04 - accuracy: 0.9997 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1806/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3749e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1807/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.1195e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1808/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6020e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1809/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7942e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1810/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1971e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1811/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4237e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1812/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1540e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1813/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7560e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1814/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1326e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1815/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4327e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1816/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.9245e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1817/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4998e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1818/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5051e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1819/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1611e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9907\n", | |
| "Epoch 1820/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.5875e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1821/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8222e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1822/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3576e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1823/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2934e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1824/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 3.1339e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1825/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3091e-04 - accuracy: 1.0000 - val_loss: 0.0080 - val_accuracy: 0.9907\n", | |
| "Epoch 1826/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4795e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1827/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1421e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1828/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7585e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1829/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8631e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1830/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7308e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1831/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4910e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1832/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3844e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1833/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5503e-04 - accuracy: 0.9997 - val_loss: 0.0227 - val_accuracy: 0.9653\n", | |
| "Epoch 1834/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.3784e-04 - accuracy: 0.9994 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1835/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2986e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9947\n", | |
| "Epoch 1836/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.6087e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1837/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4465e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1838/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1849e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1839/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.3588e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1840/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.6080e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1841/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.7981e-04 - accuracy: 0.9997 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1842/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6750e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1843/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4224e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1844/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9022e-04 - accuracy: 1.0000 - val_loss: 0.0090 - val_accuracy: 0.9907\n", | |
| "Epoch 1845/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4178e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1846/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3832e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1847/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1325e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1848/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2771e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1849/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2950e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1850/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1222e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1851/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1637e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1852/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1298e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1853/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9933e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1854/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.6854e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1855/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5633e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1856/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.4558e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1857/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2657e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9947\n", | |
| "Epoch 1858/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 4.4408e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1859/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7103e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1860/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2004e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1861/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3900e-04 - accuracy: 0.9997 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1862/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3224e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1863/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3608e-04 - accuracy: 1.0000 - val_loss: 0.0069 - val_accuracy: 0.9907\n", | |
| "Epoch 1864/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0524e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1865/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0027e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1866/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.2780e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1867/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3233e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1868/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6943e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1869/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2739e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1870/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.5668e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1871/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.1884e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1872/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8227e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1873/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0849e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1874/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7284e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1875/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2563e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1876/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6492e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1877/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9015e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 1878/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 2.0638e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1879/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7531e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1880/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8870e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1881/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.3294e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1882/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4407e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1883/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0612e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1884/3000\n", | |
| "110/110 [==============================] - 1s 10ms/step - loss: 2.3083e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1885/3000\n", | |
| "110/110 [==============================] - 2s 18ms/step - loss: 2.7522e-04 - accuracy: 0.9997 - val_loss: 0.0080 - val_accuracy: 0.9907\n", | |
| "Epoch 1886/3000\n", | |
| "110/110 [==============================] - 2s 18ms/step - loss: 1.6815e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1887/3000\n", | |
| "110/110 [==============================] - 2s 17ms/step - loss: 2.2365e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9947\n", | |
| "Epoch 1888/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 1.9199e-04 - accuracy: 1.0000 - val_loss: 0.0072 - val_accuracy: 0.9920\n", | |
| "Epoch 1889/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6921e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1890/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.3893e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1891/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1248e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1892/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7691e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1893/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0703e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1894/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8527e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1895/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.9617e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1896/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4212e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1897/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.1433e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1898/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5771e-04 - accuracy: 0.9997 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1899/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0219e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1900/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1708e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1901/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5851e-04 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 0.9947\n", | |
| "Epoch 1902/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2041e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 1903/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5120e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1904/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4140e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1905/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9886e-04 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 0.9947\n", | |
| "Epoch 1906/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.8102e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1907/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4954e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1908/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5030e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1909/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8211e-04 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 0.9907\n", | |
| "Epoch 1910/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8240e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1911/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0376e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1912/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2599e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1913/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6778e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1914/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3184e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1915/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.5146e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1916/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2561e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9907\n", | |
| "Epoch 1917/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7244e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1918/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1029e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1919/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2712e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1920/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6532e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1921/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.7834e-04 - accuracy: 1.0000 - val_loss: 0.0062 - val_accuracy: 0.9920\n", | |
| "Epoch 1922/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0691e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1923/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7244e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1924/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9790e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1925/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2899e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1926/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8099e-04 - accuracy: 1.0000 - val_loss: 0.0076 - val_accuracy: 0.9907\n", | |
| "Epoch 1927/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5022e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1928/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.4405e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1929/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5937e-04 - accuracy: 0.9997 - val_loss: 0.0093 - val_accuracy: 0.9853\n", | |
| "Epoch 1930/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.0829e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1931/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7518e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1932/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5978e-04 - accuracy: 1.0000 - val_loss: 0.0086 - val_accuracy: 0.9853\n", | |
| "Epoch 1933/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0131e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1934/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6008e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1935/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4912e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1936/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7037e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9920\n", | |
| "Epoch 1937/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9205e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1938/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3864e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1939/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.9511e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1940/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3935e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 1941/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0772e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1942/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0471e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1943/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4620e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1944/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.1425e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1945/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5573e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1946/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9337e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1947/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5027e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1948/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4661e-04 - accuracy: 0.9997 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1949/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.9301e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 1950/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4045e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9920\n", | |
| "Epoch 1951/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4853e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1952/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0293e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1953/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.2890e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9933\n", | |
| "Epoch 1954/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0667e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1955/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5017e-04 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 0.9920\n", | |
| "Epoch 1956/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6224e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1957/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7352e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1958/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0607e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1959/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2509e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1960/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7600e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1961/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8001e-04 - accuracy: 1.0000 - val_loss: 0.0091 - val_accuracy: 0.9867\n", | |
| "Epoch 1962/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.8373e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1963/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8465e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1964/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3282e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1965/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1930e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1966/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5753e-04 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 0.9947\n", | |
| "Epoch 1967/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.6693e-04 - accuracy: 0.9997 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1968/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0684e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1969/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9048e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1970/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.4829e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 1971/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5223e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 1972/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1467e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 1973/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3987e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 1974/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4337e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 1975/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7697e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 1976/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8481e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1977/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.4853e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1978/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.9919e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 1979/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.2524e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 1980/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.1700e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 1981/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8499e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 1982/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0312e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1983/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5439e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1984/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8015e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1985/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8268e-04 - accuracy: 1.0000 - val_loss: 0.0081 - val_accuracy: 0.9867\n", | |
| "Epoch 1986/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.2602e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 1987/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.3183e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1988/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9620e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 1989/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9010e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 1990/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6206e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 1991/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.5190e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 1992/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3095e-04 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 0.9907\n", | |
| "Epoch 1993/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6501e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1994/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.2792e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 1995/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7912e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1996/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8552e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 1997/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3334e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 1998/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.6038e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 1999/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0688e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 2000/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.2885e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 2001/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.2858e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2002/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.2931e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9920\n", | |
| "Epoch 2003/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6612e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9947\n", | |
| "Epoch 2004/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3011e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2005/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4345e-04 - accuracy: 1.0000 - val_loss: 0.0143 - val_accuracy: 0.9800\n", | |
| "Epoch 2006/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 3.3150e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 2007/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.0265e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2008/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2388e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2009/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7717e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 2010/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5686e-04 - accuracy: 0.9997 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 2011/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4458e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 2012/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6745e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2013/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6157e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 2014/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6549e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2015/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2635e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2016/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8860e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 2017/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8233e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 2018/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.1378e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2019/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 2.0270e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2020/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8421e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 2021/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6740e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2022/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5474e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9920\n", | |
| "Epoch 2023/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8818e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2024/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1334e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2025/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9888e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2026/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4878e-04 - accuracy: 1.0000 - val_loss: 0.0088 - val_accuracy: 0.9880\n", | |
| "Epoch 2027/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.3604e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 2028/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1955e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 2029/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 5.0145e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 2030/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4773e-04 - accuracy: 1.0000 - val_loss: 0.0065 - val_accuracy: 0.9920\n", | |
| "Epoch 2031/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7816e-04 - accuracy: 1.0000 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 2032/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7613e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 2033/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8037e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2034/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7397e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2035/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9703e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9907\n", | |
| "Epoch 2036/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3442e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 2037/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7885e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2038/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5331e-04 - accuracy: 0.9997 - val_loss: 0.0083 - val_accuracy: 0.9893\n", | |
| "Epoch 2039/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4100e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 2040/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6545e-04 - accuracy: 1.0000 - val_loss: 0.0051 - val_accuracy: 0.9947\n", | |
| "Epoch 2041/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7133e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9947\n", | |
| "Epoch 2042/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5282e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 2043/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3631e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2044/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.5156e-04 - accuracy: 0.9997 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 2045/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9162e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2046/3000\n", | |
| "110/110 [==============================] - 1s 9ms/step - loss: 1.8165e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2047/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4331e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2048/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3677e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 2049/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7798e-04 - accuracy: 1.0000 - val_loss: 0.0060 - val_accuracy: 0.9920\n", | |
| "Epoch 2050/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5002e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2051/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6568e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 2052/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7701e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2053/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.3017e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2054/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6296e-04 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 0.9920\n", | |
| "Epoch 2055/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7398e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2056/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7818e-04 - accuracy: 1.0000 - val_loss: 0.0067 - val_accuracy: 0.9920\n", | |
| "Epoch 2057/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.1635e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 2058/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.4925e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2059/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.6051e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2060/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 2.2817e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2061/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.2554e-04 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 0.9933\n", | |
| "Epoch 2062/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6638e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9947\n", | |
| "Epoch 2063/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9182e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9933\n", | |
| "Epoch 2064/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.2451e-04 - accuracy: 0.9997 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2065/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.4032e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9933\n", | |
| "Epoch 2066/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.5579e-04 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 0.9933\n", | |
| "Epoch 2067/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4235e-04 - accuracy: 1.0000 - val_loss: 0.0059 - val_accuracy: 0.9933\n", | |
| "Epoch 2068/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 3.0127e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2069/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9433e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 2070/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7186e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2071/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7226e-04 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 0.9920\n", | |
| "Epoch 2072/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.7186e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9920\n", | |
| "Epoch 2073/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8218e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9933\n", | |
| "Epoch 2074/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.7962e-04 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 0.9947\n", | |
| "Epoch 2075/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.9651e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 2076/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.4063e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2077/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.6142e-04 - accuracy: 1.0000 - val_loss: 0.0055 - val_accuracy: 0.9933\n", | |
| "Epoch 2078/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.1467e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2079/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.5106e-04 - accuracy: 1.0000 - val_loss: 0.0053 - val_accuracy: 0.9947\n", | |
| "Epoch 2080/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.8601e-04 - accuracy: 0.9997 - val_loss: 0.0063 - val_accuracy: 0.9920\n", | |
| "Epoch 2081/3000\n", | |
| "110/110 [==============================] - 1s 7ms/step - loss: 1.8716e-04 - accuracy: 1.0000 - val_loss: 0.0057 - val_accuracy: 0.9920\n", | |
| "Epoch 2082/3000\n", | |
| "110/110 [==============================] - 1s 8ms/step - loss: 1.5009e-04 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 0.9933\n", | |
| "Epoch 2083/3000\n", | |
| "110/110 [============== |
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