Inception.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JAZ3rvN3LJ0v"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow.keras\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, GlobalAveragePooling2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from tensorflow.keras.layers import Add,concatenate\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras import Model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "nGoZY5vBRLa8",
    "outputId": "7cd9c2ec-cae9-4503-c353-50e0b915ac38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "#from google.colab import files\n",
    "#uploaded = files.upload()\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "T_N74Mf4RLw7",
    "outputId": "551d0470-02ce-48f0-f743-435589b7e8fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/My Drive/ECE 657/Part-2\n"
     ]
    }
   ],
   "source": [
    "cd 'drive/My Drive/ECE 657/Part-2/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 346
    },
    "colab_type": "code",
    "id": "PxrsBUjORU2v",
    "outputId": "1a5faa94-a460-474f-b1c0-d46d5e090225"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Label</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>...</th>\n",
       "      <th>745</th>\n",
       "      <th>746</th>\n",
       "      <th>747</th>\n",
       "      <th>748</th>\n",
       "      <th>749</th>\n",
       "      <th>750</th>\n",
       "      <th>751</th>\n",
       "      <th>752</th>\n",
       "      <th>753</th>\n",
       "      <th>754</th>\n",
       "      <th>755</th>\n",
       "      <th>756</th>\n",
       "      <th>757</th>\n",
       "      <th>758</th>\n",
       "      <th>759</th>\n",
       "      <th>760</th>\n",
       "      <th>761</th>\n",
       "      <th>762</th>\n",
       "      <th>763</th>\n",
       "      <th>764</th>\n",
       "      <th>765</th>\n",
       "      <th>766</th>\n",
       "      <th>767</th>\n",
       "      <th>768</th>\n",
       "      <th>769</th>\n",
       "      <th>770</th>\n",
       "      <th>771</th>\n",
       "      <th>772</th>\n",
       "      <th>773</th>\n",
       "      <th>774</th>\n",
       "      <th>775</th>\n",
       "      <th>776</th>\n",
       "      <th>777</th>\n",
       "      <th>778</th>\n",
       "      <th>779</th>\n",
       "      <th>780</th>\n",
       "      <th>781</th>\n",
       "      <th>782</th>\n",
       "      <th>783</th>\n",
       "      <th>784</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.00000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.00000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.00000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.00000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39999.500000</td>\n",
       "      <td>2.002933</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.006067</td>\n",
       "      <td>0.033700</td>\n",
       "      <td>0.090450</td>\n",
       "      <td>0.241383</td>\n",
       "      <td>0.392517</td>\n",
       "      <td>0.777333</td>\n",
       "      <td>2.158300</td>\n",
       "      <td>5.597000</td>\n",
       "      <td>14.485433</td>\n",
       "      <td>32.866333</td>\n",
       "      <td>45.783900</td>\n",
       "      <td>51.985883</td>\n",
       "      <td>50.779350</td>\n",
       "      <td>50.425750</td>\n",
       "      <td>53.686350</td>\n",
       "      <td>51.371150</td>\n",
       "      <td>40.467633</td>\n",
       "      <td>25.962700</td>\n",
       "      <td>10.281083</td>\n",
       "      <td>4.034833</td>\n",
       "      <td>1.929250</td>\n",
       "      <td>1.148100</td>\n",
       "      <td>0.842217</td>\n",
       "      <td>0.47675</td>\n",
       "      <td>0.262000</td>\n",
       "      <td>0.08960</td>\n",
       "      <td>0.015167</td>\n",
       "      <td>0.004783</td>\n",
       "      <td>0.020917</td>\n",
       "      <td>0.102850</td>\n",
       "      <td>0.339433</td>\n",
       "      <td>0.741917</td>\n",
       "      <td>2.058883</td>\n",
       "      <td>7.226483</td>\n",
       "      <td>17.394200</td>\n",
       "      <td>32.014417</td>\n",
       "      <td>53.019567</td>\n",
       "      <td>...</td>\n",
       "      <td>92.459967</td>\n",
       "      <td>90.458233</td>\n",
       "      <td>79.041600</td>\n",
       "      <td>62.473383</td>\n",
       "      <td>46.021650</td>\n",
       "      <td>44.367617</td>\n",
       "      <td>47.964333</td>\n",
       "      <td>38.793683</td>\n",
       "      <td>22.335200</td>\n",
       "      <td>9.189950</td>\n",
       "      <td>3.814733</td>\n",
       "      <td>0.622083</td>\n",
       "      <td>0.024617</td>\n",
       "      <td>0.220950</td>\n",
       "      <td>1.03375</td>\n",
       "      <td>4.379250</td>\n",
       "      <td>12.673967</td>\n",
       "      <td>21.02115</td>\n",
       "      <td>21.219867</td>\n",
       "      <td>17.159950</td>\n",
       "      <td>22.219717</td>\n",
       "      <td>30.688333</td>\n",
       "      <td>41.950650</td>\n",
       "      <td>51.574867</td>\n",
       "      <td>52.491717</td>\n",
       "      <td>46.289000</td>\n",
       "      <td>42.470617</td>\n",
       "      <td>45.183467</td>\n",
       "      <td>50.020433</td>\n",
       "      <td>46.359133</td>\n",
       "      <td>34.545900</td>\n",
       "      <td>23.297883</td>\n",
       "      <td>16.652150</td>\n",
       "      <td>17.953617</td>\n",
       "      <td>22.969333</td>\n",
       "      <td>17.967350</td>\n",
       "      <td>8.525333</td>\n",
       "      <td>2.753883</td>\n",
       "      <td>0.832950</td>\n",
       "      <td>0.072850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17320.652413</td>\n",
       "      <td>1.415000</td>\n",
       "      <td>0.061507</td>\n",
       "      <td>0.292286</td>\n",
       "      <td>1.178115</td>\n",
       "      <td>2.306445</td>\n",
       "      <td>4.365543</td>\n",
       "      <td>5.702057</td>\n",
       "      <td>8.023953</td>\n",
       "      <td>13.942884</td>\n",
       "      <td>23.572943</td>\n",
       "      <td>38.252977</td>\n",
       "      <td>57.692077</td>\n",
       "      <td>65.947637</td>\n",
       "      <td>68.565341</td>\n",
       "      <td>67.919718</td>\n",
       "      <td>67.397257</td>\n",
       "      <td>69.421676</td>\n",
       "      <td>67.915419</td>\n",
       "      <td>62.725940</td>\n",
       "      <td>51.620728</td>\n",
       "      <td>32.300648</td>\n",
       "      <td>20.015361</td>\n",
       "      <td>13.871987</td>\n",
       "      <td>10.593331</td>\n",
       "      <td>9.214058</td>\n",
       "      <td>7.01254</td>\n",
       "      <td>5.295104</td>\n",
       "      <td>2.78752</td>\n",
       "      <td>1.048501</td>\n",
       "      <td>0.361104</td>\n",
       "      <td>1.133843</td>\n",
       "      <td>3.104964</td>\n",
       "      <td>6.149413</td>\n",
       "      <td>9.589874</td>\n",
       "      <td>15.716790</td>\n",
       "      <td>29.936239</td>\n",
       "      <td>47.877737</td>\n",
       "      <td>65.585449</td>\n",
       "      <td>82.247678</td>\n",
       "      <td>...</td>\n",
       "      <td>100.881732</td>\n",
       "      <td>99.700169</td>\n",
       "      <td>95.036194</td>\n",
       "      <td>88.766993</td>\n",
       "      <td>78.686045</td>\n",
       "      <td>77.236284</td>\n",
       "      <td>83.267721</td>\n",
       "      <td>74.407819</td>\n",
       "      <td>55.982185</td>\n",
       "      <td>37.736031</td>\n",
       "      <td>24.425300</td>\n",
       "      <td>8.175978</td>\n",
       "      <td>1.009238</td>\n",
       "      <td>4.416291</td>\n",
       "      <td>10.07838</td>\n",
       "      <td>20.894199</td>\n",
       "      <td>37.020302</td>\n",
       "      <td>49.07243</td>\n",
       "      <td>48.312498</td>\n",
       "      <td>42.208942</td>\n",
       "      <td>48.438714</td>\n",
       "      <td>55.116266</td>\n",
       "      <td>62.242895</td>\n",
       "      <td>68.555227</td>\n",
       "      <td>69.759801</td>\n",
       "      <td>67.367902</td>\n",
       "      <td>66.182219</td>\n",
       "      <td>66.754477</td>\n",
       "      <td>68.685723</td>\n",
       "      <td>66.060666</td>\n",
       "      <td>57.569109</td>\n",
       "      <td>48.952580</td>\n",
       "      <td>42.147395</td>\n",
       "      <td>43.985951</td>\n",
       "      <td>51.856475</td>\n",
       "      <td>45.225807</td>\n",
       "      <td>29.529071</td>\n",
       "      <td>17.396211</td>\n",
       "      <td>9.263115</td>\n",
       "      <td>2.131444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</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>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.00000</td>\n",
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       "      <td>0.00000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <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>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>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>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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24999.750000</td>\n",
       "      <td>1.000000</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>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>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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>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>0.000000</td>\n",
       "      <td>...</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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>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>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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39999.500000</td>\n",
       "      <td>2.000000</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>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>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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>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>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</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>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>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>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>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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54999.250000</td>\n",
       "      <td>3.000000</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>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>49.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>20.000000</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>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>23.000000</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>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>239.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>185.00000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>230.00000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 786 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Id         Label  ...           783           784\n",
       "count  60000.000000  60000.000000  ...  60000.000000  60000.000000\n",
       "mean   39999.500000      2.002933  ...      0.832950      0.072850\n",
       "std    17320.652413      1.415000  ...      9.263115      2.131444\n",
       "min    10000.000000      0.000000  ...      0.000000      0.000000\n",
       "25%    24999.750000      1.000000  ...      0.000000      0.000000\n",
       "50%    39999.500000      2.000000  ...      0.000000      0.000000\n",
       "75%    54999.250000      3.000000  ...      0.000000      0.000000\n",
       "max    69999.000000      4.000000  ...    255.000000    170.000000\n",
       "\n",
       "[8 rows x 786 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('testX.csv')\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "OXMYYGO8RvbV",
    "outputId": "5e6eac4e-2f68-4127-93d6-82d4362bc3ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 1, 2, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "ASARiWKdR1eS",
    "outputId": "17e9cc80-9429-4557-c600-c36265aa9328"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6000, 28, 28, 1)\n",
      "[[0. 1. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " ...\n",
      " [0. 1. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1024\n",
    "num_classes = 5\n",
    "epochs = 300\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "x_train = train.drop(['Label', 'Id'], axis=1).to_numpy()\n",
    "y_train = train['Label'].to_numpy()\n",
    "x_test = test.drop(['Id'], axis = 1).to_numpy()\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size = 0.1, random_state = 42)\n",
    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "x_val = x_val.reshape(x_val.shape[0], img_rows, img_cols, 1)\n",
    "input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "y_train = tensorflow.keras.utils.to_categorical(y_train, num_classes)\n",
    "y_val = tensorflow.keras.utils.to_categorical(y_val, num_classes)\n",
    "\n",
    "print(x_val.shape)\n",
    "print(y_val)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_val = x_val.astype('float32')\n",
    "\n",
    "x_train /= 255\n",
    "x_val /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 952
    },
    "colab_type": "code",
    "id": "ECvlGUOifJ8d",
    "outputId": "ef6d4b1e-afbc-462a-e787-9b7d9b7342c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 28, 28, 16)   416         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 28, 28, 16)   6416        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 14, 14, 16)   0           conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 14, 14, 32)   544         max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 14, 14, 16)   272         max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 14, 14, 16)   272         max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 14, 14, 16)   272         max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 14, 14, 16)   4624        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 14, 14, 16)   6416        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 14, 14, 16)   0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 14, 14, 64)   0           conv2d_12[0][0]                  \n",
      "                                                                 conv2d_14[0][0]                  \n",
      "                                                                 conv2d_16[0][0]                  \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 14, 14, 64)   36928       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 14, 14, 64)   36928       conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 14, 14, 64)   0           conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 7, 7, 64)     0           dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 64)           0           max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1024)         66560       global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 1024)         0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 256)          262400      dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 256)          0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 5)            1285        dropout_5[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 423,333\n",
      "Trainable params: 423,333\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Inception\n",
    "\n",
    "inputs =  Input(shape = (28,28,1))\n",
    "x = Conv2D(16, kernel_size=(5,5), activation='relu', padding = 'same')(inputs)\n",
    "x = Conv2D(16, kernel_size=(5,5), activation='relu', padding = 'same')(x)\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.4))\n",
    "\n",
    "x = MaxPooling2D(pool_size=(2,2))(x)\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.4))\n",
    "\n",
    "a1 = Conv2D(16, kernel_size=(1,1), activation='relu', padding='same')(x)\n",
    "a2 = Conv2D(32, kernel_size=(1,1), activation='relu')(x)\n",
    "a2 = Conv2D(16, kernel_size=(3,3), activation='relu', padding='same')(a2)\n",
    "a3 = Conv2D(16, kernel_size=(1,1), activation='relu', padding='same')(x)\n",
    "a3 = Conv2D(16,kernel_size=(5,5), activation='relu', padding='same')(a3)\n",
    "a4 = Conv2D(16, kernel_size=(1,1), activation='relu', padding='same')(x)\n",
    "a4 = MaxPooling2D(pool_size = (3,3), strides=(1,1), padding='same')(a4)\n",
    "x = concatenate([a1,a2,a3,a4], axis=-1)\n",
    "\n",
    "x = Conv2D(64, kernel_size=(3,3), activation='relu', padding = 'same')(x)\n",
    "x = Conv2D(64, kernel_size=(3,3), activation='relu', padding = 'same')(x)\n",
    "#model.add(BatchNormalization())\n",
    "x = Dropout(0.4)(x)\n",
    "x = MaxPooling2D(pool_size=(2,2))(x)\n",
    "\n",
    "#model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.4))\n",
    "\n",
    "#model.add(Conv2D(64, kernel_size=(1,1), activation='relu', padding = 'same'))\n",
    "\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "x = Dropout(0.25)(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(0.25)(x)\n",
    "pred = Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs = inputs, outputs = pred)\n",
    "\n",
    "#model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer = Adam(lr=0.0008), metrics = ['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "UTMdIZhk2ENk",
    "outputId": "f59be993-b61f-45cf-f6bf-42544a0210f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "53/53 [==============================] - 7s 125ms/step - loss: 1.2499 - accuracy: 0.4066 - val_loss: 1.1030 - val_accuracy: 0.5193\n",
      "Epoch 2/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.9425 - accuracy: 0.5846 - val_loss: 0.9428 - val_accuracy: 0.5745\n",
      "Epoch 3/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.7756 - accuracy: 0.6731 - val_loss: 0.7850 - val_accuracy: 0.6863\n",
      "Epoch 4/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.6848 - accuracy: 0.7171 - val_loss: 0.6627 - val_accuracy: 0.7492\n",
      "Epoch 5/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.6317 - accuracy: 0.7419 - val_loss: 0.6158 - val_accuracy: 0.7698\n",
      "Epoch 6/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.6094 - accuracy: 0.7486 - val_loss: 0.6146 - val_accuracy: 0.7792\n",
      "Epoch 7/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.5954 - accuracy: 0.7529 - val_loss: 0.6139 - val_accuracy: 0.7522\n",
      "Epoch 8/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.5407 - accuracy: 0.7789 - val_loss: 0.5514 - val_accuracy: 0.7917\n",
      "Epoch 9/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.5241 - accuracy: 0.7868 - val_loss: 0.5973 - val_accuracy: 0.7498\n",
      "Epoch 10/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.5018 - accuracy: 0.7979 - val_loss: 0.5028 - val_accuracy: 0.8220\n",
      "Epoch 11/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.4801 - accuracy: 0.8079 - val_loss: 0.4893 - val_accuracy: 0.8218\n",
      "Epoch 12/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.4767 - accuracy: 0.8077 - val_loss: 0.4811 - val_accuracy: 0.8328\n",
      "Epoch 13/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.4705 - accuracy: 0.8144 - val_loss: 0.4772 - val_accuracy: 0.8220\n",
      "Epoch 14/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.4412 - accuracy: 0.8266 - val_loss: 0.4787 - val_accuracy: 0.8195\n",
      "Epoch 15/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.4394 - accuracy: 0.8271 - val_loss: 0.4500 - val_accuracy: 0.8462\n",
      "Epoch 16/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.4258 - accuracy: 0.8346 - val_loss: 0.4637 - val_accuracy: 0.8293\n",
      "Epoch 17/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.4236 - accuracy: 0.8342 - val_loss: 0.5079 - val_accuracy: 0.7902\n",
      "Epoch 18/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.4298 - accuracy: 0.8288 - val_loss: 0.4497 - val_accuracy: 0.8390\n",
      "Epoch 19/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.4150 - accuracy: 0.8366 - val_loss: 0.4348 - val_accuracy: 0.8388\n",
      "Epoch 20/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.4091 - accuracy: 0.8392 - val_loss: 0.4089 - val_accuracy: 0.8528\n",
      "Epoch 21/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.4036 - accuracy: 0.8415 - val_loss: 0.4332 - val_accuracy: 0.8357\n",
      "Epoch 22/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3867 - accuracy: 0.8488 - val_loss: 0.4293 - val_accuracy: 0.8363\n",
      "Epoch 23/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3854 - accuracy: 0.8478 - val_loss: 0.4788 - val_accuracy: 0.7998\n",
      "Epoch 24/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3744 - accuracy: 0.8515 - val_loss: 0.4386 - val_accuracy: 0.8265\n",
      "Epoch 25/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3919 - accuracy: 0.8446 - val_loss: 0.5144 - val_accuracy: 0.7768\n",
      "Epoch 26/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3782 - accuracy: 0.8477 - val_loss: 0.4044 - val_accuracy: 0.8570\n",
      "Epoch 27/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3652 - accuracy: 0.8560 - val_loss: 0.4153 - val_accuracy: 0.8443\n",
      "Epoch 28/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3651 - accuracy: 0.8555 - val_loss: 0.4276 - val_accuracy: 0.8265\n",
      "Epoch 29/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3519 - accuracy: 0.8628 - val_loss: 0.3833 - val_accuracy: 0.8653\n",
      "Epoch 30/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3639 - accuracy: 0.8572 - val_loss: 0.4102 - val_accuracy: 0.8375\n",
      "Epoch 31/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3428 - accuracy: 0.8666 - val_loss: 0.3589 - val_accuracy: 0.8672\n",
      "Epoch 32/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3551 - accuracy: 0.8595 - val_loss: 0.3925 - val_accuracy: 0.8437\n",
      "Epoch 33/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3349 - accuracy: 0.8675 - val_loss: 0.4237 - val_accuracy: 0.8268\n",
      "Epoch 34/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3451 - accuracy: 0.8649 - val_loss: 0.3796 - val_accuracy: 0.8623\n",
      "Epoch 35/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3274 - accuracy: 0.8712 - val_loss: 0.3700 - val_accuracy: 0.8630\n",
      "Epoch 36/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3215 - accuracy: 0.8733 - val_loss: 0.3800 - val_accuracy: 0.8500\n",
      "Epoch 37/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3317 - accuracy: 0.8682 - val_loss: 0.3714 - val_accuracy: 0.8588\n",
      "Epoch 38/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3250 - accuracy: 0.8715 - val_loss: 0.4686 - val_accuracy: 0.8023\n",
      "Epoch 39/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3334 - accuracy: 0.8673 - val_loss: 0.3699 - val_accuracy: 0.8552\n",
      "Epoch 40/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3259 - accuracy: 0.8696 - val_loss: 0.4662 - val_accuracy: 0.8033\n",
      "Epoch 41/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3236 - accuracy: 0.8705 - val_loss: 0.3953 - val_accuracy: 0.8412\n",
      "Epoch 42/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3182 - accuracy: 0.8736 - val_loss: 0.4576 - val_accuracy: 0.8058\n",
      "Epoch 43/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.3268 - accuracy: 0.8702 - val_loss: 0.3896 - val_accuracy: 0.8475\n",
      "Epoch 44/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3043 - accuracy: 0.8783 - val_loss: 0.3755 - val_accuracy: 0.8465\n",
      "Epoch 45/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.3306 - accuracy: 0.8667 - val_loss: 0.3718 - val_accuracy: 0.8528\n",
      "Epoch 46/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2972 - accuracy: 0.8827 - val_loss: 0.3471 - val_accuracy: 0.8618\n",
      "Epoch 47/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2956 - accuracy: 0.8816 - val_loss: 0.3882 - val_accuracy: 0.8375\n",
      "Epoch 48/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2908 - accuracy: 0.8839 - val_loss: 0.3693 - val_accuracy: 0.8453\n",
      "Epoch 49/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2906 - accuracy: 0.8857 - val_loss: 0.3723 - val_accuracy: 0.8508\n",
      "Epoch 50/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2971 - accuracy: 0.8828 - val_loss: 0.4232 - val_accuracy: 0.8188\n",
      "Epoch 51/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2880 - accuracy: 0.8859 - val_loss: 0.3463 - val_accuracy: 0.8625\n",
      "Epoch 52/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2840 - accuracy: 0.8872 - val_loss: 0.4451 - val_accuracy: 0.8162\n",
      "Epoch 53/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2998 - accuracy: 0.8800 - val_loss: 0.3806 - val_accuracy: 0.8430\n",
      "Epoch 54/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2757 - accuracy: 0.8915 - val_loss: 0.3447 - val_accuracy: 0.8637\n",
      "Epoch 55/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2894 - accuracy: 0.8841 - val_loss: 0.3608 - val_accuracy: 0.8563\n",
      "Epoch 56/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2860 - accuracy: 0.8869 - val_loss: 0.3457 - val_accuracy: 0.8633\n",
      "Epoch 57/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2812 - accuracy: 0.8892 - val_loss: 0.3935 - val_accuracy: 0.8438\n",
      "Epoch 58/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2869 - accuracy: 0.8855 - val_loss: 0.4426 - val_accuracy: 0.8107\n",
      "Epoch 59/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2731 - accuracy: 0.8924 - val_loss: 0.3448 - val_accuracy: 0.8625\n",
      "Epoch 60/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2822 - accuracy: 0.8884 - val_loss: 0.3899 - val_accuracy: 0.8348\n",
      "Epoch 61/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2774 - accuracy: 0.8895 - val_loss: 0.3205 - val_accuracy: 0.8757\n",
      "Epoch 62/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2802 - accuracy: 0.8871 - val_loss: 0.4124 - val_accuracy: 0.8353\n",
      "Epoch 63/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2657 - accuracy: 0.8955 - val_loss: 0.3866 - val_accuracy: 0.8390\n",
      "Epoch 64/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2653 - accuracy: 0.8943 - val_loss: 0.3345 - val_accuracy: 0.8667\n",
      "Epoch 65/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2783 - accuracy: 0.8887 - val_loss: 0.3120 - val_accuracy: 0.8915\n",
      "Epoch 66/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2664 - accuracy: 0.8933 - val_loss: 0.3504 - val_accuracy: 0.8557\n",
      "Epoch 67/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2633 - accuracy: 0.8952 - val_loss: 0.3442 - val_accuracy: 0.8645\n",
      "Epoch 68/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2573 - accuracy: 0.8974 - val_loss: 0.3346 - val_accuracy: 0.8692\n",
      "Epoch 69/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2635 - accuracy: 0.8950 - val_loss: 0.3229 - val_accuracy: 0.8708\n",
      "Epoch 70/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2639 - accuracy: 0.8928 - val_loss: 0.3906 - val_accuracy: 0.8408\n",
      "Epoch 71/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2652 - accuracy: 0.8934 - val_loss: 0.3718 - val_accuracy: 0.8435\n",
      "Epoch 72/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2614 - accuracy: 0.8966 - val_loss: 0.3862 - val_accuracy: 0.8490\n",
      "Epoch 73/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2488 - accuracy: 0.9010 - val_loss: 0.3875 - val_accuracy: 0.8380\n",
      "Epoch 74/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2560 - accuracy: 0.8971 - val_loss: 0.3260 - val_accuracy: 0.8665\n",
      "Epoch 75/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2581 - accuracy: 0.8982 - val_loss: 0.3798 - val_accuracy: 0.8443\n",
      "Epoch 76/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2510 - accuracy: 0.8994 - val_loss: 0.3226 - val_accuracy: 0.8698\n",
      "Epoch 77/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2429 - accuracy: 0.9021 - val_loss: 0.3468 - val_accuracy: 0.8592\n",
      "Epoch 78/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2569 - accuracy: 0.8969 - val_loss: 0.3683 - val_accuracy: 0.8457\n",
      "Epoch 79/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2449 - accuracy: 0.9013 - val_loss: 0.4063 - val_accuracy: 0.8328\n",
      "Epoch 80/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2446 - accuracy: 0.9031 - val_loss: 0.3828 - val_accuracy: 0.8418\n",
      "Epoch 81/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2436 - accuracy: 0.9013 - val_loss: 0.3256 - val_accuracy: 0.8637\n",
      "Epoch 82/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2413 - accuracy: 0.9046 - val_loss: 0.3898 - val_accuracy: 0.8368\n",
      "Epoch 83/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2392 - accuracy: 0.9041 - val_loss: 0.2924 - val_accuracy: 0.8870\n",
      "Epoch 84/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2396 - accuracy: 0.9039 - val_loss: 0.3038 - val_accuracy: 0.8748\n",
      "Epoch 85/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2388 - accuracy: 0.9045 - val_loss: 0.3196 - val_accuracy: 0.8747\n",
      "Epoch 86/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2452 - accuracy: 0.9021 - val_loss: 0.3370 - val_accuracy: 0.8597\n",
      "Epoch 87/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2315 - accuracy: 0.9064 - val_loss: 0.3242 - val_accuracy: 0.8675\n",
      "Epoch 88/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2345 - accuracy: 0.9073 - val_loss: 0.3263 - val_accuracy: 0.8660\n",
      "Epoch 89/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2378 - accuracy: 0.9040 - val_loss: 0.3043 - val_accuracy: 0.8758\n",
      "Epoch 90/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2452 - accuracy: 0.9024 - val_loss: 0.3216 - val_accuracy: 0.8712\n",
      "Epoch 91/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2245 - accuracy: 0.9101 - val_loss: 0.3685 - val_accuracy: 0.8528\n",
      "Epoch 92/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2317 - accuracy: 0.9063 - val_loss: 0.3325 - val_accuracy: 0.8678\n",
      "Epoch 93/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2190 - accuracy: 0.9124 - val_loss: 0.3122 - val_accuracy: 0.8778\n",
      "Epoch 94/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2444 - accuracy: 0.9018 - val_loss: 0.2881 - val_accuracy: 0.8957\n",
      "Epoch 95/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2420 - accuracy: 0.9021 - val_loss: 0.4320 - val_accuracy: 0.8313\n",
      "Epoch 96/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2366 - accuracy: 0.9032 - val_loss: 0.2889 - val_accuracy: 0.8833\n",
      "Epoch 97/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2274 - accuracy: 0.9083 - val_loss: 0.3224 - val_accuracy: 0.8668\n",
      "Epoch 98/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2276 - accuracy: 0.9090 - val_loss: 0.2941 - val_accuracy: 0.8867\n",
      "Epoch 99/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2205 - accuracy: 0.9108 - val_loss: 0.3345 - val_accuracy: 0.8663\n",
      "Epoch 100/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2249 - accuracy: 0.9100 - val_loss: 0.3025 - val_accuracy: 0.8848\n",
      "Epoch 101/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2282 - accuracy: 0.9098 - val_loss: 0.4183 - val_accuracy: 0.8322\n",
      "Epoch 102/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2277 - accuracy: 0.9096 - val_loss: 0.2930 - val_accuracy: 0.8830\n",
      "Epoch 103/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2303 - accuracy: 0.9067 - val_loss: 0.3359 - val_accuracy: 0.8657\n",
      "Epoch 104/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2375 - accuracy: 0.9028 - val_loss: 0.3297 - val_accuracy: 0.8710\n",
      "Epoch 105/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2167 - accuracy: 0.9118 - val_loss: 0.2857 - val_accuracy: 0.8965\n",
      "Epoch 106/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2205 - accuracy: 0.9116 - val_loss: 0.3551 - val_accuracy: 0.8495\n",
      "Epoch 107/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2294 - accuracy: 0.9076 - val_loss: 0.3730 - val_accuracy: 0.8462\n",
      "Epoch 108/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2100 - accuracy: 0.9158 - val_loss: 0.3106 - val_accuracy: 0.8767\n",
      "Epoch 109/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2129 - accuracy: 0.9145 - val_loss: 0.3415 - val_accuracy: 0.8543\n",
      "Epoch 110/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2218 - accuracy: 0.9102 - val_loss: 0.3817 - val_accuracy: 0.8497\n",
      "Epoch 111/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2180 - accuracy: 0.9126 - val_loss: 0.2921 - val_accuracy: 0.8950\n",
      "Epoch 112/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2329 - accuracy: 0.9068 - val_loss: 0.3139 - val_accuracy: 0.8680\n",
      "Epoch 113/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2100 - accuracy: 0.9159 - val_loss: 0.4256 - val_accuracy: 0.8297\n",
      "Epoch 114/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2097 - accuracy: 0.9162 - val_loss: 0.3000 - val_accuracy: 0.8785\n",
      "Epoch 115/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2159 - accuracy: 0.9124 - val_loss: 0.4043 - val_accuracy: 0.8427\n",
      "Epoch 116/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2065 - accuracy: 0.9175 - val_loss: 0.3384 - val_accuracy: 0.8588\n",
      "Epoch 117/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2068 - accuracy: 0.9170 - val_loss: 0.4082 - val_accuracy: 0.8360\n",
      "Epoch 118/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2174 - accuracy: 0.9126 - val_loss: 0.3302 - val_accuracy: 0.8650\n",
      "Epoch 119/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2082 - accuracy: 0.9164 - val_loss: 0.3126 - val_accuracy: 0.8682\n",
      "Epoch 120/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2145 - accuracy: 0.9138 - val_loss: 0.2957 - val_accuracy: 0.8842\n",
      "Epoch 121/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2029 - accuracy: 0.9179 - val_loss: 0.2936 - val_accuracy: 0.8853\n",
      "Epoch 122/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2156 - accuracy: 0.9142 - val_loss: 0.3255 - val_accuracy: 0.8627\n",
      "Epoch 123/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2137 - accuracy: 0.9137 - val_loss: 0.3437 - val_accuracy: 0.8608\n",
      "Epoch 124/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2012 - accuracy: 0.9200 - val_loss: 0.3070 - val_accuracy: 0.8818\n",
      "Epoch 125/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2056 - accuracy: 0.9173 - val_loss: 0.3437 - val_accuracy: 0.8603\n",
      "Epoch 126/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2014 - accuracy: 0.9209 - val_loss: 0.3492 - val_accuracy: 0.8587\n",
      "Epoch 127/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.1960 - accuracy: 0.9209 - val_loss: 0.3699 - val_accuracy: 0.8558\n",
      "Epoch 128/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2113 - accuracy: 0.9130 - val_loss: 0.3213 - val_accuracy: 0.8725\n",
      "Epoch 129/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1967 - accuracy: 0.9216 - val_loss: 0.3429 - val_accuracy: 0.8672\n",
      "Epoch 130/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1968 - accuracy: 0.9204 - val_loss: 0.3332 - val_accuracy: 0.8688\n",
      "Epoch 131/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2067 - accuracy: 0.9166 - val_loss: 0.2829 - val_accuracy: 0.8923\n",
      "Epoch 132/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2010 - accuracy: 0.9185 - val_loss: 0.2826 - val_accuracy: 0.8838\n",
      "Epoch 133/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1953 - accuracy: 0.9222 - val_loss: 0.3251 - val_accuracy: 0.8747\n",
      "Epoch 134/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2118 - accuracy: 0.9133 - val_loss: 0.3828 - val_accuracy: 0.8403\n",
      "Epoch 135/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1941 - accuracy: 0.9209 - val_loss: 0.3234 - val_accuracy: 0.8712\n",
      "Epoch 136/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.1885 - accuracy: 0.9230 - val_loss: 0.3230 - val_accuracy: 0.8705\n",
      "Epoch 137/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2058 - accuracy: 0.9174 - val_loss: 0.3463 - val_accuracy: 0.8602\n",
      "Epoch 138/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2060 - accuracy: 0.9164 - val_loss: 0.2866 - val_accuracy: 0.8825\n",
      "Epoch 139/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1921 - accuracy: 0.9215 - val_loss: 0.3361 - val_accuracy: 0.8722\n",
      "Epoch 140/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1978 - accuracy: 0.9188 - val_loss: 0.3578 - val_accuracy: 0.8565\n",
      "Epoch 141/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1879 - accuracy: 0.9252 - val_loss: 0.3248 - val_accuracy: 0.8758\n",
      "Epoch 142/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.2057 - accuracy: 0.9161 - val_loss: 0.4412 - val_accuracy: 0.8287\n",
      "Epoch 143/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.1962 - accuracy: 0.9200 - val_loss: 0.3285 - val_accuracy: 0.8723\n",
      "Epoch 144/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1847 - accuracy: 0.9260 - val_loss: 0.4217 - val_accuracy: 0.8478\n",
      "Epoch 145/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1936 - accuracy: 0.9220 - val_loss: 0.3129 - val_accuracy: 0.8733\n",
      "Epoch 146/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1837 - accuracy: 0.9262 - val_loss: 0.3298 - val_accuracy: 0.8765\n",
      "Epoch 147/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1769 - accuracy: 0.9297 - val_loss: 0.2849 - val_accuracy: 0.8845\n",
      "Epoch 148/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1888 - accuracy: 0.9236 - val_loss: 0.3238 - val_accuracy: 0.8722\n",
      "Epoch 149/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1866 - accuracy: 0.9242 - val_loss: 0.4679 - val_accuracy: 0.8225\n",
      "Epoch 150/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1891 - accuracy: 0.9241 - val_loss: 0.3412 - val_accuracy: 0.8715\n",
      "Epoch 151/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1910 - accuracy: 0.9236 - val_loss: 0.3648 - val_accuracy: 0.8472\n",
      "Epoch 152/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1811 - accuracy: 0.9266 - val_loss: 0.3200 - val_accuracy: 0.8767\n",
      "Epoch 153/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1831 - accuracy: 0.9262 - val_loss: 0.3409 - val_accuracy: 0.8738\n",
      "Epoch 154/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1822 - accuracy: 0.9274 - val_loss: 0.3826 - val_accuracy: 0.8557\n",
      "Epoch 155/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1857 - accuracy: 0.9244 - val_loss: 0.4090 - val_accuracy: 0.8497\n",
      "Epoch 156/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.2030 - accuracy: 0.9169 - val_loss: 0.2758 - val_accuracy: 0.8970\n",
      "Epoch 157/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1967 - accuracy: 0.9206 - val_loss: 0.3331 - val_accuracy: 0.8720\n",
      "Epoch 158/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1731 - accuracy: 0.9308 - val_loss: 0.3206 - val_accuracy: 0.8745\n",
      "Epoch 159/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1708 - accuracy: 0.9319 - val_loss: 0.3450 - val_accuracy: 0.8695\n",
      "Epoch 160/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1857 - accuracy: 0.9258 - val_loss: 0.3172 - val_accuracy: 0.8762\n",
      "Epoch 161/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1815 - accuracy: 0.9271 - val_loss: 0.4129 - val_accuracy: 0.8355\n",
      "Epoch 162/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1742 - accuracy: 0.9301 - val_loss: 0.3263 - val_accuracy: 0.8753\n",
      "Epoch 163/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1800 - accuracy: 0.9271 - val_loss: 0.3451 - val_accuracy: 0.8637\n",
      "Epoch 164/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1727 - accuracy: 0.9303 - val_loss: 0.3513 - val_accuracy: 0.8708\n",
      "Epoch 165/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1644 - accuracy: 0.9345 - val_loss: 0.3369 - val_accuracy: 0.8703\n",
      "Epoch 166/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1742 - accuracy: 0.9298 - val_loss: 0.3656 - val_accuracy: 0.8650\n",
      "Epoch 167/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1816 - accuracy: 0.9275 - val_loss: 0.3874 - val_accuracy: 0.8540\n",
      "Epoch 168/300\n",
      "53/53 [==============================] - 6s 116ms/step - loss: 0.1727 - accuracy: 0.9299 - val_loss: 0.4118 - val_accuracy: 0.8432\n",
      "Epoch 169/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1847 - accuracy: 0.9244 - val_loss: 0.4053 - val_accuracy: 0.8457\n",
      "Epoch 170/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1699 - accuracy: 0.9317 - val_loss: 0.3335 - val_accuracy: 0.8703\n",
      "Epoch 171/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1609 - accuracy: 0.9359 - val_loss: 0.3970 - val_accuracy: 0.8558\n",
      "Epoch 172/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1660 - accuracy: 0.9335 - val_loss: 0.4146 - val_accuracy: 0.8492\n",
      "Epoch 173/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1743 - accuracy: 0.9302 - val_loss: 0.3175 - val_accuracy: 0.8840\n",
      "Epoch 174/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1716 - accuracy: 0.9305 - val_loss: 0.3987 - val_accuracy: 0.8408\n",
      "Epoch 175/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1803 - accuracy: 0.9268 - val_loss: 0.3753 - val_accuracy: 0.8592\n",
      "Epoch 176/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1544 - accuracy: 0.9390 - val_loss: 0.2939 - val_accuracy: 0.8855\n",
      "Epoch 177/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1790 - accuracy: 0.9271 - val_loss: 0.2985 - val_accuracy: 0.8853\n",
      "Epoch 178/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1808 - accuracy: 0.9264 - val_loss: 0.4199 - val_accuracy: 0.8328\n",
      "Epoch 179/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1641 - accuracy: 0.9348 - val_loss: 0.4339 - val_accuracy: 0.8428\n",
      "Epoch 180/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1613 - accuracy: 0.9352 - val_loss: 0.3762 - val_accuracy: 0.8567\n",
      "Epoch 181/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1657 - accuracy: 0.9339 - val_loss: 0.2757 - val_accuracy: 0.8907\n",
      "Epoch 182/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1668 - accuracy: 0.9334 - val_loss: 0.3478 - val_accuracy: 0.8652\n",
      "Epoch 183/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1607 - accuracy: 0.9358 - val_loss: 0.3216 - val_accuracy: 0.8790\n",
      "Epoch 184/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1534 - accuracy: 0.9386 - val_loss: 0.3815 - val_accuracy: 0.8637\n",
      "Epoch 185/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1714 - accuracy: 0.9314 - val_loss: 0.3396 - val_accuracy: 0.8682\n",
      "Epoch 186/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1508 - accuracy: 0.9409 - val_loss: 0.3492 - val_accuracy: 0.8695\n",
      "Epoch 187/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1782 - accuracy: 0.9276 - val_loss: 0.3779 - val_accuracy: 0.8545\n",
      "Epoch 188/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1661 - accuracy: 0.9320 - val_loss: 0.3330 - val_accuracy: 0.8748\n",
      "Epoch 189/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1554 - accuracy: 0.9376 - val_loss: 0.3752 - val_accuracy: 0.8647\n",
      "Epoch 190/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1469 - accuracy: 0.9412 - val_loss: 0.3761 - val_accuracy: 0.8678\n",
      "Epoch 191/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1467 - accuracy: 0.9413 - val_loss: 0.4653 - val_accuracy: 0.8398\n",
      "Epoch 192/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1497 - accuracy: 0.9399 - val_loss: 0.3378 - val_accuracy: 0.8742\n",
      "Epoch 193/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1565 - accuracy: 0.9368 - val_loss: 0.4861 - val_accuracy: 0.8320\n",
      "Epoch 194/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1687 - accuracy: 0.9318 - val_loss: 0.3447 - val_accuracy: 0.8705\n",
      "Epoch 195/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1540 - accuracy: 0.9382 - val_loss: 0.3172 - val_accuracy: 0.8800\n",
      "Epoch 196/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1553 - accuracy: 0.9391 - val_loss: 0.3680 - val_accuracy: 0.8658\n",
      "Epoch 197/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1586 - accuracy: 0.9359 - val_loss: 0.3890 - val_accuracy: 0.8603\n",
      "Epoch 198/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1731 - accuracy: 0.9311 - val_loss: 0.3114 - val_accuracy: 0.8677\n",
      "Epoch 199/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1567 - accuracy: 0.9370 - val_loss: 0.3756 - val_accuracy: 0.8685\n",
      "Epoch 200/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1431 - accuracy: 0.9430 - val_loss: 0.3033 - val_accuracy: 0.8887\n",
      "Epoch 201/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1589 - accuracy: 0.9360 - val_loss: 0.3670 - val_accuracy: 0.8665\n",
      "Epoch 202/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1458 - accuracy: 0.9412 - val_loss: 0.3603 - val_accuracy: 0.8683\n",
      "Epoch 203/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1450 - accuracy: 0.9423 - val_loss: 0.3224 - val_accuracy: 0.8787\n",
      "Epoch 204/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1476 - accuracy: 0.9408 - val_loss: 0.3007 - val_accuracy: 0.8845\n",
      "Epoch 205/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1395 - accuracy: 0.9438 - val_loss: 0.3895 - val_accuracy: 0.8558\n",
      "Epoch 206/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1582 - accuracy: 0.9359 - val_loss: 0.3616 - val_accuracy: 0.8645\n",
      "Epoch 207/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1439 - accuracy: 0.9421 - val_loss: 0.3356 - val_accuracy: 0.8795\n",
      "Epoch 208/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1425 - accuracy: 0.9431 - val_loss: 0.3446 - val_accuracy: 0.8737\n",
      "Epoch 209/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1379 - accuracy: 0.9461 - val_loss: 0.4226 - val_accuracy: 0.8585\n",
      "Epoch 210/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1397 - accuracy: 0.9444 - val_loss: 0.3613 - val_accuracy: 0.8677\n",
      "Epoch 211/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1877 - accuracy: 0.9237 - val_loss: 0.3264 - val_accuracy: 0.8772\n",
      "Epoch 212/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1461 - accuracy: 0.9409 - val_loss: 0.3962 - val_accuracy: 0.8603\n",
      "Epoch 213/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1335 - accuracy: 0.9475 - val_loss: 0.2994 - val_accuracy: 0.8920\n",
      "Epoch 214/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1351 - accuracy: 0.9464 - val_loss: 0.3549 - val_accuracy: 0.8692\n",
      "Epoch 215/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1341 - accuracy: 0.9478 - val_loss: 0.3583 - val_accuracy: 0.8657\n",
      "Epoch 216/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1476 - accuracy: 0.9416 - val_loss: 0.3603 - val_accuracy: 0.8740\n",
      "Epoch 217/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1337 - accuracy: 0.9471 - val_loss: 0.3164 - val_accuracy: 0.8843\n",
      "Epoch 218/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1424 - accuracy: 0.9438 - val_loss: 0.4610 - val_accuracy: 0.8338\n",
      "Epoch 219/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1502 - accuracy: 0.9399 - val_loss: 0.4027 - val_accuracy: 0.8627\n",
      "Epoch 220/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1362 - accuracy: 0.9452 - val_loss: 0.2934 - val_accuracy: 0.8945\n",
      "Epoch 221/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1383 - accuracy: 0.9443 - val_loss: 0.3708 - val_accuracy: 0.8703\n",
      "Epoch 222/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1355 - accuracy: 0.9463 - val_loss: 0.4422 - val_accuracy: 0.8520\n",
      "Epoch 223/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1496 - accuracy: 0.9396 - val_loss: 0.3325 - val_accuracy: 0.8783\n",
      "Epoch 224/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1341 - accuracy: 0.9462 - val_loss: 0.3557 - val_accuracy: 0.8688\n",
      "Epoch 225/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1408 - accuracy: 0.9431 - val_loss: 0.3548 - val_accuracy: 0.8663\n",
      "Epoch 226/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1337 - accuracy: 0.9473 - val_loss: 0.3832 - val_accuracy: 0.8598\n",
      "Epoch 227/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1327 - accuracy: 0.9482 - val_loss: 0.5023 - val_accuracy: 0.8338\n",
      "Epoch 228/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1521 - accuracy: 0.9387 - val_loss: 0.3884 - val_accuracy: 0.8672\n",
      "Epoch 229/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1336 - accuracy: 0.9463 - val_loss: 0.3605 - val_accuracy: 0.8735\n",
      "Epoch 230/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1260 - accuracy: 0.9503 - val_loss: 0.4080 - val_accuracy: 0.8662\n",
      "Epoch 231/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1365 - accuracy: 0.9460 - val_loss: 0.3863 - val_accuracy: 0.8662\n",
      "Epoch 232/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1332 - accuracy: 0.9471 - val_loss: 0.3076 - val_accuracy: 0.8878\n",
      "Epoch 233/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1346 - accuracy: 0.9456 - val_loss: 0.4128 - val_accuracy: 0.8598\n",
      "Epoch 234/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1414 - accuracy: 0.9425 - val_loss: 0.4642 - val_accuracy: 0.8450\n",
      "Epoch 235/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1315 - accuracy: 0.9474 - val_loss: 0.3720 - val_accuracy: 0.8708\n",
      "Epoch 236/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1197 - accuracy: 0.9528 - val_loss: 0.3713 - val_accuracy: 0.8768\n",
      "Epoch 237/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1295 - accuracy: 0.9483 - val_loss: 0.3228 - val_accuracy: 0.8863\n",
      "Epoch 238/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1211 - accuracy: 0.9526 - val_loss: 0.4693 - val_accuracy: 0.8475\n",
      "Epoch 239/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1353 - accuracy: 0.9454 - val_loss: 0.3061 - val_accuracy: 0.8852\n",
      "Epoch 240/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1221 - accuracy: 0.9522 - val_loss: 0.3863 - val_accuracy: 0.8678\n",
      "Epoch 241/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1146 - accuracy: 0.9556 - val_loss: 0.3081 - val_accuracy: 0.8893\n",
      "Epoch 242/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1174 - accuracy: 0.9532 - val_loss: 0.3417 - val_accuracy: 0.8813\n",
      "Epoch 243/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1224 - accuracy: 0.9516 - val_loss: 0.3179 - val_accuracy: 0.8883\n",
      "Epoch 244/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1381 - accuracy: 0.9444 - val_loss: 0.3443 - val_accuracy: 0.8700\n",
      "Epoch 245/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1203 - accuracy: 0.9516 - val_loss: 0.3709 - val_accuracy: 0.8712\n",
      "Epoch 246/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1237 - accuracy: 0.9512 - val_loss: 0.4545 - val_accuracy: 0.8482\n",
      "Epoch 247/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1267 - accuracy: 0.9494 - val_loss: 0.3273 - val_accuracy: 0.8863\n",
      "Epoch 248/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1265 - accuracy: 0.9497 - val_loss: 0.4291 - val_accuracy: 0.8680\n",
      "Epoch 249/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1242 - accuracy: 0.9509 - val_loss: 0.3861 - val_accuracy: 0.8685\n",
      "Epoch 250/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1245 - accuracy: 0.9493 - val_loss: 0.3651 - val_accuracy: 0.8750\n",
      "Epoch 251/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1135 - accuracy: 0.9551 - val_loss: 0.4790 - val_accuracy: 0.8503\n",
      "Epoch 252/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1208 - accuracy: 0.9515 - val_loss: 0.3688 - val_accuracy: 0.8742\n",
      "Epoch 253/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1243 - accuracy: 0.9523 - val_loss: 0.3267 - val_accuracy: 0.8818\n",
      "Epoch 254/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1277 - accuracy: 0.9481 - val_loss: 0.3533 - val_accuracy: 0.8785\n",
      "Epoch 255/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1184 - accuracy: 0.9516 - val_loss: 0.3959 - val_accuracy: 0.8717\n",
      "Epoch 256/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1141 - accuracy: 0.9547 - val_loss: 0.4003 - val_accuracy: 0.8713\n",
      "Epoch 257/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1129 - accuracy: 0.9554 - val_loss: 0.4324 - val_accuracy: 0.8627\n",
      "Epoch 258/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1206 - accuracy: 0.9516 - val_loss: 0.3953 - val_accuracy: 0.8632\n",
      "Epoch 259/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1100 - accuracy: 0.9565 - val_loss: 0.4054 - val_accuracy: 0.8643\n",
      "Epoch 260/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1087 - accuracy: 0.9575 - val_loss: 0.4788 - val_accuracy: 0.8567\n",
      "Epoch 261/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1229 - accuracy: 0.9498 - val_loss: 0.4297 - val_accuracy: 0.8615\n",
      "Epoch 262/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1074 - accuracy: 0.9571 - val_loss: 0.4398 - val_accuracy: 0.8627\n",
      "Epoch 263/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1044 - accuracy: 0.9587 - val_loss: 0.3907 - val_accuracy: 0.8782\n",
      "Epoch 264/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1053 - accuracy: 0.9580 - val_loss: 0.3759 - val_accuracy: 0.8762\n",
      "Epoch 265/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1082 - accuracy: 0.9574 - val_loss: 0.2976 - val_accuracy: 0.8945\n",
      "Epoch 266/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1209 - accuracy: 0.9514 - val_loss: 0.4578 - val_accuracy: 0.8525\n",
      "Epoch 267/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1155 - accuracy: 0.9539 - val_loss: 0.3941 - val_accuracy: 0.8698\n",
      "Epoch 268/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1145 - accuracy: 0.9548 - val_loss: 0.2882 - val_accuracy: 0.8947\n",
      "Epoch 269/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1055 - accuracy: 0.9589 - val_loss: 0.4361 - val_accuracy: 0.8625\n",
      "Epoch 270/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1101 - accuracy: 0.9570 - val_loss: 0.4056 - val_accuracy: 0.8707\n",
      "Epoch 271/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1101 - accuracy: 0.9567 - val_loss: 0.3654 - val_accuracy: 0.8807\n",
      "Epoch 272/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1121 - accuracy: 0.9559 - val_loss: 0.3958 - val_accuracy: 0.8748\n",
      "Epoch 273/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1127 - accuracy: 0.9549 - val_loss: 0.4146 - val_accuracy: 0.8620\n",
      "Epoch 274/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1079 - accuracy: 0.9565 - val_loss: 0.3196 - val_accuracy: 0.8915\n",
      "Epoch 275/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1117 - accuracy: 0.9549 - val_loss: 0.3949 - val_accuracy: 0.8732\n",
      "Epoch 276/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0957 - accuracy: 0.9621 - val_loss: 0.3852 - val_accuracy: 0.8740\n",
      "Epoch 277/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0960 - accuracy: 0.9626 - val_loss: 0.4461 - val_accuracy: 0.8630\n",
      "Epoch 278/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1139 - accuracy: 0.9540 - val_loss: 0.4774 - val_accuracy: 0.8542\n",
      "Epoch 279/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0967 - accuracy: 0.9625 - val_loss: 0.5067 - val_accuracy: 0.8552\n",
      "Epoch 280/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1116 - accuracy: 0.9560 - val_loss: 0.3248 - val_accuracy: 0.8915\n",
      "Epoch 281/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1165 - accuracy: 0.9531 - val_loss: 0.3463 - val_accuracy: 0.8858\n",
      "Epoch 282/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1088 - accuracy: 0.9577 - val_loss: 0.3418 - val_accuracy: 0.8828\n",
      "Epoch 283/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1017 - accuracy: 0.9607 - val_loss: 0.4311 - val_accuracy: 0.8690\n",
      "Epoch 284/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.1047 - accuracy: 0.9584 - val_loss: 0.3299 - val_accuracy: 0.8867\n",
      "Epoch 285/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.0969 - accuracy: 0.9619 - val_loss: 0.4026 - val_accuracy: 0.8780\n",
      "Epoch 286/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1032 - accuracy: 0.9584 - val_loss: 0.4006 - val_accuracy: 0.8728\n",
      "Epoch 287/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1006 - accuracy: 0.9608 - val_loss: 0.4522 - val_accuracy: 0.8623\n",
      "Epoch 288/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0984 - accuracy: 0.9615 - val_loss: 0.3627 - val_accuracy: 0.8810\n",
      "Epoch 289/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0953 - accuracy: 0.9622 - val_loss: 0.3505 - val_accuracy: 0.8850\n",
      "Epoch 290/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1034 - accuracy: 0.9590 - val_loss: 0.4814 - val_accuracy: 0.8465\n",
      "Epoch 291/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1032 - accuracy: 0.9601 - val_loss: 0.4584 - val_accuracy: 0.8577\n",
      "Epoch 292/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1023 - accuracy: 0.9597 - val_loss: 0.3330 - val_accuracy: 0.8832\n",
      "Epoch 293/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1093 - accuracy: 0.9566 - val_loss: 0.3852 - val_accuracy: 0.8760\n",
      "Epoch 294/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.0944 - accuracy: 0.9632 - val_loss: 0.5337 - val_accuracy: 0.8482\n",
      "Epoch 295/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1039 - accuracy: 0.9590 - val_loss: 0.3625 - val_accuracy: 0.8748\n",
      "Epoch 296/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0936 - accuracy: 0.9626 - val_loss: 0.4048 - val_accuracy: 0.8753\n",
      "Epoch 297/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0910 - accuracy: 0.9645 - val_loss: 0.4432 - val_accuracy: 0.8652\n",
      "Epoch 298/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.1001 - accuracy: 0.9606 - val_loss: 0.5396 - val_accuracy: 0.8550\n",
      "Epoch 299/300\n",
      "53/53 [==============================] - 6s 117ms/step - loss: 0.0943 - accuracy: 0.9627 - val_loss: 0.3899 - val_accuracy: 0.8763\n",
      "Epoch 300/300\n",
      "53/53 [==============================] - 6s 118ms/step - loss: 0.0917 - accuracy: 0.9641 - val_loss: 0.4069 - val_accuracy: 0.8765\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train,\n",
    "          batch_size = batch_size,\n",
    "          epochs = epochs,\n",
    "          verbose = 1,\n",
    "          validation_data = (x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "9-uRXol-1YqU",
    "outputId": "740ed8e2-ed1f-4bde-8c4d-a958ed14c5db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken for testing data:  0.9785771369934082\n",
      "Validation Accuracy:  [0.4083390235900879, 0.8765000104904175]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start = time.time()\n",
    "score = model.evaluate(x_val, y_val, verbose = 0)\n",
    "end = time.time()\n",
    "print(\"Time taken for testing data: \", end-start)\n",
    "print(\"Validation Accuracy: \", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "rwQa0EpWHto-",
    "outputId": "7a053f5f-d30f-4096-907d-81963ea16edd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+/ftLkvTZZ58JIZKTky0tLS0sLLKysl555RVra2sXF5cvv/yyYfyDBw/i4+OHDBlibm7et29fV1fX+Pj4R71H+Pr16+bm5q6urg1VrV27dsiQIWZmZjY2Nu+//36r10FL+/fvt7a2jouLa2HMCy+8MGzYsO+++67xDeDff/99dXX11KlTmwzW1RdaX18vhDAzM1M2DfFba0FQUJAQYt++fa2OBAAAALRHQxMAgPYIDQ19//33ZVnesGHD5cuXb968OWHChFOnTkVERJw6daq0tPTNN99cu3Ztbm6uMr64uDggIKCwsPDGjRtWVlbz588XQgQFBR0/ftzCwmLWrFl+fn7e3t7z58/ftGnTtm3bWi0gODg4KCiouro6JCSksLDw5MmT9fX1L774YsONwDExMREREVFRUSUlJQcPHiwqKho/fnxxcbE2RxsbN25c44czLl269L333lOr1b17905PTy8oKHBzc1u4cGHDoyfXrFnz//7f/1u7dm1paenXX39dU1Nja2tra2v7cHJ1dfWBAwcWLlxoamqq7ImOjl6+fPmiRYuKi4tv3ry5YsUKLb8ORUREhJ2dnampqaurq5+f3/HjxxsOKa+31mg0LScsXrxYCNF4ReGf/vSnsLCwh0fq6gtVXjE0atQoZdMQv7UWeHl5CSEuXbrU6kgAAACgDWQAAPAQIUR6enrLY1auXCmEqKysVDb/+te/CiHOnj2rbB47dkwIkZaW9vAH4+PjhRAlJSXK5n/+538KIbZs2bJt27awsDDti1y0aJHyJhaF0r9btWqVLMvV1dVWVlaBgYENR5V6YmNjWz0qy/LFixeFEBs2bFA2lSbpp59+qmxGRUUJIdRqtbK5fv16IUR+fr6yOXr06Oeee64h+Z133unVq5fy+MImoqKiBg8eXFFRoWxWV1dbWFi8+OKLDQOUJYSnTp3S5mpcvXr15MmTlZWV9+/f/+GHH55++mlzc/Nz585p81lZlt3d3S9fvlxWVmZpaWlnZ1ddXS3LckFBgYuLy/379ysrK4UQU6ZMafazbfpCG96fU1VVtWPHDkdHRwcHh2vXrsmG+a01OamHKU/VbPZQY+np6T3k71Jtfrfg8eC7AADAcBk/rsYpAADdnLJgTbmDWAhhYmIihHj4ldkNh5Q1g0KId9555+9///vixYt//etfd+T9y97e3hYWFso9yHl5eVVVVd7e3g1HR48ebWpqqtyT3vLRtlJOvOFMa2pqVCpVw9EHDx6YmJg8/KaOXbt2ZWRkfP31171791b25OfnV1dXT5kypR01CCGefPLJJ598UvnvMWPGpKamenl5rV+/Pjk5WfsQGxubefPmbd68OS0t7Xe/+926deuWLl1qampaW1vbwqfa+oWWl5dLkmRkZNSvX79p06atXLlSeemQIX5rLbt3754sy9bW1lpWlZGRoeVIg/bDDz/ouwQAAADDRkMTAIDHYc+ePWvXrs3Ly6uoqHi4yxkXF7djx46SkpIOzmJmZnbr1i0hRFlZmRDCysqq8VFbW1tlpWHLRzto2rRpa9euzcrKmjp1al5eXmZm5m9+85smrbG0tLSEhITs7OwnnniiYee1a9eEEPb29h2vQQjh6elpZGT0z3/+s60fXLp06ebNmzdu3Dh79uzt27f//PPPzQ7ryBdqY2OjfAVNGOK31jLl+g8dOlTL8QEBAW0q20AlJiYmJibquwoAAAADxjM0AQDodFevXp09e3a/fv1ycnLKy8vXrFnT+GhdXV1ISEhCQsIPP/zw0UcftXuWurq6srIyFxcXIYTy9MMmrS4tj3ZQTEzMCy+8EBQUZG1t/eqrr/r7+2/evLnxgE8//XTLli0HDhxo0hdTVgjev3+/4zUIITQajUajaXjZjva8vLzGjBlz7NixRYsWzZkzx87O7uExnfSFGuK31rL9+/cLIV555RUtx+v71qXHQXCbc5eh/U8yAADoalihCQBApzt79mxdXd3SpUvd3NyEEJIkNT76b//2bwsXLnz11VevX7/+4YcfTp061cfHpx2zZGdny7I8ZswYIcTIkSOtrKxOnDjRcDQnJ6e2tvbZZ59t9WgH5eXlFRQU3Lp1y9i46Z8ZsiyvWLHi7t27mZmZDx8dOXJkr169/vGPfyxZsqQd87700kvKq8YVx48fl2W5fVdy6dKlR4/+f/buPa6qKn38+NrKgcPhbgIyEMbFCyJGpRWkpTk5k47XFLBx5sc0ldcB1DEvaF4K0mzELyr1sohuEqD2BR3FaawozVSacsQAACAASURBVElTU1HTBMUbIxgqFznIgbN/f+xvZxguhzuHA5/3X+691n72s9bZyuFx7b2/27Fjh/JUyrra6QM1x0/NiBs3bsTFxXl4eLzwwgutzxAAAAAwYIUmAADtztPTUwixf//+ioqKCxcu1Hzo4ZYtW9zd3adMmSKEiI2NHTRo0O9///uSkpImRtbr9bdv366qqjp16lRUVJSnp2d4eLgQQq1WL1y48LPPPvvkk09KSkqys7Nnz57t5uY2c+bMRltbad68eZ6enmVlZXWbzp49++abb7777rsqlUqq4a233hJCODs7P/fcczt27EhMTCwpKTl16tTWrVubft7r16+npKTcuXNHp9P961//evHFFz09PQ210czMTHt7+5iYmKaECgkJ6d279+TJk5V6ZV3t9IGa46dmIMtyWVmZXq+XZfnmzZupqalPPPFEz54909PTm/4MTQAAAKBJTHmbBwAAnZVo7LbQjRs3ajQaIcQDDzxw4MCBtWvXOjg4CCFcXV23bduWkpLi6uoqhHBycvr0009lWV68eHGvXr0cHR2nTZu2efNmIYSPj09gYKAkSb169Tp06JAsy/Pnz+/Ro4cQwsHB4ejRo40mOXPmTJVK5e7ubmFhYW9vP2nSpNzcXEOrXq9fv359v379VCqVk5PT5MmTz58/35TWv/3tb0ryNjY2U6ZM2bRpU58+fYQQGo1mwoQJW7ZsUQber1+/3NzcrVu3KuWqvn37/vTTT7Isf/nll/fdd5/hm4ZKpfLz89u5c6csy9nZ2fV+G1m/fr1y6tLS0hdffPG+++6ztbUdPnz4q6++KoTw8PA4efJko7OxcOFCHx8fGxsbCwsLDw+Pl156KT8/39C6d+9eOzu7119/ve6Bn332mY+PjxCid+/e8+bNU3a+8soryociy/Ly5cuVGejRo8egQYMOHDjQsg/022+/7d+/vzJkNze3adOm1U3G7D61Xbt2DRkyRKPRWFpaKoNVXmv+6KOPrlmzpqioqNEPTsFbztHx+CwAADBfkszjYwAAqEOSpNTU1JCQEFMnYsysWbO2b99eVFRk6kT+S0JCwoULF+Li4pTNysrKJUuWJCQk3L5929ra2rS5oSEm/9TS0tJCQ0O7w/dSs/i3pZvgswAAwHzxDE0AAMxYdXW1qVP4Lzdu3IiIiDhx4oRhj6Wlpaenp06n0+l0FDQ7Jz41AAAAmBeeoQkAQGd07tw5qWFhYWGmTrB+1tbWKpUqMTGxoKBAp9Pl5+e/9957r776alhYWGsepGims2Eu2ulTAwAAANoJBU0AADqjgQMHGnlkTEpKyrJly5KSkoqLi728vHbs2GHqfP+Pg4PD559/fvr06f79+1tbWw8aNCgpKWnt2rUffvhha8I2OhttlX/31E6fGtrb/v37ly5dqtfrJ0+e7OnpqVar3d3dJ06ceOrUqaYcPnLkyLr/PWBra9vexwohdDpdbGysr6+vpaWlo6Pj4MGD8/Ly6narqKgYOHDg8uXLlc1du3atW7eusy1LBwAAJsEt5wAAmKXY2NjY2FhTZ1GPESNG/POf/zR1FmgePjWzs3Llyh9++GHbtm16vf7AgQPp6ekPP/xwQUHBzJkzn3zyybNnz/7qV79qQdjhw4e3OKWmHxsaGnr27Nlt27Y98sgjN2/enDVrVllZWd1u0dHR58+fN2xOmDDh0qVLo0ePTk9Pd3R0bHGeAACgC2CFJgAAALo+rVYbHBzc2UK1zNq1a1NSUtLS0uzs7IQQQUFBw4cP12g0Xl5eMTExxcXFH3zwQaNB1Gp1SUlJzcXOM2fOfOWVV5qSQGuOTUlJSU9P3759+2OPPWZhYeHm5paRkTF48OBa3Q4dOnT69OlaOyMjIx988MGxY8dWVVU15VwAAKCroqAJAACAri8xMbGwsLCzhWqBnJycFStWrF69Wq1WCyEsLCx2795taPX29hZC5ObmNhpn3759Sj1UcfXq1dOnTz/99NNNyaE1x7799tsPP/xwQECAkT5arXbRokUbN26s27Rq1aoTJ07U2wQAALoPCpoAAAAwD7Isb9iwwc/Pz8rKysnJadKkSefOnVOaIiIiLC0t+/Tpo2zOnTvXxsZGkqSff/5ZCBEVFbVw4cLc3FxJknx9fePj49VqtYuLy6xZs9zc3NRqdXBw8OHDh1sQSgixb98+e3v7mJiYjpmE+Ph4WZYnTJhQb6tWqxVCtOBtTmvXro2MjGxZSk0/trKy8rvvvgsMDDTeLTo6eu7cuc7OznWbnJycnnrqqY0bN8qy3JJcAQBAl0BBEwAAAOZh1apVS5cujY6OLiws/Oabb65evTpixIiCggIhRHx8fEhIiKHnli1bVq9ebdjcuHHj+PHjfXx8ZFnOycmJiIgIDw8vLy+PjIzMy8s7fvx4VVXVM888c/Xq1eaGEkIor6nR6/XtPwFCCLFnz54BAwZoNJp6W48cOSKa/yjM69evZ2VlPffccy3Ip1nH5ufnV1ZWHjt2bNSoUUop2c/Pb8uWLTWrk99++21ubu7zzz/fUJCHHnro+vXrJ0+ebEG2AACga6CgCQAAADOg1Wo3bNgwZcqUGTNmODg4BAQEvPPOOz///PPWrVtbFtDCwkJZ7Dlo0KCEhITS0tKkpKQWxBk3blxJScmKFStalkaz3L1799KlSz4+PnWbCgoKUlJSIiMjg4KCGlq/2ZC1a9f+5S9/6dGjJb8aNOtY5eU/zs7OMTE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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.keras.utils.plot_model(\n",
    "    model, to_file='model_inception.png', show_shapes=True, show_layer_names=True,\n",
    "    rankdir='TB', expand_nested=False, dpi=96\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "2Ur5Vqz91Hy8",
    "outputId": "3f78f98f-5734-486d-e5ec-7998ec679568"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.876500\n",
      "Precision: 0.876500\n",
      "Recall: 0.876500\n",
      "F1 score: 0.876500\n",
      "Cohens kappa: 0.845643\n",
      "ROC AUC: 0.985427\n",
      "[[[4765   45]\n",
      "  [  45 1145]]\n",
      "\n",
      " [[4603  167]\n",
      "  [ 117 1113]]\n",
      "\n",
      " [[4542  276]\n",
      "  [ 133 1049]]\n",
      "\n",
      " [[4596  229]\n",
      "  [ 222  953]]\n",
      "\n",
      " [[4753   24]\n",
      "  [ 224  999]]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "\n",
    "# predict probabilities for test set\n",
    "yhat_probs = model.predict(x_val, verbose=0)\n",
    "\n",
    "yhat_classes = yhat_probs.argmax(axis=-1)\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(y_val.argmax(axis=-1), yhat_classes)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(y_val.argmax(axis=-1), yhat_classes, average = 'micro')\n",
    "print('Precision: %f' % precision)\n",
    "\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(y_val.argmax(axis=-1), yhat_classes, average = 'micro')\n",
    "print('Recall: %f' % recall)\n",
    "\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(y_val.argmax(axis=-1), yhat_classes, average = 'micro')\n",
    "print('F1 score: %f' % f1)\n",
    " \n",
    "# kappa\n",
    "kappa = cohen_kappa_score(y_val.argmax(axis=-1), yhat_classes)\n",
    "print('Cohens kappa: %f' % kappa)\n",
    "\n",
    "# ROC AUC\n",
    "auc = roc_auc_score(y_val, yhat_probs)\n",
    "print('ROC AUC: %f' % auc)\n",
    "\n",
    "matrix = multilabel_confusion_matrix(y_val.argmax(axis=-1), yhat_classes, labels = [0,1,2,3,4])\n",
    "print(matrix)\n",
    "\n",
    "#Reference:\n",
    "#https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Jphwc8huk8VL",
    "outputId": "5d99dafb-ae96-4bd2-e19d-82caa06e8bbd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams['xtick.labelsize'] = 17\n",
    "#plt.rcParams['ytick.labelsize'] = 17\n",
    "#plt.rcParams['xlabel.fontsize'] = 17\n",
    "plt.rcParams['font.size'] = 17\n",
    "# list all data in history\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "z9j3XGEmPp_p",
    "outputId": "92dc40f1-f444-45c7-cc5a-95984e3f9ce6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 2, ..., 3, 1, 2])"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = test.drop(['Id'], axis = 1).to_numpy()\n",
    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "x_test = x_test.astype('float32')\n",
    "x_test /= 255\n",
    "y_sol = model.predict(x_test)\n",
    "y_sol = y_sol.argmax(axis=-1)\n",
    "y_sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zPgbP2uCIhOc"
   },
   "outputs": [],
   "source": [
    "df_solution = pd.DataFrame(np.arange(0, x_test.shape[0]), columns=['Id'])\n",
    "df_solution['Label'] = y_sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "E9dHWSjNY9GK",
    "outputId": "ab56118e-b542-4c82-b4c7-e2a9e472b1fb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9997</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9998</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>9999</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  Label\n",
       "0        0      3\n",
       "1        1      4\n",
       "2        2      2\n",
       "3        3      1\n",
       "4        4      0\n",
       "...    ...    ...\n",
       "9995  9995      1\n",
       "9996  9996      1\n",
       "9997  9997      3\n",
       "9998  9998      1\n",
       "9999  9999      2\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D2kT7_d6G2o-"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Inception Assignment-3 Part-2.ipynb",
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