6200 lines
1.9 MiB
6200 lines
1.9 MiB
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
|
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
|
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"# Install Package Needed"
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],
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"metadata": {
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"id": "MmZad6grpLF8"
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}
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},
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{
|
||
"cell_type": "code",
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"source": [
|
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"!pip install keras_tuner"
|
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],
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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},
|
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"id": "5mV_EHY2pKrq",
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"outputId": "c3905100-48f0-447d-cea9-4e84d107ca6c"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Collecting keras_tuner\n",
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" Downloading keras_tuner-1.4.7-py3-none-any.whl.metadata (5.4 kB)\n",
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"Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (from keras_tuner) (3.8.0)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from keras_tuner) (24.2)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from keras_tuner) (2.32.3)\n",
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"Collecting kt-legacy (from keras_tuner)\n",
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" Downloading kt_legacy-1.0.5-py3-none-any.whl.metadata (221 bytes)\n",
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"Requirement already satisfied: absl-py in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (1.4.0)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (2.0.2)\n",
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"Requirement already satisfied: rich in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (13.9.4)\n",
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"Requirement already satisfied: namex in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (0.0.8)\n",
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"Requirement already satisfied: h5py in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (3.13.0)\n",
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"Requirement already satisfied: optree in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (0.15.0)\n",
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"Requirement already satisfied: ml-dtypes in /usr/local/lib/python3.11/dist-packages (from keras->keras_tuner) (0.4.1)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->keras_tuner) (3.4.1)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->keras_tuner) (3.10)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->keras_tuner) (2.3.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->keras_tuner) (2025.1.31)\n",
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"Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.11/dist-packages (from optree->keras->keras_tuner) (4.13.1)\n",
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"Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras->keras_tuner) (3.0.0)\n",
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"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from rich->keras->keras_tuner) (2.18.0)\n",
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"Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich->keras->keras_tuner) (0.1.2)\n",
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"Downloading keras_tuner-1.4.7-py3-none-any.whl (129 kB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.1/129.1 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hDownloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n",
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"Installing collected packages: kt-legacy, keras_tuner\n",
|
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"Successfully installed keras_tuner-1.4.7 kt-legacy-1.0.5\n"
|
||
]
|
||
}
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Get Times New Roman Font"
|
||
],
|
||
"metadata": {
|
||
"id": "0cdkxdiaRSAF"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import matplotlib as mpl\n",
|
||
"import matplotlib.font_manager as fm\n",
|
||
"print(mpl.__version__)\n",
|
||
"\n",
|
||
"!wget -O TimesNewRoman.ttf https://github.com/justrajdeep/fonts/raw/master/Times%20New%20Roman.ttf\n",
|
||
"font_dirs = [\"/content/\"]\n",
|
||
"font_files = fm.findSystemFonts(fontpaths=font_dirs, fontext='ttf')\n",
|
||
"for font_file in font_files:\n",
|
||
" print(font_file) if 'TimesNewRoman' in font_file else None\n",
|
||
" fm.fontManager.addfont(font_file)"
|
||
],
|
||
"metadata": {
|
||
"id": "3HJhcAseRReR",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"outputId": "e44fe6f9-6a88-4153-c013-abfa70293bff"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"3.10.0\n",
|
||
"--2025-04-16 00:58:12-- https://github.com/justrajdeep/fonts/raw/master/Times%20New%20Roman.ttf\n",
|
||
"Resolving github.com (github.com)... 140.82.116.4\n",
|
||
"Connecting to github.com (github.com)|140.82.116.4|:443... connected.\n",
|
||
"HTTP request sent, awaiting response... 302 Found\n",
|
||
"Location: https://raw.githubusercontent.com/justrajdeep/fonts/master/Times%20New%20Roman.ttf [following]\n",
|
||
"--2025-04-16 00:58:12-- https://raw.githubusercontent.com/justrajdeep/fonts/master/Times%20New%20Roman.ttf\n",
|
||
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
|
||
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
|
||
"HTTP request sent, awaiting response... 200 OK\n",
|
||
"Length: 834452 (815K) [application/octet-stream]\n",
|
||
"Saving to: ‘TimesNewRoman.ttf’\n",
|
||
"\n",
|
||
"TimesNewRoman.ttf 100%[===================>] 814.89K --.-KB/s in 0.03s \n",
|
||
"\n",
|
||
"2025-04-16 00:58:12 (23.2 MB/s) - ‘TimesNewRoman.ttf’ saved [834452/834452]\n",
|
||
"\n",
|
||
"/content/TimesNewRoman.ttf\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "o4xT26dnowt7"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import tensorflow as tf\n",
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"from sklearn.model_selection import StratifiedKFold\n",
|
||
"from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Conv1D, GlobalAveragePooling1D, Input, MaxPooling1D, Flatten\n",
|
||
"from tensorflow.keras import Sequential\n",
|
||
"from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
|
||
"from tensorflow.keras.regularizers import l2\n",
|
||
"from keras.callbacks import CSVLogger\n",
|
||
"from keras_tuner import HyperParameters, RandomSearch, GridSearch\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from sklearn.metrics import classification_report"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"tf.random.set_seed(42)\n",
|
||
"df = pd.read_excel(\"Coffe Brewing Level - Spectroscopy.xlsx\")\n",
|
||
"sample = df.sample(20)\n",
|
||
"sample.head(20)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 696
|
||
},
|
||
"id": "CkgAfu71paiV",
|
||
"outputId": "a797708b-1d60-43c5-caf4-ea56a5071fa3"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" 410nm 435nm 460nm 485nm 510nm 535nm 560nm \\\n",
|
||
"107 4.229255 0.966277 1.861446 0.885403 3.017060 11.156102 1.307779 \n",
|
||
"50 10.150212 1.932554 5.584338 0.885403 2.262795 8.367077 0.871853 \n",
|
||
"48 5.920957 1.932554 3.722892 0.885403 2.262795 9.761589 0.871853 \n",
|
||
"101 4.229255 0.966277 1.861446 0.885403 3.771325 11.156102 1.307779 \n",
|
||
"22 10.150212 2.898831 6.515061 0.885403 2.262795 7.669820 0.871853 \n",
|
||
"119 10.150212 2.898831 6.515061 1.770806 3.017060 5.578051 1.307779 \n",
|
||
"0 11.841914 2.898831 7.445784 1.770806 3.017060 6.972564 0.435926 \n",
|
||
"139 5.920957 1.932554 3.722892 0.885403 3.017060 11.156102 0.871853 \n",
|
||
"11 10.150212 1.932554 5.584338 0.885403 3.017060 11.853358 1.307779 \n",
|
||
"140 6.766808 1.932554 2.792169 0.885403 1.508530 6.275308 0.871853 \n",
|
||
"6 11.841914 2.898831 7.445784 1.770806 3.017060 6.972564 0.435926 \n",
|
||
"85 13.533616 3.865108 9.307230 2.656209 3.771325 5.578051 0.871853 \n",
|
||
"147 5.920957 0.966277 2.792169 0.885403 2.262795 7.669820 1.307779 \n",
|
||
"144 5.920957 1.932554 2.792169 0.885403 2.262795 8.367077 0.871853 \n",
|
||
"111 10.150212 2.898831 6.515061 1.770806 3.017060 5.578051 1.307779 \n",
|
||
"95 9.304361 2.898831 6.515061 1.770806 3.017060 5.578051 1.743706 \n",
|
||
"113 10.150212 2.898831 6.515061 1.770806 3.017060 5.578051 1.307779 \n",
|
||
"141 6.766808 1.932554 2.792169 0.885403 1.508530 6.275308 0.871853 \n",
|
||
"87 13.533616 3.865108 9.307230 2.656209 3.771325 5.578051 0.871853 \n",
|
||
"18 10.150212 1.932554 5.584338 0.885403 3.017060 11.853358 1.307779 \n",
|
||
"\n",
|
||
" 585nm 610nm 645nm 680nm 705nm 730nm 760nm \\\n",
|
||
"107 0.824 2.197717 0.746666 2.010596 0.353923 0.747948 0.781082 \n",
|
||
"50 0.824 2.197717 0.746666 1.005298 0.000000 0.747948 0.000000 \n",
|
||
"48 1.236 2.197717 0.746666 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"101 0.824 2.197717 0.746666 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"22 0.824 5.494293 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"119 1.648 4.395434 1.119999 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"0 0.824 3.296576 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"139 0.824 3.296576 0.373333 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"11 1.648 3.296576 1.119999 1.005298 0.353923 1.495895 0.781082 \n",
|
||
"140 0.824 3.296576 0.746666 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"6 0.824 3.296576 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"85 1.236 6.593152 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"147 1.236 2.197717 0.746666 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"144 1.236 2.197717 0.746666 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"111 1.648 4.395434 1.119999 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"95 1.648 5.494293 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"113 1.648 4.395434 1.119999 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"141 1.236 3.296576 0.746666 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"87 1.236 6.593152 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"18 1.648 3.296576 1.119999 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"\n",
|
||
" 810nm 860nm 900nm 940nm Label_Temp Label \n",
|
||
"107 0.827006 2.199664 0.512009 0 Underdeveloped_Cold Underdeveloped \n",
|
||
"50 0.827006 1.099832 0.512009 0 Ideal_Warm Ideal \n",
|
||
"48 0.827006 1.099832 0.512009 0 Ideal_Cold Ideal \n",
|
||
"101 0.827006 2.199664 0.512009 0 Underdeveloped_Cold Underdeveloped \n",
|
||
"22 0.827006 2.199664 0.512009 0 Bitter_Warm Bitter \n",
|
||
"119 0.827006 1.099832 0.512009 0 Underdeveloped_Warm Underdeveloped \n",
|
||
"0 0.827006 1.099832 0.512009 0 Bitter Bitter \n",
|
||
"139 0.827006 1.099832 0.512009 0 Weak_Cold Weak \n",
|
||
"11 0.827006 1.099832 0.512009 0 Bitter_Cold Bitter \n",
|
||
"140 0.827006 1.099832 0.512009 0 Weak_Warm Weak \n",
|
||
"6 0.827006 1.099832 0.512009 0 Bitter Bitter \n",
|
||
"85 0.827006 1.099832 0.512009 0 Strong_Warm Strong \n",
|
||
"147 0.827006 1.099832 0.512009 0 Weak_Warm Weak \n",
|
||
"144 0.827006 1.099832 0.512009 0 Weak_Warm Weak \n",
|
||
"111 0.827006 1.099832 0.512009 0 Underdeveloped_Warm Underdeveloped \n",
|
||
"95 0.827006 1.099832 0.512009 0 Underdeveloped Underdeveloped \n",
|
||
"113 0.827006 1.099832 0.512009 0 Underdeveloped_Warm Underdeveloped \n",
|
||
"141 0.827006 1.099832 0.512009 0 Weak_Warm Weak \n",
|
||
"87 0.827006 1.099832 0.512009 0 Strong_Warm Strong \n",
|
||
"18 0.827006 1.099832 0.512009 0 Bitter_Cold Bitter "
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-e6281fd9-9179-43d0-bab1-32c43360f5d0\" class=\"colab-df-container\">\n",
|
||
" <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>410nm</th>\n",
|
||
" <th>435nm</th>\n",
|
||
" <th>460nm</th>\n",
|
||
" <th>485nm</th>\n",
|
||
" <th>510nm</th>\n",
|
||
" <th>535nm</th>\n",
|
||
" <th>560nm</th>\n",
|
||
" <th>585nm</th>\n",
|
||
" <th>610nm</th>\n",
|
||
" <th>645nm</th>\n",
|
||
" <th>680nm</th>\n",
|
||
" <th>705nm</th>\n",
|
||
" <th>730nm</th>\n",
|
||
" <th>760nm</th>\n",
|
||
" <th>810nm</th>\n",
|
||
" <th>860nm</th>\n",
|
||
" <th>900nm</th>\n",
|
||
" <th>940nm</th>\n",
|
||
" <th>Label_Temp</th>\n",
|
||
" <th>Label</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>107</th>\n",
|
||
" <td>4.229255</td>\n",
|
||
" <td>0.966277</td>\n",
|
||
" <td>1.861446</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>11.156102</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>2.197717</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>2.010596</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>2.199664</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Underdeveloped_Cold</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>5.584338</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>2.262795</td>\n",
|
||
" <td>8.367077</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>2.197717</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Ideal_Warm</td>\n",
|
||
" <td>Ideal</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>48</th>\n",
|
||
" <td>5.920957</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>3.722892</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>2.262795</td>\n",
|
||
" <td>9.761589</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>1.236</td>\n",
|
||
" <td>2.197717</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Ideal_Cold</td>\n",
|
||
" <td>Ideal</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>101</th>\n",
|
||
" <td>4.229255</td>\n",
|
||
" <td>0.966277</td>\n",
|
||
" <td>1.861446</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>3.771325</td>\n",
|
||
" <td>11.156102</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>2.197717</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>2.199664</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Underdeveloped_Cold</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>6.515061</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>2.262795</td>\n",
|
||
" <td>7.669820</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>5.494293</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>2.199664</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter_Warm</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>119</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>6.515061</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>5.578051</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>1.648</td>\n",
|
||
" <td>4.395434</td>\n",
|
||
" <td>1.119999</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Underdeveloped_Warm</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>139</th>\n",
|
||
" <td>5.920957</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>3.722892</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>11.156102</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.373333</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Weak_Cold</td>\n",
|
||
" <td>Weak</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>5.584338</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>11.853358</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>1.648</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>1.119999</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>1.495895</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter_Cold</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>140</th>\n",
|
||
" <td>6.766808</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>2.792169</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>1.508530</td>\n",
|
||
" <td>6.275308</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Weak_Warm</td>\n",
|
||
" <td>Weak</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>85</th>\n",
|
||
" <td>13.533616</td>\n",
|
||
" <td>3.865108</td>\n",
|
||
" <td>9.307230</td>\n",
|
||
" <td>2.656209</td>\n",
|
||
" <td>3.771325</td>\n",
|
||
" <td>5.578051</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>1.236</td>\n",
|
||
" <td>6.593152</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Strong_Warm</td>\n",
|
||
" <td>Strong</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>147</th>\n",
|
||
" <td>5.920957</td>\n",
|
||
" <td>0.966277</td>\n",
|
||
" <td>2.792169</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>2.262795</td>\n",
|
||
" <td>7.669820</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>1.236</td>\n",
|
||
" <td>2.197717</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Weak_Warm</td>\n",
|
||
" <td>Weak</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>144</th>\n",
|
||
" <td>5.920957</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>2.792169</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>2.262795</td>\n",
|
||
" <td>8.367077</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>1.236</td>\n",
|
||
" <td>2.197717</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Weak_Warm</td>\n",
|
||
" <td>Weak</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>111</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>6.515061</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>5.578051</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>1.648</td>\n",
|
||
" <td>4.395434</td>\n",
|
||
" <td>1.119999</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Underdeveloped_Warm</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>95</th>\n",
|
||
" <td>9.304361</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>6.515061</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>5.578051</td>\n",
|
||
" <td>1.743706</td>\n",
|
||
" <td>1.648</td>\n",
|
||
" <td>5.494293</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>113</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>6.515061</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>5.578051</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>1.648</td>\n",
|
||
" <td>4.395434</td>\n",
|
||
" <td>1.119999</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Underdeveloped_Warm</td>\n",
|
||
" <td>Underdeveloped</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>141</th>\n",
|
||
" <td>6.766808</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>2.792169</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>1.508530</td>\n",
|
||
" <td>6.275308</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>1.236</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Weak_Warm</td>\n",
|
||
" <td>Weak</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>87</th>\n",
|
||
" <td>13.533616</td>\n",
|
||
" <td>3.865108</td>\n",
|
||
" <td>9.307230</td>\n",
|
||
" <td>2.656209</td>\n",
|
||
" <td>3.771325</td>\n",
|
||
" <td>5.578051</td>\n",
|
||
" <td>0.871853</td>\n",
|
||
" <td>1.236</td>\n",
|
||
" <td>6.593152</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Strong_Warm</td>\n",
|
||
" <td>Strong</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>10.150212</td>\n",
|
||
" <td>1.932554</td>\n",
|
||
" <td>5.584338</td>\n",
|
||
" <td>0.885403</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>11.853358</td>\n",
|
||
" <td>1.307779</td>\n",
|
||
" <td>1.648</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>1.119999</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter_Cold</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6281fd9-9179-43d0-bab1-32c43360f5d0')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-e6281fd9-9179-43d0-bab1-32c43360f5d0 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-e6281fd9-9179-43d0-bab1-32c43360f5d0');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-0c4676fa-3a3b-4ce9-8cb5-75be638c7c4b\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0c4676fa-3a3b-4ce9-8cb5-75be638c7c4b')\"\n",
|
||
" title=\"Suggest charts\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-0c4676fa-3a3b-4ce9-8cb5-75be638c7c4b button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"variable_name": "sample",
|
||
"summary": "{\n \"name\": \"sample\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"410nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.8974824155768553,\n \"min\": 4.229255199432373,\n \"max\": 13.533616065979,\n \"num_unique_values\": 8,\n \"samples\": [\n 10.15021228790283,\n 11.84191417694092,\n 4.229255199432373\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"435nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.8528167470782404,\n \"min\": 0.9662770628929138,\n \"max\": 3.865108251571655,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.932554125785828,\n 3.865108251571655,\n 2.898831129074097\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"460nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.3248472281953485,\n \"min\": 1.861446022987366,\n \"max\": 9.30722999572754,\n \"num_unique_values\": 7,\n \"samples\": [\n 1.861446022987366,\n 5.584338188171387,\n 2.792169094085693\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"485nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6093761352142737,\n \"min\": 0.8854029774665833,\n \"max\": 2.656208992004395,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8854029774665833,\n 1.770805954933167,\n 2.656208992004395\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"510nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6520656173869126,\n \"min\": 1.508529901504517,\n \"max\": 3.771324872970581,\n \"num_unique_values\": 4,\n \"samples\": [\n 2.262794971466064,\n 1.508529901504517,\n 3.017059803009033\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"535nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.3661340542801206,\n \"min\": 5.578051090240479,\n \"max\": 11.85335826873779,\n \"num_unique_values\": 8,\n \"samples\": [\n 8.367076873779297,\n 6.972563743591309,\n 11.15610218048096\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"560nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3286610606765202,\n \"min\": 0.4359263777732849,\n \"max\": 1.74370551109314,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.8718527555465698,\n 1.74370551109314,\n 1.3077791929245\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"585nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.35112345102528975,\n \"min\": 0.8239997625350952,\n \"max\": 1.64799952507019,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8239997625350952,\n 1.235999703407288,\n 1.64799952507019\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"610nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.4382703083150796,\n \"min\": 2.197717189788818,\n \"max\": 6.593151569366455,\n \"num_unique_values\": 5,\n \"samples\": [\n 5.494293212890625,\n 6.593151569366455,\n 4.395434379577637\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"645nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.19530852902131463,\n \"min\": 0.3733329176902771,\n \"max\": 1.119998812675476,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7466658353805542,\n 1.119998812675476,\n 0.3733329176902771\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"680nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.22479147069123506,\n \"min\": 1.005298018455505,\n \"max\": 2.010596036911011,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.005298018455505,\n 2.010596036911011\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"705nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.07913962042246601,\n \"min\": 0.0,\n \"max\": 0.3539231419563293,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 0.3539231419563293\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"730nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.16724617515938625,\n \"min\": 0.747947633266449,\n \"max\": 1.495895266532898,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.495895266532898,\n 0.747947633266449\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"760nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.40068641506451047,\n \"min\": 0.0,\n \"max\": 0.7810816168785095,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 0.7810816168785095\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"810nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.8270058035850525,\n \"max\": 0.8270058035850525,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.8270058035850525\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"860nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4029208219005489,\n \"min\": 1.09983217716217,\n \"max\": 2.199664354324341,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.09983217716217\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"900nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.5470262995437506e-17,\n \"min\": 0.512009024620056,\n \"max\": 0.5120090246200562,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.512009024620056\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"940nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label_Temp\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"Bitter\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Ideal\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 7
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"plt.rcParams['font.serif'] = \"Times New Roman\"\n",
|
||
"plt.rcParams['font.family'] = \"serif\"\n",
|
||
"palette = sns.color_palette(\"crest\")\n",
|
||
"sns.countplot(data=df, x='Label', palette=palette)\n",
|
||
"plt.xlabel(\"Label\")\n",
|
||
"plt.title(\"Distribusi Label\")\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 611
|
||
},
|
||
"id": "_6LskFP2q58C",
|
||
"outputId": "57b5d300-fa7b-47ad-dc48-90739106402f"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"<ipython-input-8-a3ce1d6dcb17>:4: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.countplot(data=df, x='Label', palette=palette)\n",
|
||
"<ipython-input-8-a3ce1d6dcb17>:4: UserWarning: The palette list has more values (6) than needed (5), which may not be intended.\n",
|
||
" sns.countplot(data=df, x='Label', palette=palette)\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Preprocessing"
|
||
],
|
||
"metadata": {
|
||
"id": "BM_9cZ8KqTzB"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Applying encoder\n",
|
||
"encoder = LabelEncoder()\n",
|
||
"df['Label'] = encoder.fit_transform(df['Label'])\n",
|
||
"\n",
|
||
"# Get Classes\n",
|
||
"classes = encoder.classes_\n",
|
||
"print(classes)\n",
|
||
"\n",
|
||
"# Inspect Data after encoded\n",
|
||
"df.head()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 243
|
||
},
|
||
"id": "7MvvNZtKqVVF",
|
||
"outputId": "8ef5ab9e-0374-4f9a-be48-2adede022c35"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"['Bitter' 'Ideal' 'Strong' 'Underdeveloped' 'Weak']\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" 410nm 435nm 460nm 485nm 510nm 535nm 560nm \\\n",
|
||
"0 11.841914 2.898831 7.445784 1.770806 3.017060 6.972564 0.435926 \n",
|
||
"1 11.841914 2.898831 7.445784 1.770806 2.262795 6.972564 0.435926 \n",
|
||
"2 11.841914 2.898831 7.445784 1.770806 3.017060 6.972564 0.435926 \n",
|
||
"3 11.841914 2.898831 7.445784 1.770806 3.017060 6.972564 0.435926 \n",
|
||
"4 11.841914 2.898831 7.445784 1.770806 3.017060 6.972564 0.435926 \n",
|
||
"\n",
|
||
" 585nm 610nm 645nm 680nm 705nm 730nm 760nm \\\n",
|
||
"0 0.824 3.296576 0.746666 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"1 0.824 3.296576 0.373333 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"2 0.824 3.296576 0.373333 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"3 0.824 3.296576 0.373333 1.005298 0.353923 0.747948 0.000000 \n",
|
||
"4 0.824 3.296576 0.373333 1.005298 0.353923 0.747948 0.781082 \n",
|
||
"\n",
|
||
" 810nm 860nm 900nm 940nm Label_Temp Label \n",
|
||
"0 0.827006 1.099832 0.512009 0 Bitter 0 \n",
|
||
"1 0.827006 1.099832 0.512009 0 Bitter 0 \n",
|
||
"2 0.827006 1.099832 0.512009 0 Bitter 0 \n",
|
||
"3 0.827006 1.099832 0.512009 0 Bitter 0 \n",
|
||
"4 0.827006 1.099832 0.512009 0 Bitter 0 "
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-6f69ec5e-0f43-4787-88ef-9b4b563d814a\" class=\"colab-df-container\">\n",
|
||
" <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>410nm</th>\n",
|
||
" <th>435nm</th>\n",
|
||
" <th>460nm</th>\n",
|
||
" <th>485nm</th>\n",
|
||
" <th>510nm</th>\n",
|
||
" <th>535nm</th>\n",
|
||
" <th>560nm</th>\n",
|
||
" <th>585nm</th>\n",
|
||
" <th>610nm</th>\n",
|
||
" <th>645nm</th>\n",
|
||
" <th>680nm</th>\n",
|
||
" <th>705nm</th>\n",
|
||
" <th>730nm</th>\n",
|
||
" <th>760nm</th>\n",
|
||
" <th>810nm</th>\n",
|
||
" <th>860nm</th>\n",
|
||
" <th>900nm</th>\n",
|
||
" <th>940nm</th>\n",
|
||
" <th>Label_Temp</th>\n",
|
||
" <th>Label</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.746666</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>2.262795</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.373333</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.373333</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.373333</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>11.841914</td>\n",
|
||
" <td>2.898831</td>\n",
|
||
" <td>7.445784</td>\n",
|
||
" <td>1.770806</td>\n",
|
||
" <td>3.017060</td>\n",
|
||
" <td>6.972564</td>\n",
|
||
" <td>0.435926</td>\n",
|
||
" <td>0.824</td>\n",
|
||
" <td>3.296576</td>\n",
|
||
" <td>0.373333</td>\n",
|
||
" <td>1.005298</td>\n",
|
||
" <td>0.353923</td>\n",
|
||
" <td>0.747948</td>\n",
|
||
" <td>0.781082</td>\n",
|
||
" <td>0.827006</td>\n",
|
||
" <td>1.099832</td>\n",
|
||
" <td>0.512009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Bitter</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f69ec5e-0f43-4787-88ef-9b4b563d814a')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-6f69ec5e-0f43-4787-88ef-9b4b563d814a button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-6f69ec5e-0f43-4787-88ef-9b4b563d814a');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-c0acf99f-bcd4-42c3-a0b1-2b060261aef0\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0acf99f-bcd4-42c3-a0b1-2b060261aef0')\"\n",
|
||
" title=\"Suggest charts\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-c0acf99f-bcd4-42c3-a0b1-2b060261aef0 button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"variable_name": "df",
|
||
"summary": "{\n \"name\": \"df\",\n \"rows\": 150,\n \"fields\": [\n {\n \"column\": \"410nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.4582964309789936,\n \"min\": 4.229255199432373,\n \"max\": 13.533616065979,\n \"num_unique_values\": 12,\n \"samples\": [\n 6.766808032989502,\n 4.229255199432373,\n 11.8419141769409\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"435nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7296520006616061,\n \"min\": 0.9662770628929138,\n \"max\": 3.865108251571655,\n \"num_unique_values\": 7,\n \"samples\": [\n 2.8988311290741,\n 2.898831129074097,\n 3.86510825157165\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"460nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.882152352199596,\n \"min\": 1.861446022987366,\n \"max\": 9.30722999572754,\n \"num_unique_values\": 8,\n \"samples\": [\n 5.584338188171387,\n 9.30722999572754,\n 7.445784091949463\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"485nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5492264546958235,\n \"min\": 0.8854029774665833,\n \"max\": 2.656208992004395,\n \"num_unique_values\": 3,\n \"samples\": [\n 1.770805954933167,\n 0.8854029774665833,\n 2.656208992004395\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"510nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6101450619658129,\n \"min\": 1.508529901504517,\n \"max\": 3.771324872970581,\n \"num_unique_values\": 4,\n \"samples\": [\n 2.262794971466064,\n 1.508529901504517,\n 3.017059803009033\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"535nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.0889073991790488,\n \"min\": 5.578051090240479,\n \"max\": 11.85335826873779,\n \"num_unique_values\": 9,\n \"samples\": [\n 6.275307655334473,\n 11.15610218048096,\n 9.761589050292969\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"560nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.38552942823292247,\n \"min\": 0.4359263777732849,\n \"max\": 1.74370551109314,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.3077791929245,\n 1.74370551109314,\n 0.4359263777732849\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"585nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3801199513984839,\n \"min\": 0.4119998812675476,\n \"max\": 1.64799952507019,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.64799952507019,\n 0.4119998812675476,\n 0.8239997625350952\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"610nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.4832429200485147,\n \"min\": 2.197717189788818,\n \"max\": 6.593151569366455,\n \"num_unique_values\": 5,\n \"samples\": [\n 5.494293212890625,\n 4.395434379577637,\n 2.197717189788818\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"645nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2576985462809935,\n \"min\": 0.3733329176902771,\n \"max\": 1.119998812675476,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.7466658353805542,\n 0.3733329176902771,\n 1.119998812675476\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"680nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4501357795942562,\n \"min\": 0.0,\n \"max\": 2.010596036911011,\n \"num_unique_values\": 3,\n \"samples\": [\n 1.005298018455505,\n 2.010596036911011,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"705nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.10961818009291775,\n \"min\": 0.0,\n \"max\": 0.3539231419563293,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 0.3539231419563293\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"730nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.10506346584982147,\n \"min\": 0.747947633266449,\n \"max\": 1.495895266532898,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.495895266532898,\n 0.747947633266449\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"760nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.39059322650652445,\n \"min\": 0.0,\n \"max\": 0.7810816168785095,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 0.7810816168785095\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"810nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.13368301047291217,\n \"min\": 0.8270058035850525,\n \"max\": 1.654011607170105,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.654011607170105,\n 0.8270058035850525\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"860nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.49547777724592273,\n \"min\": 1.09983217716217,\n \"max\": 2.199664354324341,\n \"num_unique_values\": 2,\n \"samples\": [\n 2.199664354324341,\n 1.09983217716217\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"900nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.0337712221188733e-17,\n \"min\": 0.512009024620056,\n \"max\": 0.5120090246200562,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.5120090246200562,\n 0.512009024620056\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"940nm\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label_Temp\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 15,\n \"samples\": [\n \"Underdeveloped\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 9
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Modelling dengan Maxpooling"
|
||
],
|
||
"metadata": {
|
||
"id": "dIwdlvRu-Tev"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tensorflow.keras.utils import plot_model\n",
|
||
"\n",
|
||
"def build_model():\n",
|
||
" model = Sequential()\n",
|
||
"\n",
|
||
" # Input layer\n",
|
||
" model.add(Input(shape=(18, 1)))\n",
|
||
" model.add(Conv1D(\n",
|
||
" filters=16, #\n",
|
||
" kernel_size=2,\n",
|
||
" activation='relu',\n",
|
||
" kernel_regularizer=l2(0.001) # Nilai tetap untuk L2 regularization\n",
|
||
" ))\n",
|
||
" model.add(MaxPooling1D(pool_size=3))\n",
|
||
" model.add(Flatten())\n",
|
||
" model.add(Dense(\n",
|
||
" units=16,\n",
|
||
" activation='relu',\n",
|
||
" kernel_regularizer=l2(0.001) # Nilai tetap untuk L2 regularization\n",
|
||
" ))\n",
|
||
" # model.add(Dropout(0.4)) # Nilai tetap untuk dropout\n",
|
||
"\n",
|
||
" # Output layer\n",
|
||
" model.add(Dense(len(classes), activation='softmax'))\n",
|
||
"\n",
|
||
" # Optimizer dengan nilai tetap\n",
|
||
" optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
|
||
"\n",
|
||
" # Compile model\n",
|
||
" model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
|
||
" metrics=['accuracy']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",\n",
|
||
" patience=5,\n",
|
||
" verbose=1)]\n",
|
||
"\n",
|
||
"early_model = build_model()\n",
|
||
"# Plot and save the model architecture\n",
|
||
"plot_model(early_model, to_file=\"early_model_plot.png\", show_shapes=True, show_layer_names=True, show_layer_activations=True)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "Kvq_rAjaG54M",
|
||
"outputId": "939b6239-c612-4fbe-e5a8-b1916c8727ff"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<IPython.core.display.Image object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 25
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"X = df.iloc[:, :-2].values\n",
|
||
"y = df['Label'].values\n",
|
||
"accuracies = []\n",
|
||
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
|
||
"for fold, (train_idx, val_idx) in enumerate(skf.split(X, y), 1):\n",
|
||
" print(f\"\\nFold {fold}\")\n",
|
||
"\n",
|
||
" # Split data\n",
|
||
" X_train, X_val = X[train_idx], X[val_idx]\n",
|
||
" y_train, y_val = y[train_idx], y[val_idx]\n",
|
||
"\n",
|
||
" # Convert to tf.data.Dataset\n",
|
||
" train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
|
||
" val_ds = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
|
||
"\n",
|
||
" # Shuffle, batch, cache, prefetch\n",
|
||
" train_ds = train_ds.shuffle(buffer_size=len(X_train), seed=42) \\\n",
|
||
" .batch(12) \\\n",
|
||
" .cache() \\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" val_ds = val_ds.batch(12).cache().prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" model = build_model()\n",
|
||
" hist = model.fit(train_ds, epochs=100, validation_data=val_ds, callbacks=callbacks)\n",
|
||
"\n",
|
||
" plt.figure(figsize=(12, 5))\n",
|
||
" # Plot untuk accuracy dan val_accuracy\n",
|
||
" plt.title(\"Accuracy and Val Accuracy\")\n",
|
||
" plt.plot(hist.history['accuracy'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
" plt.plot(hist.history['val_accuracy'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
" plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
" plt.xlabel('Epoch')\n",
|
||
" plt.ylabel('Accuracy')\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # Evaluate\n",
|
||
" loss, acc = model.evaluate(val_ds, verbose=0)\n",
|
||
" print(f\"Accuracy Fold {fold}: {acc:.4f}\")\n",
|
||
"\n",
|
||
" accuracies.append(acc)\n",
|
||
"\n",
|
||
"print(f\"\\nAverage Accuracy: {np.mean(accuracies):.4f}\")\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "o0odzw74mCAD",
|
||
"outputId": "b0f9fa49-62a5-4cc5-9075-136406ab371c"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"Fold 1\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 60ms/step - accuracy: 0.2922 - loss: 2.0723 - val_accuracy: 0.4000 - val_loss: 1.9117 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4367 - loss: 1.7634 - val_accuracy: 0.4000 - val_loss: 1.6736 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3976 - loss: 1.6149 - val_accuracy: 0.4000 - val_loss: 1.5597 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3976 - loss: 1.5534 - val_accuracy: 0.4000 - val_loss: 1.5042 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3976 - loss: 1.5221 - val_accuracy: 0.3333 - val_loss: 1.4685 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3260 - loss: 1.4999 - val_accuracy: 0.2667 - val_loss: 1.4418 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.2366 - loss: 1.4806 - val_accuracy: 0.3000 - val_loss: 1.4195 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2953 - loss: 1.4626 - val_accuracy: 0.4333 - val_loss: 1.3998 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3604 - loss: 1.4451 - val_accuracy: 0.4333 - val_loss: 1.3817 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4062 - loss: 1.4269 - val_accuracy: 0.5333 - val_loss: 1.3643 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5027 - loss: 1.4083 - val_accuracy: 0.5667 - val_loss: 1.3473 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4499 - loss: 1.3897 - val_accuracy: 0.4667 - val_loss: 1.3313 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4617 - loss: 1.3720 - val_accuracy: 0.4667 - val_loss: 1.3166 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4676 - loss: 1.3541 - val_accuracy: 0.5000 - val_loss: 1.3012 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4830 - loss: 1.3345 - val_accuracy: 0.5333 - val_loss: 1.2869 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5006 - loss: 1.3161 - val_accuracy: 0.5667 - val_loss: 1.2650 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5361 - loss: 1.2954 - val_accuracy: 0.6000 - val_loss: 1.2493 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5894 - loss: 1.2706 - val_accuracy: 0.6000 - val_loss: 1.2333 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5894 - loss: 1.2475 - val_accuracy: 0.5333 - val_loss: 1.2078 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5942 - loss: 1.2391 - val_accuracy: 0.5333 - val_loss: 1.1893 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6494 - loss: 1.2127 - val_accuracy: 0.6000 - val_loss: 1.1734 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6584 - loss: 1.1861 - val_accuracy: 0.6000 - val_loss: 1.1575 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6584 - loss: 1.1632 - val_accuracy: 0.6000 - val_loss: 1.1411 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6584 - loss: 1.1406 - val_accuracy: 0.6000 - val_loss: 1.1246 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6584 - loss: 1.1204 - val_accuracy: 0.6333 - val_loss: 1.1077 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6584 - loss: 1.1022 - val_accuracy: 0.6000 - val_loss: 1.0904 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6584 - loss: 1.0839 - val_accuracy: 0.6000 - val_loss: 1.0741 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6584 - loss: 1.0663 - val_accuracy: 0.6000 - val_loss: 1.0585 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6584 - loss: 1.0488 - val_accuracy: 0.6000 - val_loss: 1.0434 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6733 - loss: 1.0315 - val_accuracy: 0.6000 - val_loss: 1.0283 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6733 - loss: 1.0141 - val_accuracy: 0.6000 - val_loss: 1.0129 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7041 - loss: 0.9974 - val_accuracy: 0.6000 - val_loss: 0.9979 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7270 - loss: 0.9807 - val_accuracy: 0.6000 - val_loss: 0.9836 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7270 - loss: 0.9637 - val_accuracy: 0.6000 - val_loss: 0.9694 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7571 - loss: 0.9469 - val_accuracy: 0.6000 - val_loss: 0.9550 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7586 - loss: 0.9308 - val_accuracy: 0.6000 - val_loss: 0.9409 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7586 - loss: 0.9146 - val_accuracy: 0.6333 - val_loss: 0.9268 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7715 - loss: 0.8980 - val_accuracy: 0.6333 - val_loss: 0.9129 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7991 - loss: 0.8819 - val_accuracy: 0.6333 - val_loss: 0.8989 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7991 - loss: 0.8658 - val_accuracy: 0.6667 - val_loss: 0.8847 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7991 - loss: 0.8501 - val_accuracy: 0.6667 - val_loss: 0.8699 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7991 - loss: 0.8344 - val_accuracy: 0.6667 - val_loss: 0.8553 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7991 - loss: 0.8189 - val_accuracy: 0.6667 - val_loss: 0.8414 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7991 - loss: 0.8033 - val_accuracy: 0.6667 - val_loss: 0.8273 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7991 - loss: 0.7883 - val_accuracy: 0.6667 - val_loss: 0.8130 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7991 - loss: 0.7736 - val_accuracy: 0.6667 - val_loss: 0.7989 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7991 - loss: 0.7593 - val_accuracy: 0.6667 - val_loss: 0.7843 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8374 - loss: 0.7421 - val_accuracy: 0.6667 - val_loss: 0.7688 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7991 - loss: 0.7280 - val_accuracy: 0.6667 - val_loss: 0.7513 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8145 - loss: 0.7117 - val_accuracy: 0.6667 - val_loss: 0.7353 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8145 - loss: 0.6944 - val_accuracy: 0.6667 - val_loss: 0.7199 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8145 - loss: 0.6788 - val_accuracy: 0.6667 - val_loss: 0.7050 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8216 - loss: 0.6633 - val_accuracy: 0.6667 - val_loss: 0.6903 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8216 - loss: 0.6490 - val_accuracy: 0.6667 - val_loss: 0.6746 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8231 - loss: 0.6344 - val_accuracy: 0.6667 - val_loss: 0.6595 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8456 - loss: 0.6209 - val_accuracy: 0.8000 - val_loss: 0.6447 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8456 - loss: 0.6070 - val_accuracy: 0.8000 - val_loss: 0.6305 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8802 - loss: 0.5935 - val_accuracy: 0.8000 - val_loss: 0.6170 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8861 - loss: 0.5800 - val_accuracy: 0.8000 - val_loss: 0.6039 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8884 - loss: 0.5669 - val_accuracy: 0.8667 - val_loss: 0.5917 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9057 - loss: 0.5533 - val_accuracy: 0.8667 - val_loss: 0.5798 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.5403 - val_accuracy: 0.8667 - val_loss: 0.5666 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.5275 - val_accuracy: 0.8667 - val_loss: 0.5529 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9297 - loss: 0.5158 - val_accuracy: 0.9333 - val_loss: 0.5409 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9359 - loss: 0.5045 - val_accuracy: 0.9333 - val_loss: 0.5295 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9359 - loss: 0.4933 - val_accuracy: 0.9333 - val_loss: 0.5171 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9359 - loss: 0.4821 - val_accuracy: 0.9667 - val_loss: 0.5065 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9359 - loss: 0.4715 - val_accuracy: 0.9667 - val_loss: 0.4952 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9359 - loss: 0.4611 - val_accuracy: 0.9667 - val_loss: 0.4841 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9359 - loss: 0.4510 - val_accuracy: 0.9667 - val_loss: 0.4733 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9392 - loss: 0.4410 - val_accuracy: 0.9667 - val_loss: 0.4631 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9622 - loss: 0.4315 - val_accuracy: 0.9667 - val_loss: 0.4532 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9665 - loss: 0.4217 - val_accuracy: 0.9667 - val_loss: 0.4433 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.4128 - val_accuracy: 0.9667 - val_loss: 0.4339 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.4037 - val_accuracy: 0.9667 - val_loss: 0.4244 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3950 - val_accuracy: 0.9667 - val_loss: 0.4151 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3866 - val_accuracy: 0.9667 - val_loss: 0.4065 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3781 - val_accuracy: 0.9667 - val_loss: 0.3976 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3701 - val_accuracy: 0.9667 - val_loss: 0.3891 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3622 - val_accuracy: 0.9667 - val_loss: 0.3807 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3546 - val_accuracy: 0.9667 - val_loss: 0.3726 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3469 - val_accuracy: 0.9667 - val_loss: 0.3648 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3396 - val_accuracy: 0.9667 - val_loss: 0.3571 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3325 - val_accuracy: 0.9667 - val_loss: 0.3494 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3255 - val_accuracy: 0.9667 - val_loss: 0.3422 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3188 - val_accuracy: 0.9667 - val_loss: 0.3351 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.3122 - val_accuracy: 0.9667 - val_loss: 0.3281 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3058 - val_accuracy: 0.9667 - val_loss: 0.3214 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2994 - val_accuracy: 0.9667 - val_loss: 0.3149 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2934 - val_accuracy: 0.9667 - val_loss: 0.3085 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2874 - val_accuracy: 0.9667 - val_loss: 0.3022 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2816 - val_accuracy: 0.9667 - val_loss: 0.2961 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2760 - val_accuracy: 0.9667 - val_loss: 0.2904 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2706 - val_accuracy: 0.9667 - val_loss: 0.2846 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2652 - val_accuracy: 0.9667 - val_loss: 0.2791 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2600 - val_accuracy: 0.9667 - val_loss: 0.2737 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2550 - val_accuracy: 0.9667 - val_loss: 0.2685 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2501 - val_accuracy: 0.9667 - val_loss: 0.2634 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2453 - val_accuracy: 0.9667 - val_loss: 0.2585 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2406 - val_accuracy: 0.9667 - val_loss: 0.2538 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 1: 0.9667\n",
|
||
"\n",
|
||
"Fold 2\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 57ms/step - accuracy: 0.1229 - loss: 1.8671 - val_accuracy: 0.2000 - val_loss: 1.6089 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.1922 - loss: 1.5817 - val_accuracy: 0.3000 - val_loss: 1.4846 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2771 - loss: 1.4975 - val_accuracy: 0.4667 - val_loss: 1.4310 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3786 - loss: 1.4481 - val_accuracy: 0.4667 - val_loss: 1.3943 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4393 - loss: 1.4028 - val_accuracy: 0.4333 - val_loss: 1.3636 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4493 - loss: 1.3698 - val_accuracy: 0.5000 - val_loss: 1.3312 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4723 - loss: 1.3425 - val_accuracy: 0.4333 - val_loss: 1.3070 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4493 - loss: 1.3155 - val_accuracy: 0.4333 - val_loss: 1.2809 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4493 - loss: 1.2880 - val_accuracy: 0.5000 - val_loss: 1.2593 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4493 - loss: 1.2634 - val_accuracy: 0.5000 - val_loss: 1.2378 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4670 - loss: 1.2399 - val_accuracy: 0.5667 - val_loss: 1.2209 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5279 - loss: 1.2183 - val_accuracy: 0.5000 - val_loss: 1.2025 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5659 - loss: 1.1963 - val_accuracy: 0.5000 - val_loss: 1.1877 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5659 - loss: 1.1759 - val_accuracy: 0.5333 - val_loss: 1.1697 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6272 - loss: 1.1541 - val_accuracy: 0.5333 - val_loss: 1.1572 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6344 - loss: 1.1328 - val_accuracy: 0.5333 - val_loss: 1.1368 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6344 - loss: 1.1113 - val_accuracy: 0.5333 - val_loss: 1.1255 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6392 - loss: 1.0919 - val_accuracy: 0.6000 - val_loss: 1.1010 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6546 - loss: 1.0712 - val_accuracy: 0.5667 - val_loss: 1.0886 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6661 - loss: 1.0515 - val_accuracy: 0.6333 - val_loss: 1.0664 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6891 - loss: 1.0309 - val_accuracy: 0.6333 - val_loss: 1.0538 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6935 - loss: 1.0137 - val_accuracy: 0.6333 - val_loss: 1.0313 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6935 - loss: 0.9915 - val_accuracy: 0.6333 - val_loss: 1.0188 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7155 - loss: 0.9743 - val_accuracy: 0.7333 - val_loss: 0.9924 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7317 - loss: 0.9568 - val_accuracy: 0.7333 - val_loss: 0.9846 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7473 - loss: 0.9375 - val_accuracy: 0.7333 - val_loss: 0.9564 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7226 - loss: 0.9194 - val_accuracy: 0.6667 - val_loss: 0.9515 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7702 - loss: 0.9022 - val_accuracy: 0.7333 - val_loss: 0.9274 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7589 - loss: 0.8858 - val_accuracy: 0.7333 - val_loss: 0.9199 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7912 - loss: 0.8698 - val_accuracy: 0.7333 - val_loss: 0.8974 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7912 - loss: 0.8541 - val_accuracy: 0.7333 - val_loss: 0.8928 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8028 - loss: 0.8392 - val_accuracy: 0.7333 - val_loss: 0.8687 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8141 - loss: 0.8229 - val_accuracy: 0.7333 - val_loss: 0.8601 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8143 - loss: 0.8068 - val_accuracy: 0.7333 - val_loss: 0.8416 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8373 - loss: 0.7921 - val_accuracy: 0.7333 - val_loss: 0.8352 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8143 - loss: 0.7771 - val_accuracy: 0.7667 - val_loss: 0.8124 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8373 - loss: 0.7619 - val_accuracy: 0.7667 - val_loss: 0.8071 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8349 - loss: 0.7484 - val_accuracy: 0.7667 - val_loss: 0.7851 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8579 - loss: 0.7316 - val_accuracy: 0.7667 - val_loss: 0.7810 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8579 - loss: 0.7195 - val_accuracy: 0.7667 - val_loss: 0.7597 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8579 - loss: 0.7021 - val_accuracy: 0.8000 - val_loss: 0.7575 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.6915 - val_accuracy: 0.8000 - val_loss: 0.7359 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.6753 - val_accuracy: 0.8333 - val_loss: 0.7241 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8760 - loss: 0.6636 - val_accuracy: 0.8333 - val_loss: 0.7131 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.6504 - val_accuracy: 0.8333 - val_loss: 0.7014 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.6366 - val_accuracy: 0.8333 - val_loss: 0.6892 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8760 - loss: 0.6266 - val_accuracy: 0.8333 - val_loss: 0.6798 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8760 - loss: 0.6139 - val_accuracy: 0.8333 - val_loss: 0.6716 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8783 - loss: 0.5984 - val_accuracy: 0.8333 - val_loss: 0.6570 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8783 - loss: 0.5910 - val_accuracy: 0.8333 - val_loss: 0.6481 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8783 - loss: 0.5778 - val_accuracy: 0.8333 - val_loss: 0.6356 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8783 - loss: 0.5656 - val_accuracy: 0.8333 - val_loss: 0.6213 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8807 - loss: 0.5551 - val_accuracy: 0.8333 - val_loss: 0.6068 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8807 - loss: 0.5431 - val_accuracy: 0.8333 - val_loss: 0.5929 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8807 - loss: 0.5258 - val_accuracy: 0.8333 - val_loss: 0.5794 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8807 - loss: 0.5148 - val_accuracy: 0.8333 - val_loss: 0.5685 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8807 - loss: 0.4986 - val_accuracy: 0.8333 - val_loss: 0.5533 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8807 - loss: 0.4902 - val_accuracy: 0.8333 - val_loss: 0.5437 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8807 - loss: 0.4725 - val_accuracy: 0.8333 - val_loss: 0.5326 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8807 - loss: 0.4614 - val_accuracy: 0.9000 - val_loss: 0.5199 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8807 - loss: 0.4486 - val_accuracy: 0.9000 - val_loss: 0.5157 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9032 - loss: 0.4376 - val_accuracy: 0.9000 - val_loss: 0.5034 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8938 - loss: 0.4258 - val_accuracy: 0.9333 - val_loss: 0.4915 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9235 - loss: 0.4156 - val_accuracy: 0.9333 - val_loss: 0.4885 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9235 - loss: 0.4046 - val_accuracy: 0.9333 - val_loss: 0.4768 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9306 - loss: 0.3945 - val_accuracy: 0.9333 - val_loss: 0.4642 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9330 - loss: 0.3857 - val_accuracy: 0.9333 - val_loss: 0.4624 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9713 - loss: 0.3784 - val_accuracy: 0.9333 - val_loss: 0.4514 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.3667 - val_accuracy: 0.9333 - val_loss: 0.4392 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9559 - loss: 0.3592 - val_accuracy: 0.9333 - val_loss: 0.4306 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9713 - loss: 0.3503 - val_accuracy: 0.9333 - val_loss: 0.4271 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.3425 - val_accuracy: 0.9333 - val_loss: 0.4204 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.3322 - val_accuracy: 0.9333 - val_loss: 0.4108 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9713 - loss: 0.3265 - val_accuracy: 0.9333 - val_loss: 0.4053 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.3182 - val_accuracy: 0.9333 - val_loss: 0.3982 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9713 - loss: 0.3136 - val_accuracy: 0.9333 - val_loss: 0.3927 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.3050 - val_accuracy: 0.9333 - val_loss: 0.3860 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.2989 - val_accuracy: 0.9333 - val_loss: 0.3827 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9713 - loss: 0.2925 - val_accuracy: 0.9333 - val_loss: 0.3749 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9713 - loss: 0.2865 - val_accuracy: 0.9333 - val_loss: 0.3678 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.2806 - val_accuracy: 0.9333 - val_loss: 0.3666 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2755 - val_accuracy: 0.9333 - val_loss: 0.3566 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9757 - loss: 0.2693 - val_accuracy: 0.9333 - val_loss: 0.3537 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2649 - val_accuracy: 0.9333 - val_loss: 0.3455 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2589 - val_accuracy: 0.9333 - val_loss: 0.3438 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9813 - loss: 0.2551 - val_accuracy: 0.9333 - val_loss: 0.3358 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2490 - val_accuracy: 0.9333 - val_loss: 0.3314 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9813 - loss: 0.2450 - val_accuracy: 0.9333 - val_loss: 0.3288 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2450 - val_accuracy: 0.9333 - val_loss: 0.3248 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2361 - val_accuracy: 0.9333 - val_loss: 0.3179 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9813 - loss: 0.2316 - val_accuracy: 0.9333 - val_loss: 0.3118 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2277 - val_accuracy: 0.9333 - val_loss: 0.3108 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2242 - val_accuracy: 0.9333 - val_loss: 0.3038 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2199 - val_accuracy: 0.9333 - val_loss: 0.3033 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9813 - loss: 0.2168 - val_accuracy: 0.9333 - val_loss: 0.2969 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2125 - val_accuracy: 0.9333 - val_loss: 0.2925 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2124 - val_accuracy: 0.9333 - val_loss: 0.2884 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2054 - val_accuracy: 0.9333 - val_loss: 0.2858 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.2028 - val_accuracy: 0.9333 - val_loss: 0.2806 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9813 - loss: 0.1991 - val_accuracy: 0.9333 - val_loss: 0.2778 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 2: 0.9333\n",
|
||
"\n",
|
||
"Fold 3\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 57ms/step - accuracy: 0.2381 - loss: 1.6687 - val_accuracy: 0.2000 - val_loss: 1.5969 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2787 - loss: 1.5598 - val_accuracy: 0.3333 - val_loss: 1.5506 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3224 - loss: 1.5302 - val_accuracy: 0.2667 - val_loss: 1.5245 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.2348 - loss: 1.5111 - val_accuracy: 0.3000 - val_loss: 1.4977 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2574 - loss: 1.4875 - val_accuracy: 0.4000 - val_loss: 1.4709 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3519 - loss: 1.4618 - val_accuracy: 0.4667 - val_loss: 1.4477 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4207 - loss: 1.4381 - val_accuracy: 0.4667 - val_loss: 1.4228 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4207 - loss: 1.4145 - val_accuracy: 0.4667 - val_loss: 1.4019 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4207 - loss: 1.3930 - val_accuracy: 0.4667 - val_loss: 1.3811 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3800 - loss: 1.3736 - val_accuracy: 0.4667 - val_loss: 1.3613 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3872 - loss: 1.3530 - val_accuracy: 0.4667 - val_loss: 1.3435 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3943 - loss: 1.3340 - val_accuracy: 0.4667 - val_loss: 1.3175 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3735 - loss: 1.3128 - val_accuracy: 0.4333 - val_loss: 1.2938 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3679 - loss: 1.2899 - val_accuracy: 0.4333 - val_loss: 1.2749 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3776 - loss: 1.2709 - val_accuracy: 0.4667 - val_loss: 1.2526 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3904 - loss: 1.2505 - val_accuracy: 0.4667 - val_loss: 1.2325 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3904 - loss: 1.2307 - val_accuracy: 0.4667 - val_loss: 1.2125 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4064 - loss: 1.2120 - val_accuracy: 0.4667 - val_loss: 1.1951 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4020 - loss: 1.1944 - val_accuracy: 0.4667 - val_loss: 1.1770 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4064 - loss: 1.1767 - val_accuracy: 0.4667 - val_loss: 1.1604 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3776 - loss: 1.1596 - val_accuracy: 0.4667 - val_loss: 1.1450 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3776 - loss: 1.1435 - val_accuracy: 0.4667 - val_loss: 1.1290 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3863 - loss: 1.1271 - val_accuracy: 0.4667 - val_loss: 1.1134 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3979 - loss: 1.1112 - val_accuracy: 0.4667 - val_loss: 1.0983 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3979 - loss: 1.0952 - val_accuracy: 0.4667 - val_loss: 1.0835 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4209 - loss: 1.0796 - val_accuracy: 0.5000 - val_loss: 1.0683 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4396 - loss: 1.0640 - val_accuracy: 0.5333 - val_loss: 1.0538 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5558 - loss: 1.0487 - val_accuracy: 0.5667 - val_loss: 1.0390 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5994 - loss: 1.0330 - val_accuracy: 0.5667 - val_loss: 1.0251 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5994 - loss: 1.0186 - val_accuracy: 0.5667 - val_loss: 1.0102 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6038 - loss: 1.0028 - val_accuracy: 0.5667 - val_loss: 0.9970 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6324 - loss: 0.9879 - val_accuracy: 0.5667 - val_loss: 0.9823 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6363 - loss: 0.9724 - val_accuracy: 0.5667 - val_loss: 0.9685 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6517 - loss: 0.9572 - val_accuracy: 0.5667 - val_loss: 0.9561 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6550 - loss: 0.9421 - val_accuracy: 0.5667 - val_loss: 0.9432 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6550 - loss: 0.9268 - val_accuracy: 0.6000 - val_loss: 0.9289 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6640 - loss: 0.9109 - val_accuracy: 0.6000 - val_loss: 0.9148 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6640 - loss: 0.8954 - val_accuracy: 0.6000 - val_loss: 0.9002 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6697 - loss: 0.8795 - val_accuracy: 0.6000 - val_loss: 0.8864 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6936 - loss: 0.8637 - val_accuracy: 0.6333 - val_loss: 0.8720 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7082 - loss: 0.8486 - val_accuracy: 0.6667 - val_loss: 0.8580 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7668 - loss: 0.8326 - val_accuracy: 0.6667 - val_loss: 0.8443 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8098 - loss: 0.8179 - val_accuracy: 0.6667 - val_loss: 0.8309 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8170 - loss: 0.8020 - val_accuracy: 0.6667 - val_loss: 0.8163 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8594 - loss: 0.7872 - val_accuracy: 0.7667 - val_loss: 0.8019 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8594 - loss: 0.7718 - val_accuracy: 0.7667 - val_loss: 0.7883 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8594 - loss: 0.7567 - val_accuracy: 0.8000 - val_loss: 0.7743 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8775 - loss: 0.7418 - val_accuracy: 0.8000 - val_loss: 0.7602 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.7269 - val_accuracy: 0.8000 - val_loss: 0.7456 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.7119 - val_accuracy: 0.8000 - val_loss: 0.7311 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.6964 - val_accuracy: 0.8333 - val_loss: 0.7158 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8889 - loss: 0.6809 - val_accuracy: 0.8333 - val_loss: 0.7002 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.6652 - val_accuracy: 0.8667 - val_loss: 0.6844 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8889 - loss: 0.6498 - val_accuracy: 0.8667 - val_loss: 0.6688 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8904 - loss: 0.6344 - val_accuracy: 0.8667 - val_loss: 0.6534 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9134 - loss: 0.6190 - val_accuracy: 0.8667 - val_loss: 0.6381 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9221 - loss: 0.6040 - val_accuracy: 0.8667 - val_loss: 0.6244 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9221 - loss: 0.5892 - val_accuracy: 0.8667 - val_loss: 0.6100 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9221 - loss: 0.5749 - val_accuracy: 0.8667 - val_loss: 0.5964 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9221 - loss: 0.5605 - val_accuracy: 0.8667 - val_loss: 0.5828 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9221 - loss: 0.5468 - val_accuracy: 0.8667 - val_loss: 0.5701 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9221 - loss: 0.5332 - val_accuracy: 0.8667 - val_loss: 0.5574 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9221 - loss: 0.5201 - val_accuracy: 0.8667 - val_loss: 0.5448 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9221 - loss: 0.5070 - val_accuracy: 0.9000 - val_loss: 0.5327 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9265 - loss: 0.4944 - val_accuracy: 0.9333 - val_loss: 0.5209 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9566 - loss: 0.4817 - val_accuracy: 0.9333 - val_loss: 0.5081 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9682 - loss: 0.4688 - val_accuracy: 0.9333 - val_loss: 0.4956 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.4561 - val_accuracy: 0.9333 - val_loss: 0.4837 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.4434 - val_accuracy: 0.9333 - val_loss: 0.4709 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9682 - loss: 0.4311 - val_accuracy: 0.9333 - val_loss: 0.4592 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.4189 - val_accuracy: 0.9333 - val_loss: 0.4476 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.4072 - val_accuracy: 0.9333 - val_loss: 0.4359 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9682 - loss: 0.3959 - val_accuracy: 0.9333 - val_loss: 0.4247 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3848 - val_accuracy: 0.9333 - val_loss: 0.4142 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3741 - val_accuracy: 0.9333 - val_loss: 0.4043 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3637 - val_accuracy: 0.9333 - val_loss: 0.3940 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3538 - val_accuracy: 0.9333 - val_loss: 0.3844 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3442 - val_accuracy: 0.9333 - val_loss: 0.3749 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9682 - loss: 0.3350 - val_accuracy: 0.9333 - val_loss: 0.3666 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3261 - val_accuracy: 0.9333 - val_loss: 0.3581 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.3176 - val_accuracy: 0.9333 - val_loss: 0.3500 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9682 - loss: 0.3093 - val_accuracy: 0.9333 - val_loss: 0.3414 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9682 - loss: 0.3013 - val_accuracy: 0.9333 - val_loss: 0.3336 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.2936 - val_accuracy: 0.9333 - val_loss: 0.3256 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9682 - loss: 0.2861 - val_accuracy: 0.9333 - val_loss: 0.3178 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.2790 - val_accuracy: 0.9333 - val_loss: 0.3114 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.2720 - val_accuracy: 0.9333 - val_loss: 0.3040 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.2655 - val_accuracy: 0.9333 - val_loss: 0.2975 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.2592 - val_accuracy: 0.9333 - val_loss: 0.2913 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9682 - loss: 0.2532 - val_accuracy: 0.9333 - val_loss: 0.2860 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9754 - loss: 0.2473 - val_accuracy: 0.9333 - val_loss: 0.2798 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2420 - val_accuracy: 0.9333 - val_loss: 0.2742 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2366 - val_accuracy: 0.9333 - val_loss: 0.2686 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2316 - val_accuracy: 0.9333 - val_loss: 0.2638 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2267 - val_accuracy: 0.9333 - val_loss: 0.2590 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2221 - val_accuracy: 0.9333 - val_loss: 0.2539 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2176 - val_accuracy: 0.9333 - val_loss: 0.2502 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9754 - loss: 0.2131 - val_accuracy: 0.9333 - val_loss: 0.2454 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2091 - val_accuracy: 0.9333 - val_loss: 0.2410 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.2050 - val_accuracy: 0.9333 - val_loss: 0.2365 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 3: 0.9333\n",
|
||
"\n",
|
||
"Fold 4\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - accuracy: 0.2381 - loss: 1.7027 - val_accuracy: 0.2000 - val_loss: 1.6279 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3094 - loss: 1.5757 - val_accuracy: 0.2667 - val_loss: 1.5569 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3720 - loss: 1.5237 - val_accuracy: 0.4333 - val_loss: 1.5255 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4240 - loss: 1.4880 - val_accuracy: 0.4000 - val_loss: 1.4992 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4240 - loss: 1.4523 - val_accuracy: 0.4000 - val_loss: 1.4833 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4273 - loss: 1.4281 - val_accuracy: 0.4000 - val_loss: 1.4681 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4416 - loss: 1.4024 - val_accuracy: 0.4333 - val_loss: 1.4519 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4742 - loss: 1.3824 - val_accuracy: 0.4333 - val_loss: 1.4339 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4832 - loss: 1.3603 - val_accuracy: 0.4333 - val_loss: 1.4194 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4832 - loss: 1.3395 - val_accuracy: 0.4333 - val_loss: 1.4052 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4832 - loss: 1.3166 - val_accuracy: 0.4333 - val_loss: 1.3917 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4832 - loss: 1.2969 - val_accuracy: 0.4333 - val_loss: 1.3760 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4832 - loss: 1.2732 - val_accuracy: 0.4333 - val_loss: 1.3642 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4832 - loss: 1.2540 - val_accuracy: 0.4333 - val_loss: 1.3486 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4832 - loss: 1.2324 - val_accuracy: 0.4333 - val_loss: 1.3345 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4832 - loss: 1.2111 - val_accuracy: 0.4333 - val_loss: 1.3191 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4832 - loss: 1.1917 - val_accuracy: 0.4333 - val_loss: 1.3036 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4948 - loss: 1.1707 - val_accuracy: 0.4333 - val_loss: 1.2894 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4981 - loss: 1.1521 - val_accuracy: 0.4333 - val_loss: 1.2745 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5135 - loss: 1.1323 - val_accuracy: 0.4667 - val_loss: 1.2566 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5364 - loss: 1.1108 - val_accuracy: 0.4667 - val_loss: 1.2408 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5668 - loss: 1.0915 - val_accuracy: 0.4667 - val_loss: 1.2220 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5668 - loss: 1.0700 - val_accuracy: 0.5000 - val_loss: 1.2045 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5734 - loss: 1.0499 - val_accuracy: 0.5000 - val_loss: 1.1919 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5734 - loss: 1.0333 - val_accuracy: 0.5333 - val_loss: 1.1727 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6001 - loss: 1.0104 - val_accuracy: 0.5333 - val_loss: 1.1583 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6197 - loss: 0.9960 - val_accuracy: 0.5333 - val_loss: 1.1385 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6197 - loss: 0.9738 - val_accuracy: 0.5333 - val_loss: 1.1251 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6268 - loss: 0.9596 - val_accuracy: 0.5667 - val_loss: 1.1047 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6268 - loss: 0.9384 - val_accuracy: 0.6000 - val_loss: 1.0886 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6702 - loss: 0.9216 - val_accuracy: 0.7000 - val_loss: 1.0717 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6934 - loss: 0.9034 - val_accuracy: 0.7000 - val_loss: 1.0526 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7317 - loss: 0.8860 - val_accuracy: 0.7333 - val_loss: 1.0332 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7389 - loss: 0.8686 - val_accuracy: 0.7333 - val_loss: 1.0149 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7686 - loss: 0.8518 - val_accuracy: 0.7333 - val_loss: 0.9965 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7686 - loss: 0.8334 - val_accuracy: 0.7333 - val_loss: 0.9795 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7686 - loss: 0.8174 - val_accuracy: 0.7333 - val_loss: 0.9605 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7686 - loss: 0.8009 - val_accuracy: 0.7333 - val_loss: 0.9411 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7686 - loss: 0.7832 - val_accuracy: 0.7333 - val_loss: 0.9226 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7709 - loss: 0.7682 - val_accuracy: 0.7333 - val_loss: 0.9045 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8055 - loss: 0.7525 - val_accuracy: 0.7333 - val_loss: 0.8842 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8055 - loss: 0.7360 - val_accuracy: 0.7333 - val_loss: 0.8656 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8217 - loss: 0.7205 - val_accuracy: 0.7333 - val_loss: 0.8463 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8371 - loss: 0.7052 - val_accuracy: 0.7333 - val_loss: 0.8291 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8415 - loss: 0.6908 - val_accuracy: 0.7333 - val_loss: 0.8090 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8448 - loss: 0.6752 - val_accuracy: 0.7333 - val_loss: 0.7918 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8735 - loss: 0.6609 - val_accuracy: 0.7333 - val_loss: 0.7725 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8625 - loss: 0.6465 - val_accuracy: 0.7333 - val_loss: 0.7576 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9137 - loss: 0.6339 - val_accuracy: 0.8333 - val_loss: 0.7379 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9170 - loss: 0.6174 - val_accuracy: 0.8333 - val_loss: 0.7224 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9170 - loss: 0.6061 - val_accuracy: 0.8333 - val_loss: 0.7035 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9170 - loss: 0.5909 - val_accuracy: 0.8333 - val_loss: 0.6887 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9376 - loss: 0.5791 - val_accuracy: 0.8333 - val_loss: 0.6710 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9376 - loss: 0.5658 - val_accuracy: 0.8333 - val_loss: 0.6576 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9376 - loss: 0.5536 - val_accuracy: 0.8333 - val_loss: 0.6398 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9376 - loss: 0.5407 - val_accuracy: 0.8333 - val_loss: 0.6265 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9400 - loss: 0.5294 - val_accuracy: 0.8333 - val_loss: 0.6068 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9400 - loss: 0.5198 - val_accuracy: 0.8333 - val_loss: 0.5960 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9629 - loss: 0.5039 - val_accuracy: 0.8333 - val_loss: 0.5817 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9400 - loss: 0.4968 - val_accuracy: 0.8333 - val_loss: 0.5669 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9629 - loss: 0.4813 - val_accuracy: 0.8333 - val_loss: 0.5536 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9629 - loss: 0.4737 - val_accuracy: 0.9000 - val_loss: 0.5409 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9673 - loss: 0.4606 - val_accuracy: 0.9000 - val_loss: 0.5271 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9764 - loss: 0.4528 - val_accuracy: 0.9000 - val_loss: 0.5156 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9779 - loss: 0.4404 - val_accuracy: 0.9667 - val_loss: 0.5043 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9779 - loss: 0.4338 - val_accuracy: 0.9667 - val_loss: 0.4911 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9779 - loss: 0.4209 - val_accuracy: 0.9667 - val_loss: 0.4813 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9779 - loss: 0.4148 - val_accuracy: 0.9667 - val_loss: 0.4682 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9779 - loss: 0.4030 - val_accuracy: 0.9667 - val_loss: 0.4592 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9850 - loss: 0.3975 - val_accuracy: 0.9667 - val_loss: 0.4486 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9850 - loss: 0.3862 - val_accuracy: 0.9667 - val_loss: 0.4384 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9850 - loss: 0.3802 - val_accuracy: 0.9667 - val_loss: 0.4295 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3713 - val_accuracy: 0.9667 - val_loss: 0.4191 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9866 - loss: 0.3634 - val_accuracy: 0.9667 - val_loss: 0.4113 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3572 - val_accuracy: 0.9667 - val_loss: 0.4013 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3480 - val_accuracy: 0.9667 - val_loss: 0.3940 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3425 - val_accuracy: 0.9667 - val_loss: 0.3849 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9866 - loss: 0.3355 - val_accuracy: 0.9667 - val_loss: 0.3775 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3287 - val_accuracy: 0.9667 - val_loss: 0.3698 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3218 - val_accuracy: 0.9667 - val_loss: 0.3627 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3162 - val_accuracy: 0.9667 - val_loss: 0.3552 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.3094 - val_accuracy: 0.9667 - val_loss: 0.3491 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.3041 - val_accuracy: 0.9667 - val_loss: 0.3418 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2979 - val_accuracy: 0.9667 - val_loss: 0.3361 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2927 - val_accuracy: 0.9667 - val_loss: 0.3294 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2871 - val_accuracy: 0.9667 - val_loss: 0.3233 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2818 - val_accuracy: 0.9667 - val_loss: 0.3179 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2763 - val_accuracy: 0.9667 - val_loss: 0.3127 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.2719 - val_accuracy: 0.9667 - val_loss: 0.3067 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2667 - val_accuracy: 0.9667 - val_loss: 0.3017 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2620 - val_accuracy: 0.9667 - val_loss: 0.2966 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2573 - val_accuracy: 0.9667 - val_loss: 0.2919 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2531 - val_accuracy: 0.9667 - val_loss: 0.2869 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.2485 - val_accuracy: 0.9667 - val_loss: 0.2828 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2447 - val_accuracy: 1.0000 - val_loss: 0.2780 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.2399 - val_accuracy: 1.0000 - val_loss: 0.2741 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2360 - val_accuracy: 1.0000 - val_loss: 0.2702 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2328 - val_accuracy: 1.0000 - val_loss: 0.2650 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2280 - val_accuracy: 1.0000 - val_loss: 0.2618 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2245 - val_accuracy: 1.0000 - val_loss: 0.2575 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 4: 1.0000\n",
|
||
"\n",
|
||
"Fold 5\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - accuracy: 0.2381 - loss: 2.8718 - val_accuracy: 0.2000 - val_loss: 2.2220 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2381 - loss: 2.0198 - val_accuracy: 0.2000 - val_loss: 1.7186 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2453 - loss: 1.6595 - val_accuracy: 0.3000 - val_loss: 1.5217 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4243 - loss: 1.5506 - val_accuracy: 0.3667 - val_loss: 1.4919 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3874 - loss: 1.5526 - val_accuracy: 0.3333 - val_loss: 1.4863 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2990 - loss: 1.5517 - val_accuracy: 0.3333 - val_loss: 1.4709 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2990 - loss: 1.5340 - val_accuracy: 0.3333 - val_loss: 1.4541 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2990 - loss: 1.5138 - val_accuracy: 0.3333 - val_loss: 1.4399 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2990 - loss: 1.4968 - val_accuracy: 0.3333 - val_loss: 1.4265 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.2990 - loss: 1.4827 - val_accuracy: 0.3333 - val_loss: 1.4138 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2990 - loss: 1.4705 - val_accuracy: 0.3333 - val_loss: 1.4015 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3196 - loss: 1.4580 - val_accuracy: 0.3333 - val_loss: 1.3891 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3476 - loss: 1.4450 - val_accuracy: 0.3333 - val_loss: 1.3765 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3476 - loss: 1.4326 - val_accuracy: 0.3667 - val_loss: 1.3641 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3629 - loss: 1.4197 - val_accuracy: 0.3667 - val_loss: 1.3515 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3629 - loss: 1.4079 - val_accuracy: 0.3667 - val_loss: 1.3393 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3701 - loss: 1.3967 - val_accuracy: 0.3667 - val_loss: 1.3275 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3701 - loss: 1.3848 - val_accuracy: 0.3667 - val_loss: 1.3158 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3716 - loss: 1.3741 - val_accuracy: 0.3667 - val_loss: 1.3044 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3716 - loss: 1.3632 - val_accuracy: 0.3667 - val_loss: 1.2939 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3755 - loss: 1.3521 - val_accuracy: 0.4333 - val_loss: 1.2824 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4849 - loss: 1.3384 - val_accuracy: 0.4333 - val_loss: 1.2709 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4849 - loss: 1.3262 - val_accuracy: 0.4333 - val_loss: 1.2605 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4849 - loss: 1.3170 - val_accuracy: 0.4333 - val_loss: 1.2501 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4826 - loss: 1.3066 - val_accuracy: 0.4333 - val_loss: 1.2356 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4502 - loss: 1.2914 - val_accuracy: 0.4333 - val_loss: 1.2212 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4849 - loss: 1.2749 - val_accuracy: 0.4333 - val_loss: 1.2045 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.4973 - loss: 1.2573 - val_accuracy: 0.5000 - val_loss: 1.1894 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5367 - loss: 1.2399 - val_accuracy: 0.5000 - val_loss: 1.1743 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5518 - loss: 1.2235 - val_accuracy: 0.5000 - val_loss: 1.1604 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5495 - loss: 1.2083 - val_accuracy: 0.6000 - val_loss: 1.1465 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6067 - loss: 1.1931 - val_accuracy: 0.6000 - val_loss: 1.1331 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5779 - loss: 1.1777 - val_accuracy: 0.6000 - val_loss: 1.1204 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5779 - loss: 1.1638 - val_accuracy: 0.6000 - val_loss: 1.1078 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5779 - loss: 1.1496 - val_accuracy: 0.5667 - val_loss: 1.0959 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5707 - loss: 1.1359 - val_accuracy: 0.6000 - val_loss: 1.0839 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5708 - loss: 1.1227 - val_accuracy: 0.6000 - val_loss: 1.0718 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5723 - loss: 1.1103 - val_accuracy: 0.6000 - val_loss: 1.0603 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5823 - loss: 1.0982 - val_accuracy: 0.6000 - val_loss: 1.0494 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5708 - loss: 1.0854 - val_accuracy: 0.6000 - val_loss: 1.0388 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5823 - loss: 1.0742 - val_accuracy: 0.6000 - val_loss: 1.0278 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6316 - loss: 1.0629 - val_accuracy: 0.6667 - val_loss: 1.0175 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6059 - loss: 1.0511 - val_accuracy: 0.7000 - val_loss: 1.0078 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6398 - loss: 1.0417 - val_accuracy: 0.6667 - val_loss: 0.9957 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6304 - loss: 1.0292 - val_accuracy: 0.6667 - val_loss: 0.9805 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6457 - loss: 1.0108 - val_accuracy: 0.6667 - val_loss: 0.9654 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6413 - loss: 0.9990 - val_accuracy: 0.6667 - val_loss: 0.9520 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6846 - loss: 0.9817 - val_accuracy: 0.6667 - val_loss: 0.9446 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6797 - loss: 0.9726 - val_accuracy: 0.6667 - val_loss: 0.9278 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6617 - loss: 0.9576 - val_accuracy: 0.6667 - val_loss: 0.9217 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6797 - loss: 0.9468 - val_accuracy: 0.6667 - val_loss: 0.9036 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6457 - loss: 0.9332 - val_accuracy: 0.6667 - val_loss: 0.8981 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7100 - loss: 0.9222 - val_accuracy: 0.6667 - val_loss: 0.8801 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6413 - loss: 0.9090 - val_accuracy: 0.6667 - val_loss: 0.8695 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6714 - loss: 0.8958 - val_accuracy: 0.6667 - val_loss: 0.8591 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6413 - loss: 0.8852 - val_accuracy: 0.6667 - val_loss: 0.8460 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6758 - loss: 0.8714 - val_accuracy: 0.6667 - val_loss: 0.8357 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6738 - loss: 0.8612 - val_accuracy: 0.6667 - val_loss: 0.8248 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6782 - loss: 0.8503 - val_accuracy: 0.7667 - val_loss: 0.8133 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6782 - loss: 0.8394 - val_accuracy: 0.7667 - val_loss: 0.8029 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6882 - loss: 0.8281 - val_accuracy: 0.7667 - val_loss: 0.7912 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6882 - loss: 0.8168 - val_accuracy: 0.7667 - val_loss: 0.7815 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7088 - loss: 0.8061 - val_accuracy: 0.7667 - val_loss: 0.7700 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6998 - loss: 0.7959 - val_accuracy: 0.7667 - val_loss: 0.7598 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7376 - loss: 0.7836 - val_accuracy: 0.8333 - val_loss: 0.7488 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7530 - loss: 0.7741 - val_accuracy: 0.8000 - val_loss: 0.7400 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7756 - loss: 0.7637 - val_accuracy: 0.8333 - val_loss: 0.7286 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7856 - loss: 0.7527 - val_accuracy: 0.8333 - val_loss: 0.7198 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8053 - loss: 0.7425 - val_accuracy: 0.8333 - val_loss: 0.7087 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8097 - loss: 0.7333 - val_accuracy: 0.8333 - val_loss: 0.6998 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8097 - loss: 0.7219 - val_accuracy: 0.8333 - val_loss: 0.6896 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8278 - loss: 0.7128 - val_accuracy: 0.8333 - val_loss: 0.6800 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8097 - loss: 0.7018 - val_accuracy: 0.8667 - val_loss: 0.6701 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8334 - loss: 0.6929 - val_accuracy: 0.8667 - val_loss: 0.6608 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8334 - loss: 0.6822 - val_accuracy: 0.8667 - val_loss: 0.6507 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8334 - loss: 0.6723 - val_accuracy: 0.8667 - val_loss: 0.6433 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8358 - loss: 0.6633 - val_accuracy: 0.8667 - val_loss: 0.6322 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8334 - loss: 0.6536 - val_accuracy: 0.8667 - val_loss: 0.6267 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8358 - loss: 0.6453 - val_accuracy: 0.8667 - val_loss: 0.6150 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8391 - loss: 0.6352 - val_accuracy: 0.8667 - val_loss: 0.6077 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8635 - loss: 0.6256 - val_accuracy: 0.8667 - val_loss: 0.5985 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8635 - loss: 0.6176 - val_accuracy: 0.8667 - val_loss: 0.5906 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8635 - loss: 0.6070 - val_accuracy: 0.8667 - val_loss: 0.5823 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8770 - loss: 0.5993 - val_accuracy: 0.8667 - val_loss: 0.5750 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8770 - loss: 0.5901 - val_accuracy: 0.8667 - val_loss: 0.5661 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8770 - loss: 0.5821 - val_accuracy: 0.9000 - val_loss: 0.5588 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8770 - loss: 0.5726 - val_accuracy: 0.8667 - val_loss: 0.5512 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8785 - loss: 0.5652 - val_accuracy: 0.9000 - val_loss: 0.5439 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8785 - loss: 0.5563 - val_accuracy: 0.9000 - val_loss: 0.5365 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8785 - loss: 0.5489 - val_accuracy: 0.9000 - val_loss: 0.5286 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8785 - loss: 0.5404 - val_accuracy: 0.9000 - val_loss: 0.5211 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8785 - loss: 0.5324 - val_accuracy: 0.9000 - val_loss: 0.5144 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8785 - loss: 0.5251 - val_accuracy: 0.9000 - val_loss: 0.5060 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8785 - loss: 0.5167 - val_accuracy: 0.9000 - val_loss: 0.4993 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8809 - loss: 0.5092 - val_accuracy: 0.9000 - val_loss: 0.4932 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8809 - loss: 0.5025 - val_accuracy: 0.9333 - val_loss: 0.4856 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.4946 - val_accuracy: 0.9333 - val_loss: 0.4796 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9071 - loss: 0.4876 - val_accuracy: 0.9333 - val_loss: 0.4726 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9071 - loss: 0.4811 - val_accuracy: 0.9333 - val_loss: 0.4660 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9071 - loss: 0.4734 - val_accuracy: 0.9333 - val_loss: 0.4603 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 5: 0.9333\n",
|
||
"\n",
|
||
"Average Accuracy: 0.9533\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Visualisasi akurasi tiap fold\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.plot(range(1, len(accuracies) + 1), accuracies, marker='o', linestyle='-', color='blue', label='Fold Accuracy')\n",
|
||
"plt.axhline(np.mean(accuracies), color='red', linestyle='--', label=f'Average Accuracy: {np.mean(accuracies):.4f}')\n",
|
||
"plt.title(\"Validation Accuracy per Fold\")\n",
|
||
"plt.xlabel(\"Fold\")\n",
|
||
"plt.ylabel(\"Accuracy\")\n",
|
||
"plt.xticks(range(1, len(accuracies) + 1))\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 485
|
||
},
|
||
"id": "IFMMmT68OwYL",
|
||
"outputId": "f29a0c93-f22d-401b-d1f4-af0bbc88c5f4"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Modelling tanpa Maxpooling"
|
||
],
|
||
"metadata": {
|
||
"id": "7_4Xw4XpKzR6"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tensorflow.keras.utils import plot_model\n",
|
||
"\n",
|
||
"def build_model():\n",
|
||
" model = Sequential()\n",
|
||
"\n",
|
||
" # Input layer\n",
|
||
" model.add(Input(shape=(18, 1)))\n",
|
||
" model.add(Conv1D(\n",
|
||
" filters=16, #\n",
|
||
" kernel_size=2,\n",
|
||
" activation='relu',\n",
|
||
" kernel_regularizer=l2(0.001) # Nilai tetap untuk L2 regularization\n",
|
||
" ))\n",
|
||
" model.add(Flatten())\n",
|
||
" model.add(Dense(\n",
|
||
" units=16,\n",
|
||
" activation='relu',\n",
|
||
" kernel_regularizer=l2(0.001) # Nilai tetap untuk L2 regularization\n",
|
||
" ))\n",
|
||
" # model.add(Dropout(0.4)) # Nilai tetap untuk dropout\n",
|
||
"\n",
|
||
" # Output layer\n",
|
||
" model.add(Dense(len(classes), activation='softmax'))\n",
|
||
"\n",
|
||
" # Optimizer dengan nilai tetap\n",
|
||
" optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
|
||
"\n",
|
||
" # Compile model\n",
|
||
" model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
|
||
" metrics=['accuracy']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",\n",
|
||
" patience=5,\n",
|
||
" verbose=1)]\n",
|
||
"\n",
|
||
"early_model = build_model()\n",
|
||
"# Plot and save the model architecture\n",
|
||
"plot_model(early_model, to_file=\"early_model_plot.png\", show_shapes=True, show_layer_names=True, show_layer_activations=True)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "fuw_Kz64K1O5",
|
||
"outputId": "223c3466-2c1d-424f-9180-577fee190125"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<IPython.core.display.Image object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 21
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"X = df.iloc[:, :-2].values\n",
|
||
"y = df['Label'].values\n",
|
||
"accuracies = []\n",
|
||
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
|
||
"for fold, (train_idx, val_idx) in enumerate(skf.split(X, y), 1):\n",
|
||
" print(f\"\\nFold {fold}\")\n",
|
||
"\n",
|
||
" # Split data\n",
|
||
" X_train, X_val = X[train_idx], X[val_idx]\n",
|
||
" y_train, y_val = y[train_idx], y[val_idx]\n",
|
||
"\n",
|
||
" # Convert to tf.data.Dataset\n",
|
||
" train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
|
||
" val_ds = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
|
||
"\n",
|
||
" # Shuffle, batch, cache, prefetch\n",
|
||
" train_ds = train_ds.shuffle(buffer_size=len(X_train), seed=42) \\\n",
|
||
" .batch(12) \\\n",
|
||
" .cache() \\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" val_ds = val_ds.batch(12).cache().prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" model = build_model()\n",
|
||
" hist = model.fit(train_ds, epochs=100, validation_data=val_ds, callbacks=callbacks)\n",
|
||
"\n",
|
||
" plt.figure(figsize=(12, 5))\n",
|
||
" # Plot untuk accuracy dan val_accuracy\n",
|
||
" plt.title(\"Accuracy and Val Accuracy\")\n",
|
||
" plt.plot(hist.history['accuracy'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
" plt.plot(hist.history['val_accuracy'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
" plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
" plt.xlabel('Epoch')\n",
|
||
" plt.ylabel('Accuracy')\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # Evaluate\n",
|
||
" loss, acc = model.evaluate(val_ds, verbose=0)\n",
|
||
" print(f\"Accuracy Fold {fold}: {acc:.4f}\")\n",
|
||
"\n",
|
||
" accuracies.append(acc)\n",
|
||
"\n",
|
||
"print(f\"\\nAverage Accuracy: {np.mean(accuracies):.4f}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "8AqrIdbTLJlf",
|
||
"outputId": "8dbfdfd8-0685-4c27-a9db-6553ea8a7443"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"Fold 1\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 57ms/step - accuracy: 0.1274 - loss: 1.8094 - val_accuracy: 0.2000 - val_loss: 1.8223 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1859 - loss: 1.6491 - val_accuracy: 0.2000 - val_loss: 1.6815 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1907 - loss: 1.5770 - val_accuracy: 0.2333 - val_loss: 1.5791 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.2969 - loss: 1.5192 - val_accuracy: 0.4667 - val_loss: 1.5119 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3669 - loss: 1.4743 - val_accuracy: 0.4000 - val_loss: 1.4676 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3701 - loss: 1.4346 - val_accuracy: 0.5000 - val_loss: 1.4356 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3826 - loss: 1.3924 - val_accuracy: 0.5000 - val_loss: 1.3992 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4768 - loss: 1.3526 - val_accuracy: 0.4667 - val_loss: 1.3619 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4659 - loss: 1.3160 - val_accuracy: 0.4667 - val_loss: 1.3234 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4945 - loss: 1.2761 - val_accuracy: 0.5000 - val_loss: 1.2899 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5363 - loss: 1.2385 - val_accuracy: 0.5333 - val_loss: 1.2560 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6263 - loss: 1.2030 - val_accuracy: 0.5333 - val_loss: 1.2225 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7052 - loss: 1.1646 - val_accuracy: 0.5333 - val_loss: 1.1880 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7446 - loss: 1.1278 - val_accuracy: 0.6000 - val_loss: 1.1548 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8016 - loss: 1.0890 - val_accuracy: 0.6000 - val_loss: 1.1187 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7998 - loss: 1.0494 - val_accuracy: 0.6333 - val_loss: 1.0821 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7860 - loss: 1.0090 - val_accuracy: 0.6333 - val_loss: 1.0478 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7816 - loss: 0.9694 - val_accuracy: 0.6333 - val_loss: 1.0131 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7860 - loss: 0.9309 - val_accuracy: 0.6333 - val_loss: 0.9789 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8099 - loss: 0.8915 - val_accuracy: 0.6333 - val_loss: 0.9471 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8002 - loss: 0.8546 - val_accuracy: 0.6000 - val_loss: 0.9122 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8450 - loss: 0.8181 - val_accuracy: 0.6333 - val_loss: 0.8782 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.7842 - val_accuracy: 0.6667 - val_loss: 0.8482 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.7489 - val_accuracy: 0.6667 - val_loss: 0.8158 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.7158 - val_accuracy: 0.7000 - val_loss: 0.7860 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.6843 - val_accuracy: 0.7000 - val_loss: 0.7571 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9116 - loss: 0.6529 - val_accuracy: 0.7000 - val_loss: 0.7294 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9116 - loss: 0.6235 - val_accuracy: 0.7000 - val_loss: 0.7012 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9465 - loss: 0.5952 - val_accuracy: 0.7667 - val_loss: 0.6750 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9465 - loss: 0.5696 - val_accuracy: 0.7667 - val_loss: 0.6486 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9465 - loss: 0.5439 - val_accuracy: 0.7667 - val_loss: 0.6218 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9465 - loss: 0.5216 - val_accuracy: 0.7667 - val_loss: 0.5989 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9465 - loss: 0.4985 - val_accuracy: 0.8333 - val_loss: 0.5757 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9581 - loss: 0.4771 - val_accuracy: 0.8333 - val_loss: 0.5527 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9581 - loss: 0.4575 - val_accuracy: 0.8333 - val_loss: 0.5313 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9581 - loss: 0.4379 - val_accuracy: 0.8333 - val_loss: 0.5068 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9581 - loss: 0.4204 - val_accuracy: 0.9000 - val_loss: 0.4877 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9653 - loss: 0.4027 - val_accuracy: 0.9000 - val_loss: 0.4696 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.3863 - val_accuracy: 0.9000 - val_loss: 0.4495 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9784 - loss: 0.3711 - val_accuracy: 0.9000 - val_loss: 0.4314 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9784 - loss: 0.3570 - val_accuracy: 0.9000 - val_loss: 0.4142 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9784 - loss: 0.3427 - val_accuracy: 0.9000 - val_loss: 0.3983 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9784 - loss: 0.3297 - val_accuracy: 0.9000 - val_loss: 0.3805 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9784 - loss: 0.3173 - val_accuracy: 0.9667 - val_loss: 0.3661 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9784 - loss: 0.3060 - val_accuracy: 0.9667 - val_loss: 0.3524 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2951 - val_accuracy: 0.9667 - val_loss: 0.3387 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.2851 - val_accuracy: 0.9667 - val_loss: 0.3253 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.2755 - val_accuracy: 0.9667 - val_loss: 0.3146 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2664 - val_accuracy: 0.9667 - val_loss: 0.3027 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2579 - val_accuracy: 0.9667 - val_loss: 0.2921 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2496 - val_accuracy: 0.9667 - val_loss: 0.2804 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.2418 - val_accuracy: 0.9667 - val_loss: 0.2713 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.2344 - val_accuracy: 0.9667 - val_loss: 0.2618 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2273 - val_accuracy: 0.9667 - val_loss: 0.2541 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2206 - val_accuracy: 0.9667 - val_loss: 0.2447 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.2141 - val_accuracy: 0.9667 - val_loss: 0.2366 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.2081 - val_accuracy: 0.9667 - val_loss: 0.2283 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2020 - val_accuracy: 0.9667 - val_loss: 0.2150 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.1958 - val_accuracy: 0.9667 - val_loss: 0.2065 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.1902 - val_accuracy: 0.9667 - val_loss: 0.2016 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.1842 - val_accuracy: 0.9667 - val_loss: 0.1939 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.1793 - val_accuracy: 0.9667 - val_loss: 0.1887 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.1743 - val_accuracy: 0.9667 - val_loss: 0.1830 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.1695 - val_accuracy: 0.9667 - val_loss: 0.1772 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.1651 - val_accuracy: 0.9667 - val_loss: 0.1717 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.1609 - val_accuracy: 0.9667 - val_loss: 0.1670 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.1568 - val_accuracy: 0.9667 - val_loss: 0.1633 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.1527 - val_accuracy: 0.9667 - val_loss: 0.1581 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9828 - loss: 0.1491 - val_accuracy: 1.0000 - val_loss: 0.1544 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.1453 - val_accuracy: 1.0000 - val_loss: 0.1498 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.1420 - val_accuracy: 1.0000 - val_loss: 0.1473 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.1390 - val_accuracy: 1.0000 - val_loss: 0.1442 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.1369 - val_accuracy: 1.0000 - val_loss: 0.1407 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.1328 - val_accuracy: 1.0000 - val_loss: 0.1370 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.1300 - val_accuracy: 1.0000 - val_loss: 0.1336 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.1271 - val_accuracy: 1.0000 - val_loss: 0.1309 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9884 - loss: 0.1242 - val_accuracy: 1.0000 - val_loss: 0.1267 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.1255 - val_accuracy: 1.0000 - val_loss: 0.1271 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9884 - loss: 0.1190 - val_accuracy: 1.0000 - val_loss: 0.1208 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1174 - val_accuracy: 1.0000 - val_loss: 0.1198 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.1146 - val_accuracy: 1.0000 - val_loss: 0.1171 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1123 - val_accuracy: 1.0000 - val_loss: 0.1145 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1101 - val_accuracy: 1.0000 - val_loss: 0.1128 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1081 - val_accuracy: 1.0000 - val_loss: 0.1102 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1062 - val_accuracy: 1.0000 - val_loss: 0.1085 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1042 - val_accuracy: 1.0000 - val_loss: 0.1061 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1025 - val_accuracy: 1.0000 - val_loss: 0.1048 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1006 - val_accuracy: 1.0000 - val_loss: 0.1024 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0991 - val_accuracy: 1.0000 - val_loss: 0.1009 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0974 - val_accuracy: 1.0000 - val_loss: 0.0995 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0959 - val_accuracy: 1.0000 - val_loss: 0.0977 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0944 - val_accuracy: 1.0000 - val_loss: 0.0964 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0929 - val_accuracy: 1.0000 - val_loss: 0.0946 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0916 - val_accuracy: 1.0000 - val_loss: 0.0934 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0903 - val_accuracy: 1.0000 - val_loss: 0.0919 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0890 - val_accuracy: 1.0000 - val_loss: 0.0907 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0878 - val_accuracy: 1.0000 - val_loss: 0.0897 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0866 - val_accuracy: 1.0000 - val_loss: 0.0882 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0855 - val_accuracy: 1.0000 - val_loss: 0.0873 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0844 - val_accuracy: 1.0000 - val_loss: 0.0859 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 1: 1.0000\n",
|
||
"\n",
|
||
"Fold 2\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - accuracy: 0.2112 - loss: 1.7072 - val_accuracy: 0.3333 - val_loss: 1.5664 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2490 - loss: 1.5917 - val_accuracy: 0.4667 - val_loss: 1.4792 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4092 - loss: 1.5401 - val_accuracy: 0.7000 - val_loss: 1.4181 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5381 - loss: 1.4860 - val_accuracy: 0.6667 - val_loss: 1.3673 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5545 - loss: 1.4387 - val_accuracy: 0.7000 - val_loss: 1.3157 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5776 - loss: 1.3918 - val_accuracy: 0.7000 - val_loss: 1.2704 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6190 - loss: 1.3436 - val_accuracy: 0.8333 - val_loss: 1.2230 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7319 - loss: 1.2959 - val_accuracy: 0.7667 - val_loss: 1.1794 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7295 - loss: 1.2487 - val_accuracy: 0.7333 - val_loss: 1.1355 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7239 - loss: 1.2029 - val_accuracy: 0.7000 - val_loss: 1.0965 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6882 - loss: 1.1565 - val_accuracy: 0.6667 - val_loss: 1.0522 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6806 - loss: 1.1094 - val_accuracy: 0.6333 - val_loss: 1.0102 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7074 - loss: 1.0619 - val_accuracy: 0.7000 - val_loss: 0.9734 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7381 - loss: 1.0143 - val_accuracy: 0.6000 - val_loss: 0.9321 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7535 - loss: 0.9674 - val_accuracy: 0.7000 - val_loss: 0.8932 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7381 - loss: 0.9228 - val_accuracy: 0.6000 - val_loss: 0.8567 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7591 - loss: 0.8843 - val_accuracy: 0.7667 - val_loss: 0.8209 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8291 - loss: 0.8401 - val_accuracy: 0.7000 - val_loss: 0.7861 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8291 - loss: 0.8021 - val_accuracy: 0.8000 - val_loss: 0.7497 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8386 - loss: 0.7604 - val_accuracy: 0.8000 - val_loss: 0.7196 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8409 - loss: 0.7250 - val_accuracy: 0.8000 - val_loss: 0.6886 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8639 - loss: 0.6886 - val_accuracy: 0.8333 - val_loss: 0.6557 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8639 - loss: 0.6547 - val_accuracy: 0.8333 - val_loss: 0.6284 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8698 - loss: 0.6248 - val_accuracy: 0.9000 - val_loss: 0.5984 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8742 - loss: 0.5928 - val_accuracy: 0.9000 - val_loss: 0.5737 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8757 - loss: 0.5675 - val_accuracy: 0.9000 - val_loss: 0.5468 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8873 - loss: 0.5387 - val_accuracy: 0.9333 - val_loss: 0.5216 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8873 - loss: 0.5124 - val_accuracy: 0.9000 - val_loss: 0.5023 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8896 - loss: 0.4908 - val_accuracy: 0.9667 - val_loss: 0.4792 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9355 - loss: 0.4655 - val_accuracy: 0.9667 - val_loss: 0.4585 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9376 - loss: 0.4455 - val_accuracy: 0.9333 - val_loss: 0.4391 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9178 - loss: 0.4390 - val_accuracy: 0.9667 - val_loss: 0.4234 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9456 - loss: 0.4003 - val_accuracy: 0.9667 - val_loss: 0.4064 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9483 - loss: 0.3898 - val_accuracy: 0.9667 - val_loss: 0.3865 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9483 - loss: 0.3660 - val_accuracy: 0.9667 - val_loss: 0.3735 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9637 - loss: 0.3536 - val_accuracy: 1.0000 - val_loss: 0.3569 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9637 - loss: 0.3358 - val_accuracy: 1.0000 - val_loss: 0.3448 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9681 - loss: 0.3234 - val_accuracy: 1.0000 - val_loss: 0.3312 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9737 - loss: 0.3076 - val_accuracy: 1.0000 - val_loss: 0.3189 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9737 - loss: 0.2960 - val_accuracy: 1.0000 - val_loss: 0.3087 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.2832 - val_accuracy: 1.0000 - val_loss: 0.2967 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.2705 - val_accuracy: 1.0000 - val_loss: 0.2862 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.2607 - val_accuracy: 1.0000 - val_loss: 0.2752 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.2486 - val_accuracy: 1.0000 - val_loss: 0.2662 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.2396 - val_accuracy: 1.0000 - val_loss: 0.2557 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.2299 - val_accuracy: 1.0000 - val_loss: 0.2501 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.2206 - val_accuracy: 1.0000 - val_loss: 0.2382 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.2137 - val_accuracy: 1.0000 - val_loss: 0.2345 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9967 - loss: 0.2035 - val_accuracy: 1.0000 - val_loss: 0.2235 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.1976 - val_accuracy: 1.0000 - val_loss: 0.2204 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9967 - loss: 0.1894 - val_accuracy: 1.0000 - val_loss: 0.2090 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.1835 - val_accuracy: 1.0000 - val_loss: 0.2070 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9967 - loss: 0.1778 - val_accuracy: 1.0000 - val_loss: 0.1968 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1719 - val_accuracy: 1.0000 - val_loss: 0.1938 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1660 - val_accuracy: 1.0000 - val_loss: 0.1856 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1614 - val_accuracy: 1.0000 - val_loss: 0.1840 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1558 - val_accuracy: 1.0000 - val_loss: 0.1779 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1526 - val_accuracy: 1.0000 - val_loss: 0.1720 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1480 - val_accuracy: 1.0000 - val_loss: 0.1675 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1434 - val_accuracy: 1.0000 - val_loss: 0.1671 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1392 - val_accuracy: 1.0000 - val_loss: 0.1583 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1374 - val_accuracy: 1.0000 - val_loss: 0.1578 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1326 - val_accuracy: 1.0000 - val_loss: 0.1535 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1297 - val_accuracy: 1.0000 - val_loss: 0.1492 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1271 - val_accuracy: 1.0000 - val_loss: 0.1463 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1238 - val_accuracy: 1.0000 - val_loss: 0.1438 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1208 - val_accuracy: 1.0000 - val_loss: 0.1398 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1189 - val_accuracy: 1.0000 - val_loss: 0.1365 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1158 - val_accuracy: 1.0000 - val_loss: 0.1367 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1131 - val_accuracy: 1.0000 - val_loss: 0.1313 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1120 - val_accuracy: 1.0000 - val_loss: 0.1307 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1087 - val_accuracy: 1.0000 - val_loss: 0.1267 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1072 - val_accuracy: 1.0000 - val_loss: 0.1256 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1052 - val_accuracy: 1.0000 - val_loss: 0.1218 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1034 - val_accuracy: 1.0000 - val_loss: 0.1213 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1017 - val_accuracy: 1.0000 - val_loss: 0.1193 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0997 - val_accuracy: 1.0000 - val_loss: 0.1180 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0984 - val_accuracy: 1.0000 - val_loss: 0.1136 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0970 - val_accuracy: 1.0000 - val_loss: 0.1130 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0951 - val_accuracy: 1.0000 - val_loss: 0.1122 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0939 - val_accuracy: 1.0000 - val_loss: 0.1103 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0926 - val_accuracy: 1.0000 - val_loss: 0.1084 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0909 - val_accuracy: 1.0000 - val_loss: 0.1079 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0898 - val_accuracy: 1.0000 - val_loss: 0.1041 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0890 - val_accuracy: 1.0000 - val_loss: 0.1047 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0874 - val_accuracy: 1.0000 - val_loss: 0.1032 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0860 - val_accuracy: 1.0000 - val_loss: 0.1021 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.0852 - val_accuracy: 1.0000 - val_loss: 0.0991 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0844 - val_accuracy: 1.0000 - val_loss: 0.0989 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0830 - val_accuracy: 1.0000 - val_loss: 0.0982 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0820 - val_accuracy: 1.0000 - val_loss: 0.0971 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0812 - val_accuracy: 1.0000 - val_loss: 0.0954 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0802 - val_accuracy: 1.0000 - val_loss: 0.0953 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0793 - val_accuracy: 1.0000 - val_loss: 0.0925 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0788 - val_accuracy: 1.0000 - val_loss: 0.0930 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0776 - val_accuracy: 1.0000 - val_loss: 0.0921 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0768 - val_accuracy: 1.0000 - val_loss: 0.0911 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0761 - val_accuracy: 1.0000 - val_loss: 0.0890 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0755 - val_accuracy: 1.0000 - val_loss: 0.0886 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0747 - val_accuracy: 1.0000 - val_loss: 0.0889 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 2: 1.0000\n",
|
||
"\n",
|
||
"Fold 3\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 94ms/step - accuracy: 0.2443 - loss: 2.1328 - val_accuracy: 0.1667 - val_loss: 1.8010 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.2482 - loss: 1.6734 - val_accuracy: 0.0667 - val_loss: 1.6752 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.2954 - loss: 1.6132 - val_accuracy: 0.0667 - val_loss: 1.6506 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1603 - loss: 1.6092 - val_accuracy: 0.1333 - val_loss: 1.6163 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2654 - loss: 1.5748 - val_accuracy: 0.1667 - val_loss: 1.5852 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3067 - loss: 1.5384 - val_accuracy: 0.3000 - val_loss: 1.5608 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4051 - loss: 1.5182 - val_accuracy: 0.3333 - val_loss: 1.5323 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4937 - loss: 1.4887 - val_accuracy: 0.3333 - val_loss: 1.5052 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5228 - loss: 1.4563 - val_accuracy: 0.3667 - val_loss: 1.4794 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5912 - loss: 1.4281 - val_accuracy: 0.4333 - val_loss: 1.4497 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6601 - loss: 1.3988 - val_accuracy: 0.4333 - val_loss: 1.4200 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6770 - loss: 1.3673 - val_accuracy: 0.4667 - val_loss: 1.3895 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7377 - loss: 1.3355 - val_accuracy: 0.4667 - val_loss: 1.3540 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7607 - loss: 1.3010 - val_accuracy: 0.5000 - val_loss: 1.3161 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7727 - loss: 1.2653 - val_accuracy: 0.5333 - val_loss: 1.2716 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7714 - loss: 1.2244 - val_accuracy: 0.6000 - val_loss: 1.2232 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8148 - loss: 1.1853 - val_accuracy: 0.6333 - val_loss: 1.1751 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7552 - loss: 1.1421 - val_accuracy: 0.7000 - val_loss: 1.1305 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7871 - loss: 1.1051 - val_accuracy: 0.7667 - val_loss: 1.0838 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7976 - loss: 1.0631 - val_accuracy: 0.8667 - val_loss: 1.0419 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8244 - loss: 1.0271 - val_accuracy: 0.8667 - val_loss: 1.0004 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8047 - loss: 0.9896 - val_accuracy: 0.8667 - val_loss: 0.9511 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8316 - loss: 0.9478 - val_accuracy: 0.8333 - val_loss: 0.9085 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8445 - loss: 0.9112 - val_accuracy: 0.8667 - val_loss: 0.8697 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8445 - loss: 0.8785 - val_accuracy: 0.8667 - val_loss: 0.8328 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8430 - loss: 0.8453 - val_accuracy: 0.9000 - val_loss: 0.7977 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8443 - loss: 0.8153 - val_accuracy: 0.9333 - val_loss: 0.7687 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8430 - loss: 0.7886 - val_accuracy: 0.9333 - val_loss: 0.7357 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8674 - loss: 0.7565 - val_accuracy: 0.9333 - val_loss: 0.7122 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8857 - loss: 0.7311 - val_accuracy: 0.9333 - val_loss: 0.6826 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9053 - loss: 0.7078 - val_accuracy: 0.9333 - val_loss: 0.6610 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9357 - loss: 0.6821 - val_accuracy: 0.9667 - val_loss: 0.6347 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9373 - loss: 0.6614 - val_accuracy: 0.9667 - val_loss: 0.6121 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9357 - loss: 0.6388 - val_accuracy: 0.9667 - val_loss: 0.5894 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9373 - loss: 0.6200 - val_accuracy: 0.9667 - val_loss: 0.5693 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9357 - loss: 0.5999 - val_accuracy: 0.9667 - val_loss: 0.5479 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9396 - loss: 0.5821 - val_accuracy: 0.9667 - val_loss: 0.5306 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9463 - loss: 0.5633 - val_accuracy: 0.9667 - val_loss: 0.5105 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9510 - loss: 0.5467 - val_accuracy: 0.9667 - val_loss: 0.4948 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9487 - loss: 0.5309 - val_accuracy: 0.9667 - val_loss: 0.4765 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.5151 - val_accuracy: 0.9667 - val_loss: 0.4624 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9510 - loss: 0.5006 - val_accuracy: 0.9667 - val_loss: 0.4453 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4864 - val_accuracy: 0.9667 - val_loss: 0.4326 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4731 - val_accuracy: 0.9667 - val_loss: 0.4195 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4695 - val_accuracy: 0.9667 - val_loss: 0.4043 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4475 - val_accuracy: 0.9667 - val_loss: 0.3940 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4353 - val_accuracy: 0.9667 - val_loss: 0.3795 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4241 - val_accuracy: 0.9667 - val_loss: 0.3700 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4127 - val_accuracy: 0.9667 - val_loss: 0.3578 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9510 - loss: 0.4018 - val_accuracy: 0.9667 - val_loss: 0.3476 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3911 - val_accuracy: 0.9667 - val_loss: 0.3369 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3813 - val_accuracy: 0.9667 - val_loss: 0.3271 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3712 - val_accuracy: 0.9667 - val_loss: 0.3177 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3616 - val_accuracy: 0.9667 - val_loss: 0.3087 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3531 - val_accuracy: 0.9667 - val_loss: 0.3002 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3436 - val_accuracy: 0.9667 - val_loss: 0.2928 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3359 - val_accuracy: 0.9667 - val_loss: 0.2856 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9543 - loss: 0.3272 - val_accuracy: 0.9667 - val_loss: 0.2779 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9543 - loss: 0.3199 - val_accuracy: 0.9667 - val_loss: 0.2721 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.3118 - val_accuracy: 0.9667 - val_loss: 0.2648 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9543 - loss: 0.3047 - val_accuracy: 0.9667 - val_loss: 0.2588 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9543 - loss: 0.2974 - val_accuracy: 0.9667 - val_loss: 0.2528 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2901 - val_accuracy: 0.9667 - val_loss: 0.2465 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2832 - val_accuracy: 0.9667 - val_loss: 0.2415 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2767 - val_accuracy: 0.9667 - val_loss: 0.2356 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9587 - loss: 0.2702 - val_accuracy: 0.9667 - val_loss: 0.2310 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9587 - loss: 0.2639 - val_accuracy: 0.9667 - val_loss: 0.2253 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2576 - val_accuracy: 0.9667 - val_loss: 0.2208 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9587 - loss: 0.2518 - val_accuracy: 1.0000 - val_loss: 0.2160 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9587 - loss: 0.2465 - val_accuracy: 0.9667 - val_loss: 0.2124 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9587 - loss: 0.2409 - val_accuracy: 1.0000 - val_loss: 0.2071 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2359 - val_accuracy: 0.9667 - val_loss: 0.2048 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2306 - val_accuracy: 1.0000 - val_loss: 0.1989 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2255 - val_accuracy: 0.9667 - val_loss: 0.1957 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9587 - loss: 0.2243 - val_accuracy: 1.0000 - val_loss: 0.1865 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2178 - val_accuracy: 1.0000 - val_loss: 0.1889 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9587 - loss: 0.2109 - val_accuracy: 1.0000 - val_loss: 0.1846 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.2067 - val_accuracy: 1.0000 - val_loss: 0.1817 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.2021 - val_accuracy: 1.0000 - val_loss: 0.1777 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.1982 - val_accuracy: 1.0000 - val_loss: 0.1763 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.1943 - val_accuracy: 1.0000 - val_loss: 0.1723 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.1908 - val_accuracy: 1.0000 - val_loss: 0.1713 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.1869 - val_accuracy: 1.0000 - val_loss: 0.1673 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.1836 - val_accuracy: 1.0000 - val_loss: 0.1661 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.1800 - val_accuracy: 1.0000 - val_loss: 0.1630 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9813 - loss: 0.1770 - val_accuracy: 1.0000 - val_loss: 0.1614 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.1737 - val_accuracy: 1.0000 - val_loss: 0.1584 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.1708 - val_accuracy: 1.0000 - val_loss: 0.1571 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9928 - loss: 0.1677 - val_accuracy: 1.0000 - val_loss: 0.1540 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.1651 - val_accuracy: 1.0000 - val_loss: 0.1531 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.1621 - val_accuracy: 1.0000 - val_loss: 0.1498 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.1596 - val_accuracy: 1.0000 - val_loss: 0.1491 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1568 - val_accuracy: 1.0000 - val_loss: 0.1459 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1545 - val_accuracy: 1.0000 - val_loss: 0.1453 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1518 - val_accuracy: 1.0000 - val_loss: 0.1422 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1496 - val_accuracy: 1.0000 - val_loss: 0.1417 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1471 - val_accuracy: 1.0000 - val_loss: 0.1387 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1451 - val_accuracy: 1.0000 - val_loss: 0.1381 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1427 - val_accuracy: 1.0000 - val_loss: 0.1353 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1407 - val_accuracy: 1.0000 - val_loss: 0.1348 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 3: 1.0000\n",
|
||
"\n",
|
||
"Fold 4\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 60ms/step - accuracy: 0.1668 - loss: 1.9004 - val_accuracy: 0.0333 - val_loss: 1.7638 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1911 - loss: 1.5676 - val_accuracy: 0.0667 - val_loss: 1.6981 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.1761 - loss: 1.5153 - val_accuracy: 0.2333 - val_loss: 1.6235 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2603 - loss: 1.4717 - val_accuracy: 0.2333 - val_loss: 1.5810 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2603 - loss: 1.4320 - val_accuracy: 0.2333 - val_loss: 1.5442 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3036 - loss: 1.3959 - val_accuracy: 0.3333 - val_loss: 1.5064 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3932 - loss: 1.3588 - val_accuracy: 0.3333 - val_loss: 1.4683 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4079 - loss: 1.3233 - val_accuracy: 0.3333 - val_loss: 1.4325 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4439 - loss: 1.2907 - val_accuracy: 0.3667 - val_loss: 1.3994 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4401 - loss: 1.2577 - val_accuracy: 0.5000 - val_loss: 1.3667 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4236 - loss: 1.2269 - val_accuracy: 0.5000 - val_loss: 1.3362 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4236 - loss: 1.1932 - val_accuracy: 0.5000 - val_loss: 1.2997 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4996 - loss: 1.1597 - val_accuracy: 0.5667 - val_loss: 1.2675 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4872 - loss: 1.1301 - val_accuracy: 0.5667 - val_loss: 1.2339 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5291 - loss: 1.0971 - val_accuracy: 0.5667 - val_loss: 1.2012 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.5371 - loss: 1.0672 - val_accuracy: 0.5667 - val_loss: 1.1680 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5458 - loss: 1.0357 - val_accuracy: 0.7333 - val_loss: 1.1358 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6021 - loss: 1.0038 - val_accuracy: 0.7333 - val_loss: 1.1010 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6165 - loss: 0.9731 - val_accuracy: 0.7333 - val_loss: 1.0691 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6700 - loss: 0.9406 - val_accuracy: 0.7333 - val_loss: 1.0351 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6718 - loss: 0.9107 - val_accuracy: 0.7333 - val_loss: 1.0008 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7958 - loss: 0.8758 - val_accuracy: 0.7667 - val_loss: 0.9701 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8293 - loss: 0.8439 - val_accuracy: 0.8000 - val_loss: 0.9346 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8361 - loss: 0.8057 - val_accuracy: 0.6667 - val_loss: 0.9051 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8433 - loss: 0.7701 - val_accuracy: 0.6667 - val_loss: 0.8697 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8433 - loss: 0.7339 - val_accuracy: 0.6667 - val_loss: 0.8410 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8662 - loss: 0.7008 - val_accuracy: 0.6667 - val_loss: 0.8087 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8662 - loss: 0.6701 - val_accuracy: 0.6667 - val_loss: 0.7774 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8662 - loss: 0.6401 - val_accuracy: 0.6667 - val_loss: 0.7484 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8662 - loss: 0.6131 - val_accuracy: 0.6667 - val_loss: 0.7192 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9122 - loss: 0.5867 - val_accuracy: 0.6667 - val_loss: 0.6910 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9122 - loss: 0.5630 - val_accuracy: 0.6667 - val_loss: 0.6658 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9332 - loss: 0.5393 - val_accuracy: 0.6667 - val_loss: 0.6420 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9332 - loss: 0.5169 - val_accuracy: 0.6667 - val_loss: 0.6176 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9332 - loss: 0.4955 - val_accuracy: 0.6667 - val_loss: 0.5961 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9332 - loss: 0.4754 - val_accuracy: 0.6667 - val_loss: 0.5744 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9332 - loss: 0.4553 - val_accuracy: 0.6667 - val_loss: 0.5512 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9562 - loss: 0.4360 - val_accuracy: 0.7333 - val_loss: 0.5293 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9750 - loss: 0.4172 - val_accuracy: 0.8000 - val_loss: 0.4976 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9750 - loss: 0.3975 - val_accuracy: 0.8000 - val_loss: 0.4842 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9750 - loss: 0.3783 - val_accuracy: 0.8000 - val_loss: 0.4631 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9750 - loss: 0.3563 - val_accuracy: 0.8000 - val_loss: 0.4455 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9822 - loss: 0.3427 - val_accuracy: 0.9333 - val_loss: 0.4175 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9750 - loss: 0.3335 - val_accuracy: 0.8333 - val_loss: 0.4482 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9822 - loss: 0.3264 - val_accuracy: 0.8000 - val_loss: 0.3932 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9822 - loss: 0.3053 - val_accuracy: 0.8000 - val_loss: 0.3865 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9822 - loss: 0.2817 - val_accuracy: 0.8000 - val_loss: 0.3718 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9822 - loss: 0.2759 - val_accuracy: 0.8000 - val_loss: 0.3655 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9822 - loss: 0.2618 - val_accuracy: 0.8000 - val_loss: 0.3501 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9822 - loss: 0.2525 - val_accuracy: 0.8000 - val_loss: 0.3459 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9866 - loss: 0.2411 - val_accuracy: 0.8000 - val_loss: 0.3351 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9866 - loss: 0.2334 - val_accuracy: 0.8000 - val_loss: 0.3307 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.2238 - val_accuracy: 0.8000 - val_loss: 0.3222 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.2166 - val_accuracy: 0.8000 - val_loss: 0.3140 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9822 - loss: 0.2109 - val_accuracy: 0.8333 - val_loss: 0.3077 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9866 - loss: 0.2043 - val_accuracy: 0.8000 - val_loss: 0.3007 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.1957 - val_accuracy: 0.8333 - val_loss: 0.2936 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.1891 - val_accuracy: 0.8333 - val_loss: 0.2875 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.1831 - val_accuracy: 0.8667 - val_loss: 0.2826 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.1776 - val_accuracy: 0.8667 - val_loss: 0.2776 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.1723 - val_accuracy: 0.8667 - val_loss: 0.2726 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.1677 - val_accuracy: 0.8667 - val_loss: 0.2690 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.1628 - val_accuracy: 0.8667 - val_loss: 0.2641 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.1588 - val_accuracy: 0.8667 - val_loss: 0.2602 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.1550 - val_accuracy: 0.8667 - val_loss: 0.2560 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1510 - val_accuracy: 0.8667 - val_loss: 0.2523 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1474 - val_accuracy: 0.8667 - val_loss: 0.2463 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1438 - val_accuracy: 0.8667 - val_loss: 0.2445 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1404 - val_accuracy: 0.8667 - val_loss: 0.2434 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1375 - val_accuracy: 0.8667 - val_loss: 0.2394 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1344 - val_accuracy: 0.8667 - val_loss: 0.2337 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1317 - val_accuracy: 0.8667 - val_loss: 0.2347 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1289 - val_accuracy: 0.8667 - val_loss: 0.2322 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1264 - val_accuracy: 0.8667 - val_loss: 0.2297 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1243 - val_accuracy: 0.8667 - val_loss: 0.2232 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1216 - val_accuracy: 0.8667 - val_loss: 0.2240 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1197 - val_accuracy: 0.8667 - val_loss: 0.2214 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1172 - val_accuracy: 0.8667 - val_loss: 0.2181 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1153 - val_accuracy: 0.8667 - val_loss: 0.2208 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1132 - val_accuracy: 0.8667 - val_loss: 0.2124 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1114 - val_accuracy: 0.8667 - val_loss: 0.2150 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1096 - val_accuracy: 0.8667 - val_loss: 0.2108 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1079 - val_accuracy: 0.8667 - val_loss: 0.2105 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1062 - val_accuracy: 0.8667 - val_loss: 0.2111 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1047 - val_accuracy: 0.8667 - val_loss: 0.2042 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1032 - val_accuracy: 0.8667 - val_loss: 0.2056 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1019 - val_accuracy: 0.8667 - val_loss: 0.2015 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1002 - val_accuracy: 0.8667 - val_loss: 0.2019 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0992 - val_accuracy: 0.8667 - val_loss: 0.1999 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0976 - val_accuracy: 0.8667 - val_loss: 0.1971 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0965 - val_accuracy: 0.8667 - val_loss: 0.2009 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0952 - val_accuracy: 0.8667 - val_loss: 0.1926 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0940 - val_accuracy: 0.8667 - val_loss: 0.1952 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0928 - val_accuracy: 0.8667 - val_loss: 0.1928 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0917 - val_accuracy: 0.8667 - val_loss: 0.1922 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0907 - val_accuracy: 0.8667 - val_loss: 0.1932 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0896 - val_accuracy: 0.8667 - val_loss: 0.1874 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0886 - val_accuracy: 0.8667 - val_loss: 0.1893 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.0877 - val_accuracy: 0.8667 - val_loss: 0.1902 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.0867 - val_accuracy: 0.8667 - val_loss: 0.1847 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 4: 0.8667\n",
|
||
"\n",
|
||
"Fold 5\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 60ms/step - accuracy: 0.2546 - loss: 2.0092 - val_accuracy: 0.3000 - val_loss: 1.6294 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3222 - loss: 1.5677 - val_accuracy: 0.2667 - val_loss: 1.6030 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.2548 - loss: 1.5664 - val_accuracy: 0.2333 - val_loss: 1.5563 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3090 - loss: 1.5282 - val_accuracy: 0.3333 - val_loss: 1.5188 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4004 - loss: 1.4907 - val_accuracy: 0.4000 - val_loss: 1.4767 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4216 - loss: 1.4529 - val_accuracy: 0.3000 - val_loss: 1.4419 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3471 - loss: 1.4245 - val_accuracy: 0.3000 - val_loss: 1.4075 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.3528 - loss: 1.3962 - val_accuracy: 0.4000 - val_loss: 1.3711 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4470 - loss: 1.3609 - val_accuracy: 0.3333 - val_loss: 1.3360 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3947 - loss: 1.3295 - val_accuracy: 0.3333 - val_loss: 1.3001 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4084 - loss: 1.2979 - val_accuracy: 0.4667 - val_loss: 1.2631 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4853 - loss: 1.2652 - val_accuracy: 0.5333 - val_loss: 1.2272 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.4958 - loss: 1.2321 - val_accuracy: 0.5333 - val_loss: 1.1915 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5140 - loss: 1.1987 - val_accuracy: 0.5667 - val_loss: 1.1531 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5328 - loss: 1.1649 - val_accuracy: 0.5333 - val_loss: 1.1167 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5682 - loss: 1.1316 - val_accuracy: 0.6667 - val_loss: 1.0761 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6255 - loss: 1.0984 - val_accuracy: 0.7000 - val_loss: 1.0368 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6969 - loss: 1.0629 - val_accuracy: 0.7667 - val_loss: 0.9960 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7333 - loss: 1.0257 - val_accuracy: 0.8667 - val_loss: 0.9599 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7586 - loss: 0.9926 - val_accuracy: 0.8667 - val_loss: 0.9253 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7883 - loss: 0.9591 - val_accuracy: 0.8667 - val_loss: 0.8920 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7954 - loss: 0.9273 - val_accuracy: 0.8667 - val_loss: 0.8603 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8381 - loss: 0.8971 - val_accuracy: 0.8667 - val_loss: 0.8298 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8425 - loss: 0.8670 - val_accuracy: 0.8667 - val_loss: 0.8008 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8613 - loss: 0.8389 - val_accuracy: 0.8667 - val_loss: 0.7732 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8728 - loss: 0.8126 - val_accuracy: 0.9000 - val_loss: 0.7479 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8819 - loss: 0.7868 - val_accuracy: 0.9000 - val_loss: 0.7235 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8819 - loss: 0.7621 - val_accuracy: 0.9000 - val_loss: 0.7000 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8819 - loss: 0.7384 - val_accuracy: 0.9000 - val_loss: 0.6781 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8843 - loss: 0.7147 - val_accuracy: 0.9000 - val_loss: 0.6569 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8843 - loss: 0.6937 - val_accuracy: 0.9000 - val_loss: 0.6366 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8843 - loss: 0.6718 - val_accuracy: 0.9000 - val_loss: 0.6174 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.6519 - val_accuracy: 0.9000 - val_loss: 0.5985 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.6319 - val_accuracy: 0.9000 - val_loss: 0.5811 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.6126 - val_accuracy: 0.9000 - val_loss: 0.5633 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9072 - loss: 0.5941 - val_accuracy: 0.9000 - val_loss: 0.5473 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.5755 - val_accuracy: 0.9000 - val_loss: 0.5308 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9072 - loss: 0.5585 - val_accuracy: 0.9000 - val_loss: 0.5157 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9087 - loss: 0.5411 - val_accuracy: 0.9000 - val_loss: 0.5008 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9087 - loss: 0.5244 - val_accuracy: 0.9000 - val_loss: 0.4868 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9087 - loss: 0.5078 - val_accuracy: 0.9000 - val_loss: 0.4729 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9087 - loss: 0.4918 - val_accuracy: 0.9000 - val_loss: 0.4596 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9317 - loss: 0.4761 - val_accuracy: 0.9000 - val_loss: 0.4467 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9317 - loss: 0.4623 - val_accuracy: 0.9000 - val_loss: 0.4351 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9317 - loss: 0.4483 - val_accuracy: 0.9000 - val_loss: 0.4243 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9317 - loss: 0.4364 - val_accuracy: 0.9000 - val_loss: 0.4123 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9570 - loss: 0.4235 - val_accuracy: 0.9000 - val_loss: 0.4029 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9570 - loss: 0.4119 - val_accuracy: 0.9000 - val_loss: 0.3930 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9570 - loss: 0.4015 - val_accuracy: 0.9000 - val_loss: 0.3831 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9570 - loss: 0.3905 - val_accuracy: 0.9333 - val_loss: 0.3748 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9570 - loss: 0.3806 - val_accuracy: 0.9333 - val_loss: 0.3657 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9570 - loss: 0.3713 - val_accuracy: 0.9333 - val_loss: 0.3576 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9613 - loss: 0.3612 - val_accuracy: 0.9333 - val_loss: 0.3498 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9613 - loss: 0.3533 - val_accuracy: 0.9333 - val_loss: 0.3423 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9613 - loss: 0.3445 - val_accuracy: 0.9333 - val_loss: 0.3346 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9613 - loss: 0.3366 - val_accuracy: 0.9333 - val_loss: 0.3283 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.3289 - val_accuracy: 0.9333 - val_loss: 0.3214 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9613 - loss: 0.3217 - val_accuracy: 0.9333 - val_loss: 0.3151 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.3145 - val_accuracy: 0.9333 - val_loss: 0.3089 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.3075 - val_accuracy: 0.9333 - val_loss: 0.3036 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.3006 - val_accuracy: 0.9667 - val_loss: 0.2967 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9647 - loss: 0.2957 - val_accuracy: 0.9333 - val_loss: 0.2919 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.2883 - val_accuracy: 0.9667 - val_loss: 0.2855 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.2832 - val_accuracy: 0.9333 - val_loss: 0.2812 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2772 - val_accuracy: 0.9667 - val_loss: 0.2758 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2718 - val_accuracy: 0.9667 - val_loss: 0.2708 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2665 - val_accuracy: 0.9667 - val_loss: 0.2662 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2611 - val_accuracy: 0.9667 - val_loss: 0.2618 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2562 - val_accuracy: 0.9667 - val_loss: 0.2568 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2514 - val_accuracy: 0.9667 - val_loss: 0.2525 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2476 - val_accuracy: 0.9667 - val_loss: 0.2472 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2426 - val_accuracy: 0.9667 - val_loss: 0.2443 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2375 - val_accuracy: 0.9667 - val_loss: 0.2398 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2339 - val_accuracy: 0.9667 - val_loss: 0.2361 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2303 - val_accuracy: 0.9667 - val_loss: 0.2314 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2256 - val_accuracy: 0.9667 - val_loss: 0.2281 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9718 - loss: 0.2224 - val_accuracy: 0.9667 - val_loss: 0.2241 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2184 - val_accuracy: 0.9667 - val_loss: 0.2209 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2147 - val_accuracy: 0.9667 - val_loss: 0.2171 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.2112 - val_accuracy: 0.9667 - val_loss: 0.2144 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2079 - val_accuracy: 0.9667 - val_loss: 0.2106 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2043 - val_accuracy: 0.9667 - val_loss: 0.2079 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.2009 - val_accuracy: 0.9667 - val_loss: 0.2043 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1979 - val_accuracy: 0.9667 - val_loss: 0.2015 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9718 - loss: 0.1946 - val_accuracy: 0.9667 - val_loss: 0.1988 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9718 - loss: 0.1922 - val_accuracy: 1.0000 - val_loss: 0.1945 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1897 - val_accuracy: 0.9667 - val_loss: 0.1926 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1855 - val_accuracy: 1.0000 - val_loss: 0.1900 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9718 - loss: 0.1840 - val_accuracy: 1.0000 - val_loss: 0.1863 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1815 - val_accuracy: 1.0000 - val_loss: 0.1846 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1772 - val_accuracy: 1.0000 - val_loss: 0.1819 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9718 - loss: 0.1761 - val_accuracy: 1.0000 - val_loss: 0.1786 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1737 - val_accuracy: 1.0000 - val_loss: 0.1769 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9718 - loss: 0.1699 - val_accuracy: 1.0000 - val_loss: 0.1744 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9718 - loss: 0.1687 - val_accuracy: 1.0000 - val_loss: 0.1715 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9790 - loss: 0.1664 - val_accuracy: 1.0000 - val_loss: 0.1697 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9718 - loss: 0.1630 - val_accuracy: 1.0000 - val_loss: 0.1673 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9790 - loss: 0.1618 - val_accuracy: 1.0000 - val_loss: 0.1647 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9790 - loss: 0.1597 - val_accuracy: 1.0000 - val_loss: 0.1631 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9790 - loss: 0.1566 - val_accuracy: 1.0000 - val_loss: 0.1606 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 5: 1.0000\n",
|
||
"\n",
|
||
"Average Accuracy: 0.9733\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Visualisasi akurasi tiap fold\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.plot(range(1, len(accuracies) + 1), accuracies, marker='o', linestyle='-', color='blue', label='Fold Accuracy')\n",
|
||
"plt.axhline(np.mean(accuracies), color='red', linestyle='--', label=f'Average Accuracy: {np.mean(accuracies):.4f}')\n",
|
||
"plt.title(\"Validation Accuracy per Fold\")\n",
|
||
"plt.xlabel(\"Fold\")\n",
|
||
"plt.ylabel(\"Accuracy\")\n",
|
||
"plt.xticks(range(1, len(accuracies) + 1))\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 485
|
||
},
|
||
"id": "nJ9fx6EuOq7B",
|
||
"outputId": "cd049fc0-2b61-414a-dbbd-b3dea3333872"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Finding Best Hyperparameter"
|
||
],
|
||
"metadata": {
|
||
"id": "bEuHgzpXtMvp"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def build_model(hp):\n",
|
||
" model = Sequential()\n",
|
||
" # Input layer\n",
|
||
" model.add(Input(shape=(18,1)))\n",
|
||
" model.add(Conv1D(\n",
|
||
" filters=hp.Int('filters', min_value=16, max_value=64, step=16),\n",
|
||
" kernel_size=hp.Choice('kernel_size', values=[3, 5]),\n",
|
||
" activation='relu',\n",
|
||
" kernel_regularizer=l2(hp.Float('l2_reg_conv', min_value=0.0001, max_value=0.01, sampling='LOG'))\n",
|
||
" ))\n",
|
||
" model.add(Flatten())\n",
|
||
" model.add(Dense(\n",
|
||
" units=hp.Int('dense_units', min_value=16, max_value=128, step=16),\n",
|
||
" activation='relu',\n",
|
||
" kernel_regularizer=l2(hp.Float('l2_reg_dense', min_value=0.0001, max_value=0.01, sampling='LOG'))\n",
|
||
" ))\n",
|
||
" # Output layer\n",
|
||
" model.add(Dense(len(classes), activation='softmax'))\n",
|
||
" # Hyperparameter untuk optimizer\n",
|
||
" optimizer_choice = hp.Choice('optimizer', values=['adam', 'rmsprop'])\n",
|
||
" learning_rate = hp.Choice('learning_rate', values=[0.01, 0.001, 0.0001])\n",
|
||
" if optimizer_choice == 'adam':\n",
|
||
" optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
|
||
" else:\n",
|
||
" optimizer = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)\n",
|
||
"\n",
|
||
" # Compile model\n",
|
||
" model.compile(\n",
|
||
" optimizer=optimizer,\n",
|
||
" loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
|
||
" metrics=['accuracy']\n",
|
||
" )\n",
|
||
"\n",
|
||
" return model\n",
|
||
"\n",
|
||
"callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),\n",
|
||
" tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",\n",
|
||
" patience=5,\n",
|
||
" verbose=1),\n",
|
||
" CSVLogger('hp_log.csv', append=True)]"
|
||
],
|
||
"metadata": {
|
||
"id": "BQCS3xboq3Kr"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"tuner = RandomSearch(\n",
|
||
" build_model,\n",
|
||
" objective='val_accuracy',\n",
|
||
" directory='best_params_grid_layer',\n",
|
||
" project_name='hptuning',\n",
|
||
" max_trials=5,\n",
|
||
" executions_per_trial=3,\n",
|
||
" seed=42\n",
|
||
")\n",
|
||
"tuner.search(train_ds, validation_data=val_ds, epochs=100, callbacks=callbacks)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "lF2Zel_puFc2",
|
||
"outputId": "db6dce57-980e-4b38-b038-fb206c6ad2da"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Trial 5 Complete [00h 00m 30s]\n",
|
||
"val_accuracy: 1.0\n",
|
||
"\n",
|
||
"Best val_accuracy So Far: 1.0\n",
|
||
"Total elapsed time: 00h 02m 10s\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!rm -rf best_params_grid_layer"
|
||
],
|
||
"metadata": {
|
||
"id": "5L25hBMiG_93"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## Build Hyperparameter Dataframe\n"
|
||
],
|
||
"metadata": {
|
||
"id": "ndTXAhwGJtQa"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"data = []\n",
|
||
"# Melihat semua percobaan yang telah dilakukan\n",
|
||
"trials = tuner.oracle.trials\n",
|
||
"\n",
|
||
"# Iterasi setiap trial\n",
|
||
"for trial_id, trial in trials.items():\n",
|
||
" row = {\n",
|
||
" \"Trial ID\": trial_id,\n",
|
||
" \"Score\": trial.score\n",
|
||
" }\n",
|
||
" # Tambahkan semua hyperparameter ke dalam dictionary\n",
|
||
" row.update(trial.hyperparameters.values)\n",
|
||
"\n",
|
||
" # Tambahkan ke list\n",
|
||
" data.append(row)\n",
|
||
"\n",
|
||
"# Buat DataFrame\n",
|
||
"df_trials = pd.DataFrame(data)\n",
|
||
"df_trials = df_trials.sort_values(by=\"Score\", ascending=False)\n",
|
||
"# Tampilkan DataFrame\n",
|
||
"display(df_trials)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 206
|
||
},
|
||
"id": "VvFWyM8WJwZf",
|
||
"outputId": "5503a144-958b-47a1-841b-9f38756ec44c"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
" Trial ID Score filters kernel_size l2_reg_conv dense_units \\\n",
|
||
"0 0 1.000000 48 3 0.000656 48 \n",
|
||
"4 4 1.000000 64 3 0.000140 48 \n",
|
||
"2 2 0.933333 64 3 0.001450 32 \n",
|
||
"1 1 0.922222 16 3 0.000307 128 \n",
|
||
"3 3 0.711111 32 3 0.000675 16 \n",
|
||
"\n",
|
||
" l2_reg_dense optimizer learning_rate \n",
|
||
"0 0.005978 adam 0.0010 \n",
|
||
"4 0.005242 adam 0.0010 \n",
|
||
"2 0.000413 adam 0.0001 \n",
|
||
"1 0.001715 rmsprop 0.0100 \n",
|
||
"3 0.000141 rmsprop 0.0001 "
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-a1aba6c0-338c-4d1f-93b5-f0ac9aff44c7\" class=\"colab-df-container\">\n",
|
||
" <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>Trial ID</th>\n",
|
||
" <th>Score</th>\n",
|
||
" <th>filters</th>\n",
|
||
" <th>kernel_size</th>\n",
|
||
" <th>l2_reg_conv</th>\n",
|
||
" <th>dense_units</th>\n",
|
||
" <th>l2_reg_dense</th>\n",
|
||
" <th>optimizer</th>\n",
|
||
" <th>learning_rate</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.000656</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>0.005978</td>\n",
|
||
" <td>adam</td>\n",
|
||
" <td>0.0010</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.000140</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>0.005242</td>\n",
|
||
" <td>adam</td>\n",
|
||
" <td>0.0010</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.933333</td>\n",
|
||
" <td>64</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.001450</td>\n",
|
||
" <td>32</td>\n",
|
||
" <td>0.000413</td>\n",
|
||
" <td>adam</td>\n",
|
||
" <td>0.0001</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.922222</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.000307</td>\n",
|
||
" <td>128</td>\n",
|
||
" <td>0.001715</td>\n",
|
||
" <td>rmsprop</td>\n",
|
||
" <td>0.0100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.711111</td>\n",
|
||
" <td>32</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.000675</td>\n",
|
||
" <td>16</td>\n",
|
||
" <td>0.000141</td>\n",
|
||
" <td>rmsprop</td>\n",
|
||
" <td>0.0001</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" <div class=\"colab-df-buttons\">\n",
|
||
"\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1aba6c0-338c-4d1f-93b5-f0ac9aff44c7')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
||
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-buttons div {\n",
|
||
" margin-bottom: 4px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-a1aba6c0-338c-4d1f-93b5-f0ac9aff44c7 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-a1aba6c0-338c-4d1f-93b5-f0ac9aff44c7');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
"\n",
|
||
"<div id=\"df-fa720fc5-0733-4002-b2f0-e79b132722ab\">\n",
|
||
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fa720fc5-0733-4002-b2f0-e79b132722ab')\"\n",
|
||
" title=\"Suggest charts\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <g>\n",
|
||
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
||
" </g>\n",
|
||
"</svg>\n",
|
||
" </button>\n",
|
||
"\n",
|
||
"<style>\n",
|
||
" .colab-df-quickchart {\n",
|
||
" --bg-color: #E8F0FE;\n",
|
||
" --fill-color: #1967D2;\n",
|
||
" --hover-bg-color: #E2EBFA;\n",
|
||
" --hover-fill-color: #174EA6;\n",
|
||
" --disabled-fill-color: #AAA;\n",
|
||
" --disabled-bg-color: #DDD;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-quickchart {\n",
|
||
" --bg-color: #3B4455;\n",
|
||
" --fill-color: #D2E3FC;\n",
|
||
" --hover-bg-color: #434B5C;\n",
|
||
" --hover-fill-color: #FFFFFF;\n",
|
||
" --disabled-bg-color: #3B4455;\n",
|
||
" --disabled-fill-color: #666;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart {\n",
|
||
" background-color: var(--bg-color);\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: var(--fill-color);\n",
|
||
" height: 32px;\n",
|
||
" padding: 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart:hover {\n",
|
||
" background-color: var(--hover-bg-color);\n",
|
||
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: var(--button-hover-fill-color);\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-quickchart-complete:disabled,\n",
|
||
" .colab-df-quickchart-complete:disabled:hover {\n",
|
||
" background-color: var(--disabled-bg-color);\n",
|
||
" fill: var(--disabled-fill-color);\n",
|
||
" box-shadow: none;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-spinner {\n",
|
||
" border: 2px solid var(--fill-color);\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" animation:\n",
|
||
" spin 1s steps(1) infinite;\n",
|
||
" }\n",
|
||
"\n",
|
||
" @keyframes spin {\n",
|
||
" 0% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 20% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 30% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-left-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 40% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-top-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 60% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 80% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-right-color: var(--fill-color);\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" 90% {\n",
|
||
" border-color: transparent;\n",
|
||
" border-bottom-color: var(--fill-color);\n",
|
||
" }\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" async function quickchart(key) {\n",
|
||
" const quickchartButtonEl =\n",
|
||
" document.querySelector('#' + key + ' button');\n",
|
||
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
||
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
||
" try {\n",
|
||
" const charts = await google.colab.kernel.invokeFunction(\n",
|
||
" 'suggestCharts', [key], {});\n",
|
||
" } catch (error) {\n",
|
||
" console.error('Error during call to suggestCharts:', error);\n",
|
||
" }\n",
|
||
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
||
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
||
" }\n",
|
||
" (() => {\n",
|
||
" let quickchartButtonEl =\n",
|
||
" document.querySelector('#df-fa720fc5-0733-4002-b2f0-e79b132722ab button');\n",
|
||
" quickchartButtonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
"</div>\n",
|
||
"\n",
|
||
" <div id=\"id_e50cf125-df2a-4f2a-9192-ddd075f60f44\">\n",
|
||
" <style>\n",
|
||
" .colab-df-generate {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-generate:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-generate {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-generate:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_trials')\"\n",
|
||
" title=\"Generate code using this dataframe.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
"\n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
" <script>\n",
|
||
" (() => {\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#id_e50cf125-df2a-4f2a-9192-ddd075f60f44 button.colab-df-generate');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" buttonEl.onclick = () => {\n",
|
||
" google.colab.notebook.generateWithVariable('df_trials');\n",
|
||
" }\n",
|
||
" })();\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
"\n",
|
||
" </div>\n",
|
||
" </div>\n"
|
||
],
|
||
"application/vnd.google.colaboratory.intrinsic+json": {
|
||
"type": "dataframe",
|
||
"variable_name": "df_trials",
|
||
"summary": "{\n \"name\": \"df_trials\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Trial ID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"4\",\n \"3\",\n \"2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.118738213816186,\n \"min\": 0.7111111283302307,\n \"max\": 1.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.9333333174387614,\n 0.7111111283302307,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"filters\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 20,\n \"min\": 16,\n \"max\": 64,\n \"num_unique_values\": 4,\n \"samples\": [\n 64,\n 32,\n 48\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"kernel_size\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 3,\n \"max\": 3,\n \"num_unique_values\": 1,\n \"samples\": [\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"l2_reg_conv\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0005047113543128985,\n \"min\": 0.00014023399602325874,\n \"max\": 0.001450488633461591,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.00014023399602325874\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dense_units\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 43,\n \"min\": 16,\n \"max\": 128,\n \"num_unique_values\": 4,\n \"samples\": [\n 32\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"l2_reg_dense\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0027364463468293877,\n \"min\": 0.00014111965749931114,\n \"max\": 0.005977728042983696,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.005241515692754444\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"optimizer\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"rmsprop\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"learning_rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.004250058823122334,\n \"min\": 0.0001,\n \"max\": 0.01,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.001\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"print(tuner.get_best_hyperparameters()[0].values)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "dyxga3JvVcNJ",
|
||
"outputId": "192f0f4f-ed50-4f7a-e7e2-c6fe16204da8"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"{'filters': 48, 'kernel_size': 3, 'l2_reg_conv': 0.0006562536901904111, 'dense_units': 48, 'l2_reg_dense': 0.005977728042983696, 'optimizer': 'adam', 'learning_rate': 0.001}\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Save Hyperparameter df"
|
||
],
|
||
"metadata": {
|
||
"id": "3Z6fMRg0U-gC"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df_trials.to_excel(\"Hyperparameters.xlsx\", index=False)"
|
||
],
|
||
"metadata": {
|
||
"id": "GiXlufiXU9vF"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Modelling Using Best Parameter"
|
||
],
|
||
"metadata": {
|
||
"id": "N3TfM2fbvj0R"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"X = df.iloc[:, :-2].values\n",
|
||
"y = df['Label'].values\n",
|
||
"accuracies = []\n",
|
||
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
|
||
"for fold, (train_idx, val_idx) in enumerate(skf.split(X, y), 1):\n",
|
||
" print(f\"\\nFold {fold}\")\n",
|
||
"\n",
|
||
" # Split data\n",
|
||
" X_train, X_val = X[train_idx], X[val_idx]\n",
|
||
" y_train, y_val = y[train_idx], y[val_idx]\n",
|
||
"\n",
|
||
" # Convert to tf.data.Dataset\n",
|
||
" train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
|
||
" val_ds = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
|
||
"\n",
|
||
" # Shuffle, batch, cache, prefetch\n",
|
||
" train_ds = train_ds.shuffle(buffer_size=len(X_train), seed=42) \\\n",
|
||
" .batch(12) \\\n",
|
||
" .cache() \\\n",
|
||
" .prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" val_ds = val_ds.batch(12).cache().prefetch(tf.data.AUTOTUNE)\n",
|
||
"\n",
|
||
" best_hp = tuner.get_best_hyperparameters()[0]\n",
|
||
" model = tuner.hypermodel.build(best_hp)\n",
|
||
" hist = model.fit(train_ds, epochs=100, validation_data=val_ds, callbacks=callbacks)\n",
|
||
"\n",
|
||
" plt.figure(figsize=(12, 5))\n",
|
||
" # Plot untuk accuracy dan val_accuracy\n",
|
||
" plt.title(\"Accuracy and Val Accuracy\")\n",
|
||
" plt.plot(hist.history['accuracy'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
" plt.plot(hist.history['val_accuracy'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
" plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
" plt.xlabel('Epoch')\n",
|
||
" plt.ylabel('Accuracy')\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" # Evaluate\n",
|
||
" loss, acc = model.evaluate(val_ds, verbose=0)\n",
|
||
" print(f\"Accuracy Fold {fold}: {acc:.4f}\")\n",
|
||
"\n",
|
||
" accuracies.append(acc)\n",
|
||
"\n",
|
||
"print(f\"\\nAverage Accuracy: {np.mean(accuracies):.4f}\")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "K7no1gMCRncs",
|
||
"outputId": "17413b26-a289-4abe-b986-1289ec833693"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\n",
|
||
"Fold 1\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 71ms/step - accuracy: 0.2651 - loss: 2.1802 - val_accuracy: 0.2000 - val_loss: 1.9714 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.2595 - loss: 1.9394 - val_accuracy: 0.4333 - val_loss: 1.7638 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4485 - loss: 1.7237 - val_accuracy: 0.4333 - val_loss: 1.6394 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5834 - loss: 1.5730 - val_accuracy: 0.4667 - val_loss: 1.5271 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5566 - loss: 1.4590 - val_accuracy: 0.5000 - val_loss: 1.4293 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5788 - loss: 1.3487 - val_accuracy: 0.5667 - val_loss: 1.3228 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6688 - loss: 1.2636 - val_accuracy: 0.6333 - val_loss: 1.2376 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7173 - loss: 1.1508 - val_accuracy: 0.6000 - val_loss: 1.1544 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7531 - loss: 1.0958 - val_accuracy: 0.6333 - val_loss: 1.0820 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8252 - loss: 0.9918 - val_accuracy: 0.6333 - val_loss: 1.0096 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8264 - loss: 0.9463 - val_accuracy: 0.6667 - val_loss: 0.9518 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8877 - loss: 0.8476 - val_accuracy: 0.7000 - val_loss: 0.8875 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9106 - loss: 0.8069 - val_accuracy: 0.7000 - val_loss: 0.8355 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9403 - loss: 0.7382 - val_accuracy: 0.7667 - val_loss: 0.7811 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9433 - loss: 0.6941 - val_accuracy: 0.7667 - val_loss: 0.7383 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9481 - loss: 0.6446 - val_accuracy: 0.8333 - val_loss: 0.6913 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9481 - loss: 0.6098 - val_accuracy: 0.8333 - val_loss: 0.6569 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9481 - loss: 0.5745 - val_accuracy: 0.9000 - val_loss: 0.6191 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.5449 - val_accuracy: 0.8333 - val_loss: 0.5946 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.5176 - val_accuracy: 0.9000 - val_loss: 0.5627 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.4952 - val_accuracy: 0.9000 - val_loss: 0.5395 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.4737 - val_accuracy: 0.9000 - val_loss: 0.5170 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.4607 - val_accuracy: 0.9667 - val_loss: 0.4913 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9712 - loss: 0.4405 - val_accuracy: 0.9667 - val_loss: 0.4727 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.4262 - val_accuracy: 0.9000 - val_loss: 0.4677 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9712 - loss: 0.4096 - val_accuracy: 0.9667 - val_loss: 0.4393 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9712 - loss: 0.3900 - val_accuracy: 0.9667 - val_loss: 0.4181 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3794 - val_accuracy: 0.9667 - val_loss: 0.4064 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3654 - val_accuracy: 0.9667 - val_loss: 0.3930 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3551 - val_accuracy: 0.9667 - val_loss: 0.3802 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3446 - val_accuracy: 0.9667 - val_loss: 0.3689 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3351 - val_accuracy: 0.9667 - val_loss: 0.3586 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.3266 - val_accuracy: 0.9667 - val_loss: 0.3483 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3177 - val_accuracy: 0.9667 - val_loss: 0.3396 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.3109 - val_accuracy: 0.9667 - val_loss: 0.3314 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9828 - loss: 0.3032 - val_accuracy: 1.0000 - val_loss: 0.3235 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9828 - loss: 0.2969 - val_accuracy: 1.0000 - val_loss: 0.3162 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2898 - val_accuracy: 1.0000 - val_loss: 0.3097 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2837 - val_accuracy: 1.0000 - val_loss: 0.3014 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2783 - val_accuracy: 1.0000 - val_loss: 0.2961 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2725 - val_accuracy: 1.0000 - val_loss: 0.2897 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2672 - val_accuracy: 1.0000 - val_loss: 0.2840 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2617 - val_accuracy: 1.0000 - val_loss: 0.2775 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2570 - val_accuracy: 1.0000 - val_loss: 0.2733 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2518 - val_accuracy: 1.0000 - val_loss: 0.2674 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2475 - val_accuracy: 1.0000 - val_loss: 0.2622 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.2434 - val_accuracy: 1.0000 - val_loss: 0.2584 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9944 - loss: 0.2391 - val_accuracy: 1.0000 - val_loss: 0.2528 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9944 - loss: 0.2348 - val_accuracy: 1.0000 - val_loss: 0.2494 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2308 - val_accuracy: 1.0000 - val_loss: 0.2449 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2274 - val_accuracy: 1.0000 - val_loss: 0.2405 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2233 - val_accuracy: 1.0000 - val_loss: 0.2370 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2197 - val_accuracy: 1.0000 - val_loss: 0.2328 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2169 - val_accuracy: 1.0000 - val_loss: 0.2293 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2130 - val_accuracy: 1.0000 - val_loss: 0.2256 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.2098 - val_accuracy: 1.0000 - val_loss: 0.2222 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2073 - val_accuracy: 1.0000 - val_loss: 0.2188 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2034 - val_accuracy: 1.0000 - val_loss: 0.2160 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2010 - val_accuracy: 1.0000 - val_loss: 0.2121 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1982 - val_accuracy: 1.0000 - val_loss: 0.2096 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1952 - val_accuracy: 1.0000 - val_loss: 0.2067 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1924 - val_accuracy: 1.0000 - val_loss: 0.2036 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1902 - val_accuracy: 1.0000 - val_loss: 0.2015 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1874 - val_accuracy: 1.0000 - val_loss: 0.1978 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1851 - val_accuracy: 1.0000 - val_loss: 0.1955 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1825 - val_accuracy: 1.0000 - val_loss: 0.1930 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1801 - val_accuracy: 1.0000 - val_loss: 0.1888 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1807 - val_accuracy: 1.0000 - val_loss: 0.1855 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1770 - val_accuracy: 1.0000 - val_loss: 0.1838 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1739 - val_accuracy: 1.0000 - val_loss: 0.1831 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1713 - val_accuracy: 1.0000 - val_loss: 0.1821 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1691 - val_accuracy: 1.0000 - val_loss: 0.1789 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1671 - val_accuracy: 1.0000 - val_loss: 0.1757 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1648 - val_accuracy: 1.0000 - val_loss: 0.1737 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1630 - val_accuracy: 1.0000 - val_loss: 0.1712 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1614 - val_accuracy: 1.0000 - val_loss: 0.1695 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1594 - val_accuracy: 1.0000 - val_loss: 0.1682 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1575 - val_accuracy: 1.0000 - val_loss: 0.1663 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1559 - val_accuracy: 1.0000 - val_loss: 0.1635 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1542 - val_accuracy: 1.0000 - val_loss: 0.1622 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1523 - val_accuracy: 1.0000 - val_loss: 0.1602 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1508 - val_accuracy: 1.0000 - val_loss: 0.1583 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1492 - val_accuracy: 1.0000 - val_loss: 0.1568 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1475 - val_accuracy: 1.0000 - val_loss: 0.1551 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1460 - val_accuracy: 1.0000 - val_loss: 0.1535 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1444 - val_accuracy: 1.0000 - val_loss: 0.1519 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1432 - val_accuracy: 1.0000 - val_loss: 0.1501 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1419 - val_accuracy: 1.0000 - val_loss: 0.1487 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1401 - val_accuracy: 1.0000 - val_loss: 0.1473 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1388 - val_accuracy: 1.0000 - val_loss: 0.1457 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1377 - val_accuracy: 1.0000 - val_loss: 0.1447 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1361 - val_accuracy: 1.0000 - val_loss: 0.1430 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1350 - val_accuracy: 1.0000 - val_loss: 0.1415 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1338 - val_accuracy: 1.0000 - val_loss: 0.1399 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1323 - val_accuracy: 1.0000 - val_loss: 0.1387 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1313 - val_accuracy: 1.0000 - val_loss: 0.1376 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1300 - val_accuracy: 1.0000 - val_loss: 0.1364 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1286 - val_accuracy: 1.0000 - val_loss: 0.1351 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1276 - val_accuracy: 1.0000 - val_loss: 0.1337 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1265 - val_accuracy: 1.0000 - val_loss: 0.1326 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 1: 1.0000\n",
|
||
"\n",
|
||
"Fold 2\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 64ms/step - accuracy: 0.3198 - loss: 2.2166 - val_accuracy: 0.3000 - val_loss: 1.9603 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2780 - loss: 1.9526 - val_accuracy: 0.3667 - val_loss: 1.7237 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4023 - loss: 1.6635 - val_accuracy: 0.5000 - val_loss: 1.5807 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6365 - loss: 1.4799 - val_accuracy: 0.3667 - val_loss: 1.4486 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5131 - loss: 1.3786 - val_accuracy: 0.5000 - val_loss: 1.3188 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6538 - loss: 1.2584 - val_accuracy: 0.5333 - val_loss: 1.2044 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6755 - loss: 1.1723 - val_accuracy: 0.7667 - val_loss: 1.1102 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7350 - loss: 1.0988 - val_accuracy: 0.8333 - val_loss: 1.0188 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7982 - loss: 1.0167 - val_accuracy: 0.9333 - val_loss: 0.9464 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8582 - loss: 0.9600 - val_accuracy: 0.9667 - val_loss: 0.8731 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9180 - loss: 0.8843 - val_accuracy: 0.9333 - val_loss: 0.8141 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9180 - loss: 0.8255 - val_accuracy: 0.9333 - val_loss: 0.7657 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9156 - loss: 0.7776 - val_accuracy: 0.9333 - val_loss: 0.7233 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9224 - loss: 0.7352 - val_accuracy: 0.9333 - val_loss: 0.6818 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9445 - loss: 0.6876 - val_accuracy: 0.9333 - val_loss: 0.6491 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9592 - loss: 0.6462 - val_accuracy: 0.9333 - val_loss: 0.6141 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9592 - loss: 0.6056 - val_accuracy: 0.9333 - val_loss: 0.5911 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9592 - loss: 0.5770 - val_accuracy: 0.9333 - val_loss: 0.5641 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.5454 - val_accuracy: 0.9333 - val_loss: 0.5465 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.5248 - val_accuracy: 0.9333 - val_loss: 0.5251 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.5001 - val_accuracy: 0.9333 - val_loss: 0.5076 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9746 - loss: 0.4803 - val_accuracy: 0.9333 - val_loss: 0.4909 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.4665 - val_accuracy: 0.9333 - val_loss: 0.4852 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9900 - loss: 0.4533 - val_accuracy: 0.9333 - val_loss: 0.4587 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9746 - loss: 0.4270 - val_accuracy: 0.9333 - val_loss: 0.4525 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.4193 - val_accuracy: 0.9333 - val_loss: 0.4368 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.4035 - val_accuracy: 0.9333 - val_loss: 0.4272 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3921 - val_accuracy: 0.9667 - val_loss: 0.4157 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3803 - val_accuracy: 0.9667 - val_loss: 0.4065 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3707 - val_accuracy: 0.9667 - val_loss: 0.3964 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3597 - val_accuracy: 0.9667 - val_loss: 0.3880 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3512 - val_accuracy: 0.9667 - val_loss: 0.3802 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9900 - loss: 0.3418 - val_accuracy: 0.9667 - val_loss: 0.3712 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3335 - val_accuracy: 0.9667 - val_loss: 0.3627 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3259 - val_accuracy: 0.9667 - val_loss: 0.3576 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9900 - loss: 0.3178 - val_accuracy: 0.9667 - val_loss: 0.3486 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3109 - val_accuracy: 0.9667 - val_loss: 0.3424 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9900 - loss: 0.3047 - val_accuracy: 0.9667 - val_loss: 0.3352 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9944 - loss: 0.2967 - val_accuracy: 0.9667 - val_loss: 0.3274 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2907 - val_accuracy: 0.9667 - val_loss: 0.3236 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9944 - loss: 0.2854 - val_accuracy: 0.9667 - val_loss: 0.3154 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2788 - val_accuracy: 0.9667 - val_loss: 0.3114 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2737 - val_accuracy: 1.0000 - val_loss: 0.3044 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2681 - val_accuracy: 0.9667 - val_loss: 0.3012 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2635 - val_accuracy: 1.0000 - val_loss: 0.2941 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2580 - val_accuracy: 1.0000 - val_loss: 0.2912 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2538 - val_accuracy: 1.0000 - val_loss: 0.2843 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2487 - val_accuracy: 1.0000 - val_loss: 0.2833 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2450 - val_accuracy: 1.0000 - val_loss: 0.2763 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2407 - val_accuracy: 1.0000 - val_loss: 0.2724 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2369 - val_accuracy: 1.0000 - val_loss: 0.2697 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2323 - val_accuracy: 1.0000 - val_loss: 0.2636 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2289 - val_accuracy: 1.0000 - val_loss: 0.2601 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2254 - val_accuracy: 1.0000 - val_loss: 0.2583 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2213 - val_accuracy: 1.0000 - val_loss: 0.2522 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2178 - val_accuracy: 1.0000 - val_loss: 0.2492 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2151 - val_accuracy: 1.0000 - val_loss: 0.2476 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2112 - val_accuracy: 1.0000 - val_loss: 0.2420 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2080 - val_accuracy: 1.0000 - val_loss: 0.2394 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2060 - val_accuracy: 1.0000 - val_loss: 0.2375 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2019 - val_accuracy: 1.0000 - val_loss: 0.2327 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1993 - val_accuracy: 1.0000 - val_loss: 0.2300 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1969 - val_accuracy: 1.0000 - val_loss: 0.2287 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1940 - val_accuracy: 1.0000 - val_loss: 0.2238 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1910 - val_accuracy: 1.0000 - val_loss: 0.2215 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1888 - val_accuracy: 1.0000 - val_loss: 0.2204 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1860 - val_accuracy: 1.0000 - val_loss: 0.2158 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1837 - val_accuracy: 1.0000 - val_loss: 0.2137 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1819 - val_accuracy: 1.0000 - val_loss: 0.2127 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1791 - val_accuracy: 1.0000 - val_loss: 0.2084 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1764 - val_accuracy: 1.0000 - val_loss: 0.2065 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1749 - val_accuracy: 1.0000 - val_loss: 0.2056 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1722 - val_accuracy: 1.0000 - val_loss: 0.2015 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1702 - val_accuracy: 1.0000 - val_loss: 0.1995 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1687 - val_accuracy: 1.0000 - val_loss: 0.1989 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1661 - val_accuracy: 1.0000 - val_loss: 0.1948 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1640 - val_accuracy: 1.0000 - val_loss: 0.1930 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1629 - val_accuracy: 1.0000 - val_loss: 0.1927 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1603 - val_accuracy: 1.0000 - val_loss: 0.1888 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1584 - val_accuracy: 1.0000 - val_loss: 0.1871 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1574 - val_accuracy: 1.0000 - val_loss: 0.1865 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1552 - val_accuracy: 1.0000 - val_loss: 0.1829 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1533 - val_accuracy: 1.0000 - val_loss: 0.1815 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1523 - val_accuracy: 1.0000 - val_loss: 0.1808 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1502 - val_accuracy: 1.0000 - val_loss: 0.1775 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1484 - val_accuracy: 1.0000 - val_loss: 0.1761 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1475 - val_accuracy: 1.0000 - val_loss: 0.1757 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1456 - val_accuracy: 1.0000 - val_loss: 0.1722 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1438 - val_accuracy: 1.0000 - val_loss: 0.1709 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1430 - val_accuracy: 1.0000 - val_loss: 0.1706 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1410 - val_accuracy: 1.0000 - val_loss: 0.1673 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1394 - val_accuracy: 1.0000 - val_loss: 0.1660 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1387 - val_accuracy: 1.0000 - val_loss: 0.1656 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1370 - val_accuracy: 1.0000 - val_loss: 0.1626 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1354 - val_accuracy: 1.0000 - val_loss: 0.1614 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1347 - val_accuracy: 1.0000 - val_loss: 0.1612 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1332 - val_accuracy: 1.0000 - val_loss: 0.1590 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1330 - val_accuracy: 1.0000 - val_loss: 0.1593 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1353 - val_accuracy: 1.0000 - val_loss: 0.1597 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1377 - val_accuracy: 1.0000 - val_loss: 0.1613 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 2: 1.0000\n",
|
||
"\n",
|
||
"Fold 3\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 63ms/step - accuracy: 0.3161 - loss: 2.1486 - val_accuracy: 0.4667 - val_loss: 1.9231 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4762 - loss: 1.8527 - val_accuracy: 0.5667 - val_loss: 1.7024 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6411 - loss: 1.6629 - val_accuracy: 0.5667 - val_loss: 1.5590 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6466 - loss: 1.5057 - val_accuracy: 0.6000 - val_loss: 1.4261 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7467 - loss: 1.3869 - val_accuracy: 0.5667 - val_loss: 1.3259 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7533 - loss: 1.2957 - val_accuracy: 0.6667 - val_loss: 1.2242 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7933 - loss: 1.2015 - val_accuracy: 0.7000 - val_loss: 1.1361 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8014 - loss: 1.1175 - val_accuracy: 0.8000 - val_loss: 1.0546 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8635 - loss: 1.0315 - val_accuracy: 0.8333 - val_loss: 0.9623 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8707 - loss: 0.9646 - val_accuracy: 0.8000 - val_loss: 0.8984 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8707 - loss: 0.8857 - val_accuracy: 0.9000 - val_loss: 0.8262 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9166 - loss: 0.8243 - val_accuracy: 0.9000 - val_loss: 0.7636 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9190 - loss: 0.7635 - val_accuracy: 0.9333 - val_loss: 0.7014 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9280 - loss: 0.7124 - val_accuracy: 0.9667 - val_loss: 0.6511 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9280 - loss: 0.6665 - val_accuracy: 0.9667 - val_loss: 0.6057 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9324 - loss: 0.6320 - val_accuracy: 1.0000 - val_loss: 0.5770 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9569 - loss: 0.5969 - val_accuracy: 0.9667 - val_loss: 0.5355 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9381 - loss: 0.5584 - val_accuracy: 1.0000 - val_loss: 0.5128 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9625 - loss: 0.5343 - val_accuracy: 1.0000 - val_loss: 0.4848 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9625 - loss: 0.5086 - val_accuracy: 1.0000 - val_loss: 0.4605 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9424 - loss: 0.4932 - val_accuracy: 1.0000 - val_loss: 0.4574 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.4813 - val_accuracy: 1.0000 - val_loss: 0.4310 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9741 - loss: 0.4519 - val_accuracy: 1.0000 - val_loss: 0.4094 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9741 - loss: 0.4332 - val_accuracy: 1.0000 - val_loss: 0.3964 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9741 - loss: 0.4204 - val_accuracy: 1.0000 - val_loss: 0.3809 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9741 - loss: 0.4019 - val_accuracy: 1.0000 - val_loss: 0.3702 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.3901 - val_accuracy: 1.0000 - val_loss: 0.3579 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.3764 - val_accuracy: 1.0000 - val_loss: 0.3479 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.3663 - val_accuracy: 1.0000 - val_loss: 0.3341 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.3569 - val_accuracy: 1.0000 - val_loss: 0.3329 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.3502 - val_accuracy: 1.0000 - val_loss: 0.3262 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.3367 - val_accuracy: 1.0000 - val_loss: 0.3119 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9741 - loss: 0.3261 - val_accuracy: 1.0000 - val_loss: 0.3058 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.3184 - val_accuracy: 1.0000 - val_loss: 0.2987 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.3108 - val_accuracy: 1.0000 - val_loss: 0.2908 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.3019 - val_accuracy: 1.0000 - val_loss: 0.2837 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9741 - loss: 0.2950 - val_accuracy: 1.0000 - val_loss: 0.2788 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9857 - loss: 0.2884 - val_accuracy: 1.0000 - val_loss: 0.2721 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9857 - loss: 0.2816 - val_accuracy: 1.0000 - val_loss: 0.2673 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.2763 - val_accuracy: 1.0000 - val_loss: 0.2618 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9928 - loss: 0.2699 - val_accuracy: 1.0000 - val_loss: 0.2577 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9928 - loss: 0.2651 - val_accuracy: 1.0000 - val_loss: 0.2525 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2595 - val_accuracy: 1.0000 - val_loss: 0.2479 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2546 - val_accuracy: 1.0000 - val_loss: 0.2445 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2500 - val_accuracy: 1.0000 - val_loss: 0.2394 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2451 - val_accuracy: 1.0000 - val_loss: 0.2361 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2409 - val_accuracy: 1.0000 - val_loss: 0.2315 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2366 - val_accuracy: 1.0000 - val_loss: 0.2286 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2327 - val_accuracy: 1.0000 - val_loss: 0.2248 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.2285 - val_accuracy: 1.0000 - val_loss: 0.2211 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2248 - val_accuracy: 1.0000 - val_loss: 0.2180 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2212 - val_accuracy: 1.0000 - val_loss: 0.2146 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2176 - val_accuracy: 1.0000 - val_loss: 0.2110 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2141 - val_accuracy: 1.0000 - val_loss: 0.2089 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2110 - val_accuracy: 1.0000 - val_loss: 0.2050 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2074 - val_accuracy: 1.0000 - val_loss: 0.2026 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.2046 - val_accuracy: 1.0000 - val_loss: 0.1994 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2013 - val_accuracy: 1.0000 - val_loss: 0.1971 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1988 - val_accuracy: 1.0000 - val_loss: 0.1948 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1958 - val_accuracy: 1.0000 - val_loss: 0.1912 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1927 - val_accuracy: 1.0000 - val_loss: 0.1896 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1903 - val_accuracy: 1.0000 - val_loss: 0.1862 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1875 - val_accuracy: 1.0000 - val_loss: 0.1841 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1851 - val_accuracy: 1.0000 - val_loss: 0.1823 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1829 - val_accuracy: 1.0000 - val_loss: 0.1789 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1798 - val_accuracy: 1.0000 - val_loss: 0.1776 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1782 - val_accuracy: 1.0000 - val_loss: 0.1752 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1754 - val_accuracy: 1.0000 - val_loss: 0.1724 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1733 - val_accuracy: 1.0000 - val_loss: 0.1714 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1715 - val_accuracy: 1.0000 - val_loss: 0.1685 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1687 - val_accuracy: 1.0000 - val_loss: 0.1659 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1670 - val_accuracy: 1.0000 - val_loss: 0.1658 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1656 - val_accuracy: 1.0000 - val_loss: 0.1630 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1627 - val_accuracy: 1.0000 - val_loss: 0.1605 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1611 - val_accuracy: 1.0000 - val_loss: 0.1602 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1598 - val_accuracy: 1.0000 - val_loss: 0.1571 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1571 - val_accuracy: 1.0000 - val_loss: 0.1555 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1558 - val_accuracy: 1.0000 - val_loss: 0.1546 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1539 - val_accuracy: 1.0000 - val_loss: 0.1516 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1522 - val_accuracy: 1.0000 - val_loss: 0.1516 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1511 - val_accuracy: 1.0000 - val_loss: 0.1496 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1490 - val_accuracy: 1.0000 - val_loss: 0.1471 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1472 - val_accuracy: 1.0000 - val_loss: 0.1465 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1462 - val_accuracy: 1.0000 - val_loss: 0.1447 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1441 - val_accuracy: 1.0000 - val_loss: 0.1427 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1425 - val_accuracy: 1.0000 - val_loss: 0.1421 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1416 - val_accuracy: 1.0000 - val_loss: 0.1402 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1397 - val_accuracy: 1.0000 - val_loss: 0.1385 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1385 - val_accuracy: 1.0000 - val_loss: 0.1376 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1372 - val_accuracy: 1.0000 - val_loss: 0.1361 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1358 - val_accuracy: 1.0000 - val_loss: 0.1351 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1346 - val_accuracy: 1.0000 - val_loss: 0.1333 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1330 - val_accuracy: 1.0000 - val_loss: 0.1321 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1319 - val_accuracy: 1.0000 - val_loss: 0.1314 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1306 - val_accuracy: 1.0000 - val_loss: 0.1296 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1294 - val_accuracy: 1.0000 - val_loss: 0.1290 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1283 - val_accuracy: 1.0000 - val_loss: 0.1272 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1268 - val_accuracy: 1.0000 - val_loss: 0.1264 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1258 - val_accuracy: 1.0000 - val_loss: 0.1251 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1247 - val_accuracy: 1.0000 - val_loss: 0.1239 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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9DMPQoEGD9P7775tdCgDAJIyPAgDAA3399dfcV+zlvvnmm7MOywcAlB8sewYAgIeYP3++Vq1apcDAQHXt2pX7ir3QyZMn9dBDD6lx48ZavXq1vvjiC7NLAgCYiH/pAQDwEHv37tVHH32ksLAwPfjgg2aXg1KQmZmplStX6tNPP9Xs2bNdy7IBAMon7iEHAAAAAMAE9JADAAAAAGACAjkAAAAAACbw+knd7Ha7kpKSVKlSJVksFrPLAQAAAAB4OcMwlJaWpsjIyCInafX6QJ6UlKTatWubXQYAAAAAoJw5ePCgatWqVeh+rw/klSpVkuT4RoSEhJhcDQAAAADA26Wmpqp27dquPFoYrw/kzmHqISEhBHIAAAAAwEVzrtummdQNAAAAAAATEMgBAAAAADABgRwAAAAAABN4/T3kxZGXl6ecnByzyygzfH195efnxzJyAAAAAHAByn0gT09P16FDh2QYhtmllCnBwcGKiIhQQECA2aUAAAAAQJlUrgN5Xl6eDh06pODgYIWHh9PjWwyGYchmsyk5OVkJCQlq3LhxkQvdAwAAAADOrlwH8pycHBmGofDwcAUFBZldTpkRFBQkf39/JSYmymazKTAw0OySAAAAAKDMoWtT514bDgXRKw4AAAAAF4ZUBQAAAACACQjkAAAAAACYwPRA/u233+qqq67SgQMHCj1m2bJleuSRRzR06FB9++23F6+4ciAqKkrTpk0zuwwAAAAAKHdMndQtOTlZ6enpWr9+faHH7Ny5U5MnT9a6detkt9vVtm1bffrpp7rkkksuYqWe5amnntKHH36oli1bKiEhQVu2bFH//v118uRJff/99/r9999Vr169YrV15513qnLlyqVaLwAAAACgIFN7yMPDw9W3b98ij5k1a5Z69+4ti8UiX19fdejQQa+99tpFqtAzXXLJJfrll1/03nvvadiwYapSpYreffddff7555o3b955tdWpUydddtllpVQpAAAAAKAwpi97dq7ZulesWKEnnnjC9b5JkyZasmRJocdnZ2crOzvb9T41NbX4xRiGlJlZ/OPdKThYKuZs7/379y90qbG+ffvKbre7szKUMsMw9N/l/1VWbpZmXz9bPpaS/Z1swooJ+mTXJ26uDiivDOnkKenECYnfqQAAeJTopg/ojgdnm12GW5geyM/l8OHDqlq1qut9xYoVlZSUVOjxsbGxmjRpUsk+LDNTqlixZOdeqPR0qUKFYh0aFhZW6L7PP/9c06ZN0/DhwzV58mTdeeedioqK0vjx49W8eXN9+umnmjRpkrp166Zff/1VkydP1pVXXqnHH39cr732mmbMmKFly5ZpyJAhOnXqlFasWKGGDRu666vEWaxIWKEXfn5BknRdw+t0U7ObzruNzX9u1uQfJ7u7NACVzC4AAAD82/HUP80uwW08PpBbLJZ8vcE2m03+/v6FHh8dHa3Ro0e73qempqp27dqlWqMn6datmx544AH99ttveuedd5SXl6cXXnhB9evX1+OPPy6LxaKZM2eqW7duatGihTIyMpSXlyd/f3917txZjz32mHbs2KGNGzfqpptu0ty5cxUbG2v2l+XVpqyeku9136Z9ZSnmaAmn2NWOn9GNTW7UyKtGurU+oFwwDGnjBumtedKePY5tlSpJt90mNW5sbm0AACCfpi2uMbsEt/H4QB4ZGamUlBTX+7S0NEVGRhZ6vNVqldVqLdmHBQc7eqrNEBzslmaqVq2q0NBQ9e3bVx07dpQk1axZUxUqVNCRI0f0+++/K/3vr9FqtapmzZqSHLcOOEci3HnnnZKkK6+8ssjRCLhwPx/6WSsSVsjPx09+Pn5af3i9ViSsUPcG3Yvdxu7ju7V4+2JJ0rPdnlXLGi1Lq1zAO61ZI0VHSz/84HhfsaI0erTjERpqbm0AAMCreXwg7969u/Y4eysk7d27V127di2dD7NYij1s3JNZLJZ8Pay1atXSc889p8aNG6t9+/ZKSEjId+zZXkuSn58f96OXMmfP9l0t71IF/wp6+ZeXNWX1lPMK5NN+miZDhv6vyf8RxlF+bN0q7dp1YW3k5Unvvit9/rnjvdUqRUU5wnl4+IXXCAAAcA6mB3LDMPI9S9K0adN0ww03qEWLFnrwwQf16KOPasKECcrNzdX69ev19NNPm1VumTR8+HB17dpVgwcP1vz5880uB3/b9tc2Ldu1TBZZ9ETHJxToF6i4jXFakbBC6w6t01W1rjpnGwdTDurtLW9LkqI7RZd2yYD5fvtNmjBB+sSNExj6+kr33is9+aRUjm5xAgAA5jM1kKenp+udd96RJC1YsEAPP/ywwsLCtGjRIjVo0EAtWrTQFVdcoXvvvVf//e9/ZbPZNHPmTNcwa0h5eXnKzc0963anTZs26bLLLlNmZqZ++uknnT59WgkJCapfv74Mw3D9McTZG24Yhqu3/Mw/lMC9pq6eKkm65dJb1DSsqSRpcMvBmr95vmJXx2rp7UvP2caMtTOUY8/RtfWuVYfaHUqzXMBc+/ZJEydK77/vuN/bx0fq0EHyu8B/xho0kMaNk5o0cU+dAAAA58HUQF6xYkWNGDFCI0aMyLd948aN+d7fe++9F7OsMmPjxo364IMP9Ndff2nu3Lm69dZb9c033+jIkSOaO3euWrRooZo1a+rRRx/Vf//7X/3000+666679NFHH2n79u3KysrSzz//rAMHDigxMVFvv+3oaX3zzTfVvXt3/fjjj0pKStL27dvVokULk79a77L/5H7Fb4uXlL9n+4mOT2jB5gX6ZNcn2vbXNl1WvfA14pMzkvX6xtclSTGdYkq3YMAsSUnSM89Ic+dKzj8+DhwoPf201KyZubUBAABcIIvh5V2gqampCg0NVUpKikJCQvLty8rKcvUUF7auN86O792FGf7ZcM3ZOEe9G/XWl4O+zLdv4OKBWvL7Eg26fJDeHfBuoW1MWDFBk3+crDaRbbT+gfXnPTM74NGOH5emTZNmz5ZOn3Zs691bevZZqXVrc2sDAAA4h6Jy6JlMv4ccKG+S0pI0b/M8SWfv2Y7uFK0lvy9R/LZ4Pd31aTWo0qDAManZqZq9frarDcI4yoxVq6QpU6TExKKPO3z4n1UvOnZ0nNOlS+nXBwAAcBERyIGLbObambLl2dSpTid1rtu5wP7WEa3Vq2Evfb3va037aZribowrcMxrv7ymlOwUNQ9rrpua3XQxygYuzIYN0vjx0vLlxT+nVStHEO/Tx7EKBgAAgJchkAMX0YnTJ/TahtckFX3fd0znGH2972vN2zxPE6+ZqIhKEa59p3NO64WfX5Akjes0Tj4Wn9ItGrgQO3Y4ZkX/8EPHez8/adgw6dZbHROzFSY4WLryyqKPAQAAKOMI5MBFNHvdbGXkZOiKmleod6PehR7XuU5ndazdUT8d/EkvrH1B06+b7tr31qa39FfGX6obWld3XHbHxSgbOH+JidJTT0lvvy3Z7Y4e7sGDHdsaFLwNAwAAoDwikAMXSVp2ml5c96Ikx33iRd33bbFYFNM5Rje8f4Ne2/CaojtHq2pQVeXk5Wj6Gkc4f7zj4/L39b8otZ+Xo0el775zhDCUT+vWSXPmSDk5jvf9+jlmSr+s8FUDAAAAyiMCOXCRvL7xdZ3MOqnGVRvr5uY3n/P4Po36qFWNVtpydItmr5utiddOVPy2eCWmJKpGhRq69woPXA7QMKQbb3TcLwx06+a4B/yqq8yuBAAAwCMRyIGLIDs3WzPWzpDkuO/b18f3nOc4e8lvW3KbXlz3oh7r8JhiV8dKkkZ3GK0g/6BSrblEvv7aEcaDgqROncyuBmapVEmKipK6dze7EgAAAI9GIAcuggVbFuhI+hHVCqmlwS0HF/u8m5vfrMZVG2vPiT3qv6i/dh7bqcqBlTW8zfBSrPYCTJnieB4xQpoxw9xaAAAAAA/H9LVAKcu15+q5n56TJI29eqwCfAOKfa6vj6+e6PiEJGlFwgpJ0sNtH1aINcT9hV6o1aulH3+U/P2l0aPNrgYAAADwePSQlxOrV6/W008/rUGDBumee+4xuxyvkZOXo1uX3KrNf24u8pjDaYcVFhymB1o/cN6fcVeru/TUqqd0KPWQgv2DNbL9yJIV+8sv0sMPS3/9VfRxdepIH30kVat2fu3HOobTa8gQ6ZJLSlQiAAAAUJ7QQ17GLFmyROHh4bJarfrQua7v32bOnKmAgAC9/vrrBc5r1aqVDh48KMMwLlap5UL8tngt3blUB04dKPRxOO2wJOmJjk8o2D/4vD8jwDdAE6+ZKEkaddUohQWHnX+hW7ZI110nrV8vHThQ9OOHH6QxY86v/c2bpS++cKwZ/fjj518fAAAAUA7RQ17G3HLLLTp48KBiYmLUp0+ffPtuvvlmJSQkaNiwYQXOq1SpkqpXr36xyiwX7IbdNcna2KvH6pZLbyn02GD/YLUIb1Hiz3qg9QPq0aCH6oTWOf+Td+2SevaUTp2Srr7acW+3TyF/i0tMlG67TVqwQLrrruJPyuXsHb/tNqlRo/OvEQAAACiHCORnMAxDmTmZpnx2sH9wketSn2nIkCEaP368Fi9enG/4+aJFizR8eOGTfRW3fRTP0p1LXZOs/a/L/0r9vu56leud/0kJCY5QnZwsXXml9PnnUuXKhR/frp3jPvDZs6UHH5R++80xY3pRdu+WFi92vB437vxrBAAAAMopAvkZMnMyVTG2oimfnR6drgoBFYp1bJUqVXTbbbdpzpw5+QL5jh07dP311+v+++9Xw4YN9fnnnysuLk6XX355aZVdbhmGoSk/OmYUvyiTrNls0ldfSR06SOHhxTvn8GGpRw/H86WXSsuXFx3GnZ59Vvr4Y2nfPunpp//p/S7MtGmO9cf/7/+kli2LVxsAAAAA7iEvq4YPH661a9dq69atkqQ1a9aoQ4cOmjhxoq655hrFxMToiiuuOOv95Lhw3+7/VhuPbLywSdaKy2aT+veXbrpJatBAevJJKSWl6HOSkx1hfP9+qWFD6ZtvpLBi3nteqZL0yiuO19OnO+4/L8zBg9LbbzteR0cXr30AAAAAkughzyfYP1jp0emmffb5uOqqq3TllVcqLi5Or776qj744AM988wzatOmjerXr699+/bpwIED3DdeSqasdvSOD2s9rGSTrBVXbq40eLBjwjRJSk+XnnnGEZijo6WHHio4pPzUKccEbjt3SrVqSd9+K0VGnt/n9u0r3XKLtGSJNHSotHat5Otb8LgZM6ScHOnaax299wAAAACKjR7yM1gsFlUIqGDKoyT3dw8fPlzvvfeejh49KrvdrkqVKql27dp67rnn9Ntvv6l169bMql4K1hxco5UHVsrfx19jrj7P2cjPh90uPfCA4/7sgADpyy8dAblZM+nECWnsWMcEanPmOEKx5Ajsffo4Zj2vXl367jupXr2Sff5LL0mhoY7l0l5+ueD+5GTJOQIjJqZknwEAAACUYwTyMmzQoEGSpIEDB2rgwIGSpAEDBui6665Tv3795Hu2Hk1cMOfM6ne3ulu1QmqVzocYhvTII47Zzn19pYULpd69pZtvdky0Nm+eY73wpCRp+HCpeXPpnXccPds//yxVqeIYpt6kSclriIiQnnvO8Xr8eOmPP/Lvf/FF6fRpqU0bx/B4AAAAAOeFQF6GVahQQYMHD9aff/6pzp07S5I2bdqk5ORknTx5Uhs3btTp06eVkJAgyTERGT3mF2br0a36bPdn8rH46ImOT5TOhxiGY7byV1+VLBZHKO/f/5/9fn7SkCGO2c1fesnRE75vn3T33dL330sVKzomgHPHBGtDh0qdOkkZGVJUlKM2SUpN/afXPCbGUScAAACA80IgL+NGjBih++67z/V+9OjRGjp0qB577DH93//9n1avXq3k5GRt2LBB27Zt06effqqkpCQTKy7bpq6eKkkaeOlANa7WuHQ+5NlnHTOXS1JcnPT3SIgCrFZHL/q+fY5zQkOlChWkzz5zLF/mDj4+jmHp/v6OJdOcy5u99ppjYrnmzR2TzQEAAAA4bxbDy7tMU1NTFRoaqpSUFIWE5F+aKisrSwkJCapfv74CAwNNqrBsKo/fu70n9qrpy01lN+za/OBmtarZyv0fMnOmNHq04/ULL0iPPVb8czMypOxsqWpV99f11FPSpElSjRrSr7861jT/6y9H7/3dd7v/8wAAAIAyrKgceiZ6yIFimvbTNNkNu25ofEPphPE33vgnjD/99PmFccnRO14aYVxyzOjerJl09Kh09dWOMF63rnTHHaXzeQAAAEA5QCAHiuFw6mHN3zxfkhTdyc3rbRuGNH++9OCDjvdjx0r/+597P+NCWa3/zKiemOh4fvxxx1B2AAAAACVCIAeKYcbaGcqx56hL3S7qWKej+xpeu1bq2lW6915HMB8xwjGzuSdOkta5szRsmON1jRqOmgEAAACUmJ/ZBQCe7njmcc3ZOEeSFNPJTettb93q6AX/9FPH+4AAacwYafJkzwzjTtOnO4bG33CDFBRkdjUAAABAmUYgl1gKrATK0/fspXUvKTMnU60jWuu6htddWGN790oTJ0rx8Y4ecR8fR0/zk0861hX3dCEhjsnmAAAAAFywch3IfX19JUk2m01B9Padl8zMTEmSv5ffQ5yWnaaX1r8kydE7bilp7/Xhw9Izz0hvvinl5jq23XqrY/K2pk3dVC0AAACAsqRcB3I/Pz8FBwcrOTlZ/v7+8vHhlvpzMQxDmZmZ+uuvv1S5cmXXHzW8VdyGOJ3KOqVmYc3Uv3n//DsNw7Hm9+TJjrXAi5KaKuXkOF736eNYN/zKK0unaAAAAABlQrkO5BaLRREREUpISFCic+ZoFEvlypVVs2ZNs8soVVm5WXrhZ8fw7Cc6PiEfyxl/sFm5UoqJcUzKVlydOklTpjgmRwMAAABQ7pXrQC5JAQEBaty4sWw2m9mllBn+/v5e3zMuSfM3z9ef6X+qTmgdDbp8kGPjhg3S+PHS8uWO90FB0siR0qBBUlHfk8BAqV49z56wDQAAAMBFVe4DuST5+PgoMDDQ7DLgQXLtuXrup+ckSWOvHiv/3Xsds6J/9JHjAH9/xxJg48dLEREmVgoAAACgrCKQA2excNtCHTh1QNUDq+n+136W3h4p2e2OHu677pKeekqqX9/sMgEAAACUYQRy4F/shl2xK5+RJI368qSCVr3n2NG/v2Om9BYtTKwOAAAAgLcgkANnOnlSn84Yqt/9dyskS4r62S517+6YjK1dO7OrAwAAAOBFWOcLkKSMDCk2VkaD+ppy9ENJ0sOHIhX6xXfSt98SxgEAAAC4HT3kKN9sNun11x1riR89qhX1pfW1pCBLgEa+9qtUsYbZFQIAAADwUgRylF9padLVV0vbtjneN2igKQ8GSqd/19C2w1WdMA4AAACgFDFkHeXX0qWOMF61qvTqq/r52/lacfp3+fn4aczVY8yuDgAAAICXo4cc5dfHHzueH3pIGjFCsQtvkiTd1fIu1QmtY2JhAAAAAMoDeshRPmVmSl995Xjdv7+2/bVNy3Ytk0UWPdHxCXNrAwAAAFAu0EOO8mn5cun0aaluXemKKzT147skSbdceouahjU1uTgAAAAA5QE95CifnMPV+/fX/lMJit8WL0mK7hRtYlEAAAAAyhNTe8gzMjI0duxYhYaGKiMjQ9OnT5fVas13TEpKisaOHauIiAglJCRo9OjRuuKKK8wpGN4hJ0datszxun9/TftpmuyGXb0b9daVEVeaWxsAAACAcsPUHvIRI0aoZ8+eio2NVZs2bRQdXbB38qGHHlK3bt00adIkTZs2TbfccosyMzNNqBZeY9Uq6dQpKTxcSS3ra97meZKkmE4x5tYFAAAAoFwxLZAnJSVp8eLF6tOnjySpT58+iouLU1pamuuY7OxsLVy4UJdffrkkqWbNmoqMjNR7771nSs3wEs7h6jfdpJnrX5Itz6ZOdTqpc93O5tYFAAAAoFwxbcj6ypUrFRYWpsDAQElSeHi4rFar1q9fr+7du0tyDGnPy8vT4cOH1aJFC0lS7dq1tW3btkLbzc7OVnZ2tut9ampqKX4V8GQvr39ZT3z7hHLtufl3VLNJ/5PkP1+2tY599I4DAAAAuNhM6yE/fPiwqlatmm9bxYoVlZSU5HpftWpV/ec//9GLL76ovLw8paamaufOnbLb7YW2Gxsbq9DQUNejdu3apfY1wLPN2zxPmTmZsuXZ8j98JZufZDMcYbxznc7q3ai3ydUCAAAAKG9MC+QWi8XVO+5ks9nk7++fb9uSJUsUFBSk/v3766233tLvv/+uZs2aFdpudHS0UlJSXI+DBw+WSv3wbHbDrp3HdkqSfhjygw4+dtDxOD1CB1+QDu75P9e27+/5XhaLxeSKAQAAAJQ3pg1Zj4yMVEpKSr5t6enpioyMzLetXr16WrJkiSTpiy++UF5engYOHFhou1artcBM7Sh/DqUeUmZOpvx9/NW+Vnv5+/pLhiF99K2UKqnvYCmkltllAgAAACjHTOsh79q1qw4dOiSbzSZJrqHq7dq1O+vxdrtdzzzzjKKjo1W9evWLVifKph3JOyRJjao2coRxSfr9d2nPHslqlf6eTBAAAAAAzGJaII+IiFDv3r21atUqSdLy5csVFRUlq9WqmJgYHTlyJN/xkyZNUoMGDTRhwgQzykUZ4xyu3jy8+T8bnbOr9+ghVapkQlUAAAAA8A/ThqxLUlxcnMaNG6d169bpxIkTmjp1qrKyshQfH6++ffsqIiJCn376qTZu3KhLLrlETz31FPf6olh2HHP0kDerdsZ8A85A3r+/CRUBAAAAQH4WwzAMs4soTampqQoNDVVKSopCQkLMLgcXyTXzr9EPiT/onf7vaHDLwVJiolSvnuTjI/35pxQebnaJAAAAALxUcXOoaUPWgdLkGrIe9veQdWfveOfOhHEAAAAAHoFADq9z4vQJ/ZXxlySpaVhTx0aGqwMAAADwMARyeB1n73jtkNqqGFBRSk6WVq927OzXz7zCAAAAAOAMBHJ4HeeSZ64Z1pctk+x2qXVrqW5dEysDAAAAgH8QyOF1CsywznB1AAAAAB6IQA6vk28N8rQ06ZtvHDsGDDCxKgAAAADIj0AOr+PqIQ9rJn3xhWSzSU2aSM2bm1wZAAAAAPyDQA6vkpWbpYSTCZL+XvLszOHqFouJlQEAAABAfgRyeJXdx3fLkKEqgVVU3S/U0UMucf84AAAAAI/jZ3YBQInZ7VJubr5NO478JklqVq2pLN9847iHPDJSatvWjAoBAAAAoFD0kKPsSUuTnnlGqlZNslrzPXaOGixJav7pz1Lfvo7j+/eXfLjUAQAAAHgWeshRdmRlSXFx0pQpUnLyWQ/ZEe54bnbs7w1BQdJ9912c+gAAAADgPNBtCM+Xmyu9+aZjpvTHHnOE8UaNpPh46eRJ6dQp12PHtS0kSc1fXujYdvKk1Lq1mdUDAAAAwFnRQw7PZbdLS5ZIEyZIu3c7ttWqJU2cKN1zj+Tvn+/wPHuedp/aJ0lqXreNFBp6sSsGAAAAgGIjkMMzbdok3X+/41mSwsKkmBhpxAgpMPCspySmJCorN0tWX6vqVa538WoFAAAAgBIgkMPzGIZ0553Szp1SpUrSf/8rjRolhYQUedrOYzslSU2qNZGvj+9FKBQAAAAASo5ADs+zY4cjjAcEOIaq16xZvNOSd0iSmoU1K83qAAAAAMAtmNQNnuejjxzPPXoUO4xL0o5jjkDePKx5aVQFAAAAAG5FIIfn+fhjx/OAAed1mnPIevNwAjkAAAAAz0cgh2dJTJR+/VXy8ZH69i32aYZhuHrIGbIOAAAAoCwgkMOzLF3qeO7USQoPL/ZpxzKP6cTpE7LIoibVmpRObQAAAADgRgRyeBbn/eP9+5/Xac7e8bqV6yrYP9jdVQEAAACA2xHI4TmSk6XVqx2v+/U7r1OdM6wzoRsAAACAsoJADs+xbJlkt0utW0v16p3Xqa4J3QjkAAAAAMoIAjk8h3N29fMcri6JCd0AAAAAlDkEcniG1FTpm28cr0sQyFnyDAAAAEBZQyCHZ/jyS8lmkxo3li699LxOzbBlKDElURJD1gEAAACUHQRyeIYzh6tbLDIMQ4ZhFOvUXcd3SZLCgsNULbhaaVUIAAAAAG5FIIf5srOlL75wvB4wQJI09NOhqvJcFf2e/Ps5T2dCNwAAAABlEYEc5vvuOyktTYqMlNq21ba/tunNTW8qJTtFz/zwzDlPdy55xoRuAAAAAMoSAjnM99FHjud+/SQfH01dPdW164PtH2jP8T1Fnu6cYZ0ecgAAAABlCYEc5srLc6w/Lkn9+2v/yf2K3xYvSWoR3kJ2w65pP00rsglmWAcAAABQFhHIYa6ffpKSk6UqVaRrrtH0n6bLbtjVu1FvzblxjiRpwZYFOpx6+Kyn59pztfv4bkkMWQcAAABQthDIYS7n7Or/9386knVMb21+S5IU3SlaHet0VJe6XZRjz9GMtTPOenrCyQTl2HMU5BekOqF1LlbVAAAAAHDBCOQwj2H8c/94//56Ye0LsuXZ1LF2R3Wu01mSFNMpRpI0Z+McHcs8VqAJ5/3jTcOaysfC5QwAAACg7CDBwDybNkl//CEFBelE5zZ6bcNrkqSYzjGyWCySpOsaXqfWEa2VmZOpl9a9VKAJ5wzrTOgGAAAAoKwhkMM8zuHqvXtr9m9vKiMnQ61qtFKfRn1ch1gsFlcv+ez1s5WWnZaviZ3HWYMcAAAAQNlEIId5/g7k6Tf10UvrHb3fZ/aOO/Vv3l9NqzXVqaxTitsQl28fa5ADAAAAKKsI5DDH7t3S9u2Sn59er3VUJ06fUOOqjXVz85sLHOpj8dG4TuMkSTPWzlBWbpYkyTAMljwDAAAAUGYRyGGOv3vHs7tdo+c3vypJeqLjE/L18T3r4YMuH6Q6oXV0NOOo5m2aJ0n6M/1PpWSnyMfio8ZVG1+cugEAAADATQjkMMffgXxBrxo6kn5EtUJq6a5WdxV6uL+vv8ZePVaSNG3NNOXac10zrDeo0kBWP2vp1wwAAAAAbkQgx8V3+LC0bp1yfaRpljWSpP92+K8CfAOKPO2+K+9TeHC4Dpw6oIXbFv4zXJ0J3QAAAACUQQRyXHyffCJJWnxTI+1LPaCw4DA90PqBc54W7B+sx9o/JkmKXR2r35N/l8SEbgAAAADKJj8zPzwjI0Njx45VaGioMjIyNH36dFmt+Yce5+bmKiYmRmFhYcrIyFCVKlU0atQocwqGe3z8sewWacoV6ZIhjbxqpCoEVCjWqVFtozT1p6n6Pfl3HUo9JIkecgAAAABlk6k95CNGjFDPnj0VGxurNm3aKDo6usAxcXFxCg0N1eOPP65Jkybp008/1bp160yoFm5x4oT0/ff6vLG0zfhTlQIq6aG2DxX79NDAUD3c9mFJUmp2qiRmWAcAAABQNpkWyJOSkrR48WL16dNHktSnTx/FxcUpLS0t33E7duzIty0wMFApKSkXtVa40WefycjL05ReQZIcPd5VgqqcVxMj249UoF+g6z1D1gEAAACURaYF8pUrVyosLEyBgY5gFR4eLqvVqvXr1+c7bsCAAZo9e7Z++uknJSQkKCwsTD179iy03ezsbKWmpuZ7wIN8/LF+riX9XO20Av0CXfeEn4/qFapraOuhkqSaFWuqcmBlNxcJAAAAAKXPtHvIDx8+rKpVq+bbVrFiRSUlJeXb1r17dz333HPq1auX+vbtq3fffVcWi6XQdmNjYzVp0qRSqRkXKDNT+vprrb3C8fb6xterRsUaJWpqXKdxWntorfo26eu++gAAAADgIjKth9xisbh6x51sNpv8/f0LHBscHKxFixbp22+/VVRUVJHtRkdHKyUlxfU4ePCgW+vGBfj6a+n0ae2oX1GS1CK8RYmbiqwUqV+G/qIJ10xwV3UAAAAAcFGZ1kMeGRlZ4F7w9PR0RUZG5tv2zjvv6PTp07rhhhu0YsUKdezYUV27dtVtt9121natVmuBmdrhIT76SJK0s0GIpHTu/QYAAABQrpnWQ961a1cdOnRINptNklxD1du1a5fvuEWLFqlRo0aSpMsuu0yjR4/Wjz/+eHGLxYXLyZE++0yStCMoQxLLlQEAAAAo30wL5BEREerdu7dWrVolSVq+fLmioqJktVoVExOjI0eOSJKuuOIKbdq0yXWer69vgdCOMmDlSunUKSXXCdPxHMfIiKZhTc2tCQAAAABMZOo65HFxcVq0aJEmT56srVu36tlnn1VWVpbi4+OVmJgoSRo/frz+/PNPzZo1S6+99poCAgJ01113mVk2SuLjjyVJO/+vvSSpbmhdBfsHm1kRAAAAAJjKtHvIJSksLExz584tsD0hIcH1OigoSLNmzbqIVcHt7HZp6VJJ0o629aUDUvNwhqsDAAAAKN9M7SFHObFunXTkiFSpknaGO5asa1aNCd0AAAAAlG8EcpS+v4er64YbtOPkbkn0kAMAAAAAgRylyzD+CeQDBmhH8g5JzLAOAAAAAARylK7t26W9eyWrVZk9rlFiimOyPtYgBwAAAFDeEchRupy94z17alfWYUlStaBqCq8QbmJRAAAAAGA+AjlK10cfOZ7799fOYzsl0TsOAAAAABKBHKUpIUHavFny8ZH69tWOY9w/DgAAAABOBHKUnr/XHleXLlJY2D+BnBnWAQAAAIBAjlLkvH+8f39JYsg6AAAAAJyBQI7ScfSotHq143W/fsq152r38b/XIGfIOgAAAAAQyFFKli1zrEHepo1Up44OnDogW55NgX6BqhNax+zqAAAAAMB0BHKUjn8NV9+R7Lh/vGm1pvL18TWrKgAAAADwGARyuF9qqvTdd47XzkDOhG4AAAAAkA+BHO73xReSzSY1bSo1dwRw14Ru1ZjQDQAAAAAkAjlKg3O4+oABrk30kAMAAABAfgRyuFdWlqOHXHINVzcMgyXPAAAAAOBfCORwr2+/ldLTpVq1HDOsSzqacVSnsk7Jx+KjJtWamFwgAAAAAHgGAjncyzlcvV8/yWKR9M8M6/Ur11egX6BJhQEAAACAZyGQw31ycx3rj0uu4eqSGK4OAAAAAGdBIIf7/PSTdOyYVLWq1KWLa7NrQrcwJnQDAAAAACcCOdzno48cz337Sn5+rs30kAMAAABAQQRyuIdhSEuXOl6fMVxdYskzAAAAADgbAjnc49dfpT/+kCpUkHr2dG1Oy07TodRDkughBwAAAIAzEcjhHs7Z1Xv3loKCXJt3Hd8lSapeobqqBlU1ozIAAAAA8EgEcriHM5D/e7h6MhO6AQAAAMDZEMhx4Xbtkn7/XfL3l264Id8uJnQDAAAAgLMjkOPCOXvHu3WTKlfOt4slzwAAAADg7AjkuHCFDFeXmGEdAAAAAApDIMeFOXxYWr9eslikm27KtysnL0d7T+yVxJB1AAAAAPg3AjkujHPt8Q4dpJo18+3ad3Kfcu25quBfQbVCal382gAAAADAgxHIcWE++sjxPGBAgV3OCd2ahjWVj4VLDQAAAADOREpCyR0/Lq1a5Xh9tvvHWfIMAAAAAApFIEfJffaZlJcntWwpNWhQYDczrAMAAABA4QjkKLkiZleXWIMcAAAAAIpCIEfJZGRIX3/teH2WQG4YhiuQs+QZAAAAABREIEfJfPWVlJUl1a/vGLL+L0lpSUqzpcnX4qtGVRuZUCAAAAAAeLYSBfJdu3a5uw54ipMnpaSkcx/nHK4+YIBjDfJ/cd4/3rBqQwX4BrizQgAAAADwCiUK5H369NHzzz+vo0ePursemMkwpE6dpEsuke68U9qz5+zH2WyOCd2kc94/zoRuAAAAAHB2JQrkS5cuVb9+/RQXF6eHHnpIS5YsUU5Ojrtrw8W2c6f0+++O1/HxUvPm0oMPSocO5T9u5UopJUWqUUPq0OGsTTmXPGNCNwAAAAA4uxIF8pYtW6pRo0aaOHGiZs2apa+++koRERF69NFHtXHjRnfXiIvlxx8dz5dfLl1/vWNJs9dflxo1kv77X+nYMcd+53D1m26SfM5+CbHkGQAAAAAUrUSBfMuWLUpNTdXzzz+vRo0aafXq1Zo8ebImTJig3377Tffcc48SEhLcXStKmzOQ33ST9PnnjvedO0vZ2dKMGY61xidNkpYudRxXyHB1iSXPAAAAAOBc/EpyUrdu3ZSZmakuXbrotdde0/XXX+/aN2TIEAUGBurmm2/Wr7/+6rZCcRE4A3nnzo7nTp2kVascy5vFxEibNklPPeXYFxIidet21mZSslJ0JP2IJAI5AAAAABSmRD3kjRo10vr16/X111/nC+NOmZmZstvtF1wcLqKDB6XERMcQ9DPvC7dYpN69pQ0bpA8+kJo0cWy/4w4p4Oyzpzt7xyMrRSo0MLS0KwcAAACAMqlEgfzjjz/W5Zdf7nqfl5eXb/99992nzZs3n7OdjIwMRUVFKTo6Wo8++qiys7MLHPPee+/JYrHkewwcOLAkZaMoq1c7nq+8UqpUqeB+Hx9p4EBp+3Zp/Xpp1qxCm3LeP07vOAAAAAAUrkSBfNOmTWratKmS/l6vetOmTZowYYJOnTp1Xu2MGDFCPXv2VGxsrNq0aaPo6OgCx2zYsEHLli3T2rVrtXbtWj3yyCPq27dvScpGUf49XL0wfn5S27ZSYGChhzhnWGdCNwAAAAAoXIkC+XPPPaeHH35YNWvWlCS1adNGnTt31pAhQ4rdRlJSkhYvXqw+ffpIcqxtHhcXp7S0tHzHjRkzRv/3f/+n9u3bq3379tq3b59uvPHGkpSNohQ3kBfDzuNM6AYAAAAA51KiQN6rVy898sgj8jljyavs7GytWrWq2G2sXLlSYWFhCvy7pzU8PFxWq1Xr16/Pd1ytWrVcr1NSUmQYhqpUqVJou9nZ2UpNTc33wDmcOCFt2+Z43anTBTWVZ8/Tr0cck/nRQw4AAAAAhStRILfb7Vq2bJkyMzN18uRJxcfHa9iwYerevXux2zh8+LCqVq2ab1vFihVdw+DP5vPPP9cNN9xQZLuxsbEKDQ11PWrXrl3smsqtn35yPDdtKlWvfkFNLf59sQ6lHlK1oGpqX6u9G4oDAAAAAO9UokD+xBNPaNWqVapevbrCwsI0ZMgQ9ejRQ2+88Uax27BYLK7ecSebzSZ/f/9Cz1m2bJn69etXZLvR0dFKSUlxPQ4ePFjsmsot53D1C+wdNwxDsatjJUkjrxqpCgEVLrQyAAAAAPBaJVqHPCAgQDNmzNDzzz+v5ORkhYWF6c8//1SFCsUPYJGRkUpJScm3LT09XZGRkWc93maz6dixY7rkkkuKbNdqtcpqtRa7Dsht949/secLbT26VRUDKurhdg+7oTAAAAAA8F4lCuSSY2b19PR0GYYhyXF/9+uvv65PP/20WOd37dpVw4YNk81mU0BAgGuoert27c56/HfffXdeQ+JRTJmZjjXGpQsK5IZh6Nkfn5UkRbWJUpWgwu/zBwAAAACUMJAPHjxYn376qfz9/VXp7zWrU1JS1KFDh2K3ERERod69e2vVqlXq2bOnli9frqioKFmtVsXExOiRRx5RRESE6/ilS5fqscceK0m5KMr69VJurhQZKdWvX+Jmfkj8QWsPrZXV16rHOvBzAgAAAIBzKVEgDwoK0smTJ/XNN9+oXr16atq0qdavX68dO3acVztxcXEaN26c1q1bpxMnTmjq1KnKyspSfHy8+vbt6wrkhmFoz549ataMZbTc7szh6hZLiZuZsnqKJOm+K+9TzYo13VEZAAAAAHi1EgXy8PBw+fj4qFevXnryySf19NNPq1mzZho4cKDuueeeYrcTFhamuXPnFtiekJCQ773FYtGKFStKUirOxQ33j29M2qjl+5bL1+KrsVePdVNhAAAAAODdShTIa9WqpcDAQH399dfq2rWrmjZtqoyMDNWsSc9omZKbK61d63h9AYHcObP6nZffqfpVSj7sHQAAAADKE4vhnJXtPBw7dkwBAQEKCQmRJO3evVvbtm1Tz549XfeUe4rU1FSFhoYqJSXFVS/+tmGD1LatVLmydPy45HP+q+DtSN6hFq+2kCFD26O269LwS91fJwAAAACUIcXNoSVah/yyyy5TfHy8632TJk00YMAAjwvjOAfncPWOHUsUxiXpuZ+ekyFD/Zv1J4wDAAAAwHkoUQobOnSo2rRpU2D7559/fsEF4SK6wPvHD5w6oHe3vitJiu4U7a6qAAAAAKBcKNE95Fu2bNH8+fPVqFEjWf6emTs3N1dbt27VqVOn3FkfSothSKtXO16XMJA/v+Z55Rl56tGgh9pe0taNxQEAAACA9ytRIG/QoIHat2+vyMhIVyCXpI8//ththaGU7d4tJSdLVqv0n/+c9+lH04/qzU1vSpJiOsW4uzoAAAAA8HolCuRPPPGEwsLC5O/v79qWl5ennj17uq0wlDLncPWrrnKE8vM06+dZysrNUvta7XVtvWvdWxsAAAAAlAMlCuTffPNNgW3Jyck6cOCAZs+efcFF4SK4gPvHT2Wd0iu/vCLJ0Tt+5igJAAAAAEDxlCiQP/7442revHm+bQcPHiywDR7sAgL5K+tfUZotTZdVv0w3NLnBzYUBAAAAQPlQokC+ZMkSderUKd+2lStX6tChQ24pCqXs8GEpIcGx1FmHDud1aoYtQ7PWzZLkmFndx1Ky5dIAAAAAoLwrUZr6dxiXpFatWumJJ5644IJwETh7x6+4QipikfqzWfL7Eh3LPKYGVRro1ha3ur82AAAAACgnStRD3q1bt3zv8/LytGvXLrVs2dItRaGUXcBw9c1/bpYk3dT0Jvn5lOjyAQAAAACohIE8JCRE/fr1c03m5ePjoxo1aqhHjx5uLQ6l5AIC+c7jOyVJzcOYLwAAAAAALkSJAvlLL72kOnXquLsWXAwnT0rbtjlen+XWg3PZkbxDktQ8nEAOAAAAABeiRPeQ//nnn+rVq5eSk5MlSWvWrNHMmTNls9ncWhxKwZo1kmFITZpINWqc16mZOZlKTEmUJDULa1Ya1QEAAABAuVGiQD569GhddtllCvl7QrCrr75adevW1fDhw91aHEqBc7h6CXrHdx3bJUkKCw5TWHCYO6sCAAAAgHKnRIG8R48emjFjhqxWq2tb5cqV9fHHH7utMJSSC7h/fMexv4erc/84AAAAAFywEgXy3Nxcbd261fV+7dq1euihh9S6dWu3FYZScPq09MsvjtclmdDtmGNCN4arAwAAAMCFK9Gkbo8//rjuvfde/fjjj8rOzlZaWpo6deqkBQsWuLs+uNP69VJOjhQRITVocN6n00MOAAAAAO5T4mXPPvzwQx09elSJiYmKjIxUrVq13F0b3G3hQsdz587S30vWnQ96yAEAAADAfUo0ZP348eOKjY1VSEiI2rVrpz/++EPffvutu2uDO/38szRnjuP1gw+e9+m59lztPr5bEkueAQAAAIA7lCiQ33777frwww+VmZkpyTHL+tatWzVr1ix31gZ3sdmkoUMdy53dc4/Urdt5N5FwMkG2PJuC/IJUJ5Q16AEAAADgQpUokF922WXasGGDqlWr5tp29dVXKzY21m2FwY2ef17atk0KC3O8LgHncPWmYU3lYynRZQMAAAAAOEOJklVgYKBsNpvr/enTpzV16tR8AR0eYs8e6emnHa9nznSE8hJgQjcAAAAAcK8STeo2ZMgQderUSfXq1VN2drZWr16tvLw8ffLJJ+6uDxfCMBz3i2dnS9ddJw0aVOKmmNANAAAAANyrRD3kTZs21cqVK3XzzTerY8eOevHFF5WQkKC0tDR314cLMX++9P33UlCQ9NprJZpZ3YkecgAAAABwrxL1kEtScHCwbrvtNklSVlaW3n33XT355JNKSkpyW3G4AEePSmPGOF4//XSJ1h13MgxDO5L/DuTMsA4AAAAAblHiQC5JmzZt0ty5cxUfH6/MzEz5+vq6qy5cqMcek06elK68Uho16oKaOppxVCnZKfKx+Khx1cbuqQ8AAAAAyrnzHrKelpamuLg4tWnTRv/5z3/00UcfacqUKUpOTtbixYtLo0acry+/lOLjJR8f6Y03JL8L+ruLq3e8QZUGsvpZ3VEhAAAAAJR7xQ7kP/74o4YMGaKIiAiNHz9enTp10tatW3X77bdr+PDhqlSpkq6//vrSrBXFkZ4ujRjheD1qlPSf/1xwk877x5nQDQAAAADcp9hdpzt27NBvv/2mVq1aacmSJYqIiJAkWS5gojCUgieflBITpbp1pUmT3NKkc4Z1JnQDAAAAAPcpdiAfNmyYhg0bpg0bNig2Nla5ubkadAHLaKEUbNggvfii4/Vrr0kVK7qlWWZYBwAAAAD3O+97yNu0aaOXXnpJ06ZN07Zt27RlyxbNnDlTp06d0vvvv18aNaI4DEMaPlyy26U77pD69HFb06xBDgAAAADuZzEMw7jQRn755RfNmTNH8fHxysjIcEddbpOamqrQ0FClpKQoJCTE7HJKT1KSdMklkq+v43X16m5pNi07TSFTHd+3E4+fUJWgKm5pFwAAAAC8VXFz6IVNv/23tm3bqm3btmrdurU7mkNJ7NnjeK5Xz21hXPqnd7xGhRqEcQAAAABwo/Mesl6UqKgodzaH87F7t+O5SRO3Nuua0C2c+8cBAAAAwJ3cGshhImcPeePGbm2WCd0AAAAAoHQQyL1FKfeQM6EbAAAAALgXgdxb0EMOAAAAAGUKgdwb5OVJe/c6XruxhzwnL0d7TzjapYccAAAAANyLQO4NDh6UbDYpIECqXdttze47uU+59lxVDKioWiG13NYuAAAAAIBA7h2c9483bOhYh9xNdiQ7hqs3C2smi8XitnYBAAAAAARy7+C8f5wJ3QAAAACgzPAz88MzMjI0duxYhYaGKiMjQ9OnT5fVaj3rscePH9ebb76pWrVq6bLLLlPLli0vcrUezNlDzoRuAAAAAFBmmNpDPmLECPXs2VOxsbFq06aNoqOjz3pcQkKC7rnnHt1///268847CeP/Vko95M5ATg85AAAAALifaYE8KSlJixcvVp8+fSRJffr0UVxcnNLS0vIdl52drX79+mnmzJmqVq2aGaV6vmL2kGfnZuv9395Xui39nE0ahuEask4POQAAAAC4n2mBfOXKlQoLC1NgYKAkKTw8XFarVevXr8933Jw5cxQYGKhFixapZ8+emj59ugzDKLTd7Oxspaam5nt4NZtNOnDA8focPeQx38Vo0EeDNPyz4eds9nDaYaXb0uXn46dGVRu5oVAAAAAAwJlMC+SHDx9W1apV822rWLGikpKS8m2Lj4/XNddco/HjxysuLk7PPPOM5syZU2i7sbGxCg0NdT1qu3EZMI+UkOBYh7xCBSkiotDDkjOSFbcxTpIUvy1e+07sK7JZ5wzrDas0lL+vv/vqBQAAAABIMjGQWywWV++4k81mk79//vC3fft2denSRRaLRQ0bNtTAgQP19ttvF9pudHS0UlJSXI+DBw+WSv0ew3n/eOPGUhFLk7207iVl5mRKkuyGXdN+mlZks67h6uEMVwcAAACA0mBaII+MjFRKSkq+benp6YqMjMy3LTc3V3l5ea73LVu21PHjxwtt12q1KiQkJN/DqxXj/vHU7FS9/MvLkqTH2j8mSZq/Zb6S0pIKPcc1oVs1JnQDAAAAgNJgWiDv2rWrDh06JJvNJkmuoert2rXLd1zLli21x9kLLMnPz08tWrS4eIV6umLMsB63IU6nsk6pWVgzPX/d8+pcp7NseTa9sPaFQs+hhxwAAAAASpdpgTwiIkK9e/fWqlWrJEnLly9XVFSUrFarYmJidOTIEUnS6NGj9eGHH7rOW7t2rUaOHGlKzR7pzCHrZ3E657QreI/rOE4+Fh/FdI6R5AjqxzPPPtqANcgBAAAAoHT5mfnhcXFxGjdunNatW6cTJ05o6tSpysrKUnx8vPr27auIiAjdeuutSkxM1JgxYxQeHq4uXbrommuuMbNsz+Icsl5ID/m8zfN0NOOo6oTW0Z2X3ylJ6tWwl66seaU2/blJs9fP1lPXPpXvnFNZp/Rn+p+SpKZhTUutdAAAAAAozyxGUWuIeYHU1FSFhoYqJSXF++4nP31aCg52vE5OlsLC8u3OyctR49mNlZiSqJf7vKyH2j3k2rfk9yUauHigqgRWUeKoRFWyVnLt+/nQz+rwZgddUukSHRp96KJ8KQAAAADgLYqbQ00bsg432LvX8VylilStWoHdC7ctVGJKoqpXqK77rrwv377+zfqrSbUmOpl1UnM25l9GzrnkWbMwJnQDAAAAgNJCIC/LiljyzG7YFbs6VpI0uv1oBfkH5dvv6+OrcR3HSZJeWPuCsnKzXPtcE7px/zgAAAAAlBoCeVlWxP3jn+z8RDuO7VCoNVQj2o446+mDWg5S7ZDaOpJ+RAs2L3Btd03oxgzrAAAAAFBqCORlWSEzrBuGoSmrp0iSHm73sEKsZ79nIcA3QGOvHitJeu6n55Rrz5V0xhrkDFkHAAAAgFJDIC/LCukh/3b/t9qQtEFBfkEaeVXRS8Td3/p+hQeHK+FUghZtW6Ts3GztP7lfEkPWAQAAAKA0EcjLskJ6yJ33jg/7zzCFVwgvsolg/2CNaj/Kdd7u47tlN+wKsYaoZsWabi8ZAAAAAOBAIC+rUlOlo0cdr88I5GsPrtX3B76Xv4+/xnQYU6ymotpGKcQaou3J2zVtzTRJjt5xy78migMAAAAAuA+BvKxy9o7XqCGdsa6ds3f87lZ3q3Zo7WI1VTmwsh5q61ij/N2t70piQjcAAAAAKG0E8rLqLPePbz26VZ/u/lQWWfR4x8fPq7lR7Ucp0C/Q9b5ZNSZ0AwAAAIDS5Gd2ASieY5nHtGDzAp3OPe3YsPt7qYuk1hnSD5MlSV/v+1qSNLDFQDWpVnAptKJUr1BdD1z5gF7+5WVJ9JADAAAAQGkjkJcRsT/G6oWfX8i/sZsk/Sp9/2u+zdGdokv0Gf+9+r+K2xinXHuuLqt+WckKBQAAAAAUC4G8jNj05yZJUo8GPdSgcgNp6cfSX8nSdT2levVdx7Wv1V5X1LyiRJ9Rt3JdfXzbxzqeeVwNqjRwR9kAAAAAgEJYDMMwzC6iNKWmpio0NFQpKSkKOWPys7ImckakjqQf0boH1qldZFupalXp1Clp61bp8svNLg8AAAAA8Lfi5lAmdSsDUrJSdCT9iCSpabWm0vHjjjAuSY0amVcYAAAAAKDECORlwI5jOyRJkZUiFRoY+s8M67VrS0FBJlYGAAAAACgpAnkZsPPYTklS87C/Zz53rkHe5PxmUgcAAAAAeA4CeRmwI9nRQ94s7O+1wZ095I0bm1QRAAAAAOBCEcjLgJ3H6SEHAAAAAG9DIC8D6CEHAAAAAO9DIPdw2bnZ2ndynySpeXhzyTDoIQcAAAAAL0Ag93B7T+yV3bArxBqiiIoRUlKSlJkp+fpK9eubXR4AAAAAoIQI5B7OueRZs7Bmslgs//SO168v+fubWBkAAAAA4EIQyD1cgSXPuH8cAAAAALwCgdzDndlDLon7xwEAAADASxDIPZxzhnV6yAEAAADAuxDIPZjdsGvX8V2S/p5hXaKHHAAAAAC8BIHcgx1MOajMnEz5+/irQZUGUl6etM+xBBo95AAAAABQthHIPZhzQrfG1RrLz8dP+uMPyWaTrFapdm2TqwMAAAAAXAgCuQcrMKGb8/7xhg0d65ADAAAAAMosArkHKzChG/ePAwAAAIDXIJB7sJ3HWYMcAAAAALwVgdyDOXvIWYMcAAAAALwPgdxDHc88ruTMZElS07Cmjo30kAMAAACA1yCQeyjnDOu1Q2qrYkBFx+zqBw44dtJDDgAAAABlHoHcQzlnWG8e/vf94/v3S3a7VLGiVLOmiZUBAAAAANyBQO6hnD3kBWZYb9RIslhMqgoAAAAA4C4Ecg9V6BrkDFcHAAAAAK9AIPdQBXrI9+93PDdqZFJFAAAAAAB3IpB7oNM5p5VwMkHSGT3kSUmO50suMakqAAAAAIA7Ecg90O7ju2XIUJXAKqpeobpj459/Op4jIswrDAAAAADgNgRyD+Qarh7eXBbnBG7OQM4M6wAAAADgFQjkHsg1oVu1v4erG4Z05IjjNYEcAAAAALwCgdwDndlDLklKSZGysx2vCeQAAAAA4BVMDeQZGRmKiopSdHS0Hn30UWU7Q+e/JCYmyt/fXxaLRRaLRb/++utFrvTiKrDkmXO4emioFBRkUlUAAAAAAHcyNZCPGDFCPXv2VGxsrNq0aaPo6OizHjd37lx9+umn+uabb7Ry5Uq1bt36Ild68eTZ87Tr2C5JZyx5xnB1AAAAAPA6pgXypKQkLV68WH369JEk9enTR3FxcUpLS8t33MmTJ7Vx40Zdeuml6tGjh6655poi283OzlZqamq+R1mSmJKo7LxsWX2tqle5nmMjM6wDAAAAgNcxLZCvXLlSYWFhCgwMlCSFh4fLarVq/fr1+Y5bsmSJfvjhB9WtW1eDBw9Wenp6ke3GxsYqNDTU9ahdu3apfQ2lYUeyY7h6k2pN5Ovj69jIDOsAAAAA4HVMC+SHDx9W1apV822rWLGikpKS8m0bOnSoUlJS9MUXX+jHH3/UvffeW2S70dHRSklJcT0OHjzo9tpLU4EJ3SSGrAMAAACAF/Iz64MtFourd9zJZrPJ39+/wLG+vr7q06ePli9frssvv1xJSUmKjIw8a7tWq1VWq7VUar4YCix5JjFkHQAAAAC8kGk95JGRkUpJScm3LT09vdCgLUlNmzZV9+7dy1yv9/lwBnJ6yAEAAADAu5kWyLt27apDhw7JZrNJkmuoert27Yo8r0KFCmrWrFmRx5RVhmG47iF3LXkm0UMOAAAAAF7ItEAeERGh3r17a9WqVZKk5cuXKyoqSlarVTExMTryd6/w+++/73q9Zs0ade7cWaGhoWaVXaqSM5N1MuukLLKoabWm/+xgUjcAAAAA8DqmrkMeFxenRYsWafLkydq6daueffZZZWVlKT4+XomJiZKkL7/8Updddpluu+027dq1SyNHjjSz5FLlnNCtXuV6CvIPcmy02aRjxxyvCeQAAAAA4DVMm9RNksLCwjR37twC2xMSElyv33nnnYtZkqnOOlz9r78cz35+UrVqJlQFAAAAACgNpvaQIz/XhG5hZ0zo5hyuXqOG5MOPCwAAAAC8BQnPgziHrOfrIWeGdQAAAADwSgRyD3LWJc+YYR0AAAAAvBKB3EOk29L1R8ofkgoZsk4POQAAAAB4FQK5h9h9fLckKSw4TNWCz5i8jSHrAAAAAOCVCOQewjnDer7ecYkh6wAAAADgpQjkHuKsE7pJDFkHAAAAAC9FIPcQZ13yTPpnyDo95AAAAADgVQjkHuKsM6wbBj3kAAAAAOClCOQeINeeqz3H90j615D1lBQpK8vxmkAOAAAAAF6FQO4B9p/crxx7joL8glQntM4/O5y946GhUlCQOcUBAAAAAEqFn9kFQKoWVE1z/2+uTmWdko/ljL+RMFwdAAAAALwWgdwDVAuupvtb319wB2uQAwAAAIDXYsi6J2MNcgAAAADwWgRyT8aQdQAAAADwWgRyT8aQdQAAAADwWgRyT8aQdQAAAADwWgRyT8aQdQAAAADwWgRyT8aQdQAAAADwWgRyT5WTIx075njNkHUAAAAA8DoEck919Kjj2c9PqlbN3FoAAAAAAG5HIPdUzvvHa9SQfPgxAQAAAIC3Iel5KiZ0AwAAAACvRiD3VEzoBgAAAABejUDuqViDHAAAAAC8GoHcUzFkHQAAAAC8GoHcUzFkHQAAAAC8GoHcUzFkHQAAAAC8GoHcUzFkHQAAAAC8GoHcExkGQ9YBAAAAwMsRyD1RaqqUleV4TSAHAAAAAK9EIPdEzuHqISFScLC5tQAAAAAASgWB3BM5h6szoRsAAAAAeC0CuSdiQjcAAAAA8HoEck/EhG4AAAAA4PUI5J6INcgBAAAAwOsRyD0RQ9YBAAAAwOsRyD0RQ9YBAAAAwOsRyD0RQ9YBAAAAwOsRyD0RQ9YBAAAAwOsRyD1NTo6UnOx4TSAHAAAAAK9FIPc0f/3lePb1lcLCzK0FAAAAAFBq/Mz88IyMDI0dO1ahoaHKyMjQ9OnTZbVaCz1+6tSp2rlzp+bPn3/xirzYnMPVa9SQfPh7CQAAAAB4K1MT34gRI9SzZ0/FxsaqTZs2io6OLvTYrVu36vXXX7+I1ZnEOcM6E7oBAAAAgFczLZAnJSVp8eLF6tOnjySpT58+iouLU1paWoFjbTab3njjDQ0ePPhil3nxMaEbAAAAAJQLpgXylStXKiwsTIGBgZKk8PBwWa1WrV+/vsCxzz//vMaMGSOfYgzhzs7OVmpqar5HmcIa5AAAAABQLpgWyA8fPqyqVavm21axYkUlJSXl27ZmzRrVqlVL9erVK1a7sbGxCg0NdT1q167trpIvDtYgBwAAAIBywbRAbrFYXL3jTjabTf7+/q73GRkZWrp0qe6+++5itxsdHa2UlBTX4+DBg26r+aJgyDoAAAAAlAumzbIeGRmplJSUfNvS09MVGRnpev/RRx8pLi5Ob731liQpMzNTdrtdW7du1a+//nrWdq1Wa5EztXs8hqwDAAAAQLlgWg95165ddejQIdlsNklyDVVv166d65ibb75Zv//+uzZv3qzNmzdr+PDh6tu3r7744gtTar4oGLIOAAAAAOWCaYE8IiJCvXv31qpVqyRJy5cvV1RUlKxWq2JiYnTkyBEFBwerVq1arkdISIiCg4NV01t7jw2DIesAAAAAUE6Yug55XFycFi1apMmTJ2vr1q169tlnlZWVpfj4eCUmJppZmjlSU6XTpx2vCeQAAAAA4NUshmEYZhdRmlJTUxUaGqqUlBSFhISYXU7Rdu2SmjWTQkKkf91fDwAAAAAoG4qbQ03tIce/MFwdAAAAAMoNArkncc6wzoRuAAAAAOD1COSehB5yAAAAACg3COSehEAOAAAAAOUGgdyTMGQdAAAAAMoNArknoYccAAAAAMoNArkncfaQE8gBAAAAwOsRyD2Js4ecIesAAAAA4PUI5J4iJ0c6dszxmh5yAAAAAPB6BHJP8ddfkmFIvr5SWJjZ1QAAAAAAShmB3FM4h6vXqCH58GMBAAAAAG9H8vMUzLAOAAAAAOUKgdxTMMM6AAAAAJQrBHJPwQzrAAAAAFCuEMg9BUPWAQAAAKBcIZB7CueQdXrIAQAAAKBcIJB7CnrIAQAAAKBcIZB7CiZ1AwAAAIByhUDuCQyDSd0AAAAAoJwhkHuCtDTp9GnH6xo1zK0FAAAAAHBREMg9gXO4eqVKUoUK5tYCAAAAALgo/MwuAJLq1JF++klKTTW7EgAAAADARUIg9wRBQdLVV5tdBQAAAADgImLIOgAAAAAAJiCQAwAAAABgAgI5AAAAAAAmIJADAAAAAGACAjkAAAAAACYgkAMAAAAAYAICOQAAAAAAJiCQAwAAAABgAgI5AAAAAAAmIJADAAAAAGACAjkAAAAAACYgkAMAAAAAYAICOQAAAAAAJiCQAwAAAABgAj+zCyhthmFIklJTU02uBAAAAABQHjjzpzOPFsbrA3laWpokqXbt2iZXAgAAAAAoT9LS0hQaGlrofotxrshextntdiUlJalSpUqyWCxml1Oo1NRU1a5dWwcPHlRISIjZ5QCF4lpFWcG1irKA6xRlBdcqygpPuVYNw1BaWpoiIyPl41P4neJe30Pu4+OjWrVqmV1GsYWEhPBLDmUC1yrKCq5VlAVcpygruFZRVnjCtVpUz7gTk7oBAAAAAGACAjkAAAAAACYgkHsIq9WqiRMnymq1ml0KUCSuVZQVXKsoC7hOUVZwraKsKGvXqtdP6gYAAAAAgCeihxwAAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwgZ/ZBUDKyMjQ2LFjFRoaqoyMDE2fPr3MzAoI7/bFF1/o0Ucf1YkTJzRo0CDNnDlTfn5+Onr0qCZMmKDKlSvL399fkydPlsViMbtcQDabTW3bttWLL76oa6+9lt+v8Fhr1qzR2rVr1bBhQ3Xu3FmBgYFcq/AoO3bs0Msvv6xGjRppz549GjZsmK644gp+r8IjfPvttxo/frwWLVqkevXqSSo6U3ny/13pIfcAI0aMUM+ePRUbG6s2bdooOjra7JIAHTt2TO+9957i4+M1e/ZszZs3T7NmzZIkDRw4UCNGjNC0adNktVo1e/Zsc4sF/jZ9+nQdOHDA9Z7fr/BEc+fO1eeff64xY8aoX79+qlatGtcqPM5dd92lmJgYPfbYYxo3bpzuuOMOSfxehfmSk5OVnp6u9evX59te1LXpyf93ZdkzkyUlJalhw4Y6efKkAgMDlZycrLp16+ro0aOqVKmS2eWhHPv555/VqlUrBQUFSZKeeOIJbdu2TRMmTNCtt96qP/74Q5L0yy+/aMCAAfrjjz885i+NKJ/WrFmjnTt36umnn9b8+fPVpEkTfr/C46xcuVLPPvusli9f7vqdyf8F4IkqVKigjRs3qlmzZkpOTlarVq20YcMGrlV4BLvdLl9fXyUkJKhevXpF/h7dvn27R//flR5yk61cuVJhYWEKDAyUJIWHh8tqtRb4iw9wsbVv394VxiXpkksuUa1atbRixQrVrVvXtb1JkyY6dOiQ9u/fb0aZgCTHMLXFixfrvvvuc23j9ys80ejRo9W8eXM98sgj6tOnj9auXcu1Co90yy236IEHHlBaWpreffddzZ49m2sVHsPHJ3+MLera9PT/uxLITXb48GFVrVo137aKFSsqKSnJpIqAs/vll1/04IMPFrhmK1asKElcszDVc889V2DYJL9f4Wl27dqlzZs3a+jQoXr55ZfVrVs39erVi2sVHumVV16Rv7+/2rZtq4oVK+rmm2/mWoXHKura9PT/uxLITWaxWFx/yXGy2Wzy9/c3qSKgoISEBFWpUkWtW7cucM3abDZJ4pqFab766iu1adNG1atXz7ed36/wNNu3b1fVqlV1+eWXS5Iefvhh2e12GYbBtQqPk5WVpUGDBunOO+/UqFGj9O233/J7FR6rqGvT0//vyizrJouMjFRKSkq+benp6YqMjDSpIiA/u92u1157TdOmTZPkuGb37t3r2p+WlubaDphhxowZ2rRpk+v9yZMnddNNN2nMmDH8foVHyc3NVV5enut9UFCQGjdurJycHK5VeJzBgwdr4cKFqly5siwWi+644w7NmjWLaxUeqahM5en/d6WH3GRdu3bVoUOHXH+pcQ6daNeunZllAS6zZs3SqFGjXH9Z7N69u/bs2ePav3fvXjVo0EB16tQxq0SUc++//742b97sekRGRmru3Lm65557+P0Kj9KyZUudOnVKx44dc23z8/NTrVq1uFbhUY4dO6YtW7aocuXKkqT//e9/CgkJUZ06dbhW4ZGKylSe/n9XArnJIiIi1Lt3b61atUqStHz5ckVFRRUYcgGY4YUXXlDTpk1ls9m0f/9+vfXWW6pWrZqqVKni+sW2fPlyjR492uRKUZ6Fh4erVq1aroevr6/Cw8NVt25dfr/CozRr1kx9+vTRkiVLJEmnTp1Sbm6uBg8ezLUKj1K1alUFBgbq8OHDrm3VqlVTq1atuFbhEZwLhTmfi8pUV111lUf/35VlzzzAsWPHNG7cONWrV08nTpzQ1KlTFRAQYHZZKOdeeukljRw5Mt+25s2b6/fff9e+ffs0ZcoU1alTR4ZhaOLEiR6xbAQgSfXq1dP8+fN17bXX8vsVHufYsWMaOXKk2rRpo4MHD2ro0KFq3rw51yo8zpYtW/Tqq6/qP//5j44ePaouXbrommuu4VqF6dLT0/XOO+8oKipKEydO1MMPP6ywsLAir01P/r8rgRwAAAAAABMwZB0AAAAAABMQyAEAAAAAMAGBHAAAAAAAExDIAQAAAAAwAYEcAAAAAAATEMgBAAAAADABgRwAAAAAABMQyAEAAAAAMAGBHAAAXJDc3Fy9/vrrqlu3rtmlAABQpviZXQAAAHC/DRs26Mknn9SPP/6o+++/X5JkGIbWrl2rO++8U6NGjXLbZ9ntdlWtWlV//PGH29oEAKA8IJADAOCF2rRpowEDBmjr1q2aNWuWa3t2drY++OADt35WQECAWrdu7dY2AQAoDxiyDgCAl/LzK/h3d6vVqoEDB7r9s3x8+C8FAADnix5yAADKkfnz5+vqq69WbGysrFaratSooZkzZ+qqq65SfHy8wsLCZBiGpk+froyMDG3btk3169fXtGnT5OPjI7vdrpkzZyo7O1vLly/XXXfd5RoSL0m//vqr7rnnHqWnp+v7779XvXr1zPtiAQDwcPw5GwAAL5aamqpx48Zp3Lhx6tu3r7777js1bNhQFSpU0Lp163TjjTdqy5Yt2rlzp8aNGydJmjNnjlJSUjRp0iQtXrxYy5cv14wZMyRJL7/8snx9fRUTE6PRo0froYceUl5enuvzDhw4oM2bN6tZs2Z66623TPmaAQAoKwjkAAB4sZCQEE2dOlVTp07Vxx9/rFatWsnX11dhYWFq1aqV2rZtq/r16+vhhx/WZ599Jkl65ZVX1KFDB0mOoehDhgzR66+/Lkl69dVX1aNHD0lS3759tXPnTvn6+ro+b8CAAfL19dV//vMfHTly5CJ/tQAAlC0EcgAAyglfX1/169fvrPtatGihlJQUSdKePXuUk5Pj2tegQQMdOnRIkpSYmKjs7GzXvsKGpPv5+Sk3N9c9hQMA4KUI5AAAlCONGjXSH3/8obS0tHzbbTabGjduLEmqU6eOdu7c6dpnGIaaNm0qSYqMjNRXX33l2peQkFBoT7hhGO4uHwAAr0IgBwDAS9nt9gKh2G63a9asWapUqVK+IL1y5UpFRUVJkoYPH6533nnH1cO9fv16jRgxQpJ0xx13aMqUKXrnnXf0ww8/aMaMGYqIiDhr+CaQAwBQNGZZBwDAC/3yyy+Kj4/Xn3/+qYceekhBQUHKy8vT2rVr1alTJ0lSUlKSYmNjJUmhoaEaOnSoJGnUqFE6dOiQ+vXrpyuvvFKhoaEaNmyYJOl///uf/vzzTz3yyCNq1aqVFixYoJycHNcEbnPnzlX37t31448/6siRI9q5c6eaNWtmwncAAADPZzH48zUAAOXOU089pQMHDmj+/PlmlwIAQLnFkHUAAMohwzAYUg4AgMkI5AAAlDNbtmzRN998o3Xr1mndunVmlwMAQLnFkHUAAAAAAExADzkAAAAAACYgkAMAAAAAYAICOQAAAAAAJiCQAwAAAABgAgI5AAAAAAAmIJADAAAAAGACAjkAAAAAACYgkAMAAAAAYIL/B2ZVqUSOlXszAAAAAElFTkSuQmCC\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 3: 1.0000\n",
|
||
"\n",
|
||
"Fold 4\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 67ms/step - accuracy: 0.2094 - loss: 2.2134 - val_accuracy: 0.2667 - val_loss: 1.9787 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.3303 - loss: 1.9239 - val_accuracy: 0.4333 - val_loss: 1.7484 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5574 - loss: 1.6246 - val_accuracy: 0.3667 - val_loss: 1.6172 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4120 - loss: 1.4848 - val_accuracy: 0.4000 - val_loss: 1.5210 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4721 - loss: 1.3831 - val_accuracy: 0.6667 - val_loss: 1.4348 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6378 - loss: 1.2744 - val_accuracy: 0.6000 - val_loss: 1.3517 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.5910 - loss: 1.2075 - val_accuracy: 0.8000 - val_loss: 1.2759 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6722 - loss: 1.1351 - val_accuracy: 0.8000 - val_loss: 1.2027 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6822 - loss: 1.0759 - val_accuracy: 0.8000 - val_loss: 1.1291 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7458 - loss: 1.0189 - val_accuracy: 0.8000 - val_loss: 1.0568 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8508 - loss: 0.9607 - val_accuracy: 0.8000 - val_loss: 0.9863 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9075 - loss: 0.9174 - val_accuracy: 0.8000 - val_loss: 0.9216 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9206 - loss: 0.8629 - val_accuracy: 0.8667 - val_loss: 0.8646 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9147 - loss: 0.8198 - val_accuracy: 0.8667 - val_loss: 0.8098 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9261 - loss: 0.7772 - val_accuracy: 0.8667 - val_loss: 0.7618 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9506 - loss: 0.7374 - val_accuracy: 0.9333 - val_loss: 0.7203 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9276 - loss: 0.7105 - val_accuracy: 0.9667 - val_loss: 0.6833 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9712 - loss: 0.6673 - val_accuracy: 0.9333 - val_loss: 0.6549 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9712 - loss: 0.6370 - val_accuracy: 0.9667 - val_loss: 0.6245 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9712 - loss: 0.6067 - val_accuracy: 0.8333 - val_loss: 0.6170 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.5994 - val_accuracy: 0.8333 - val_loss: 0.5874 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9621 - loss: 0.5802 - val_accuracy: 0.9667 - val_loss: 0.5922 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9636 - loss: 0.5715 - val_accuracy: 0.8333 - val_loss: 0.5512 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9866 - loss: 0.5147 - val_accuracy: 0.8333 - val_loss: 0.5282 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4962 - val_accuracy: 0.8333 - val_loss: 0.5219 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4829 - val_accuracy: 0.9667 - val_loss: 0.4926 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4599 - val_accuracy: 0.9667 - val_loss: 0.4844 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4461 - val_accuracy: 0.9667 - val_loss: 0.4709 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4302 - val_accuracy: 0.8333 - val_loss: 0.4774 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4238 - val_accuracy: 0.8333 - val_loss: 0.4852 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.4175 - val_accuracy: 0.9667 - val_loss: 0.4373 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3962 - val_accuracy: 1.0000 - val_loss: 0.4168 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3832 - val_accuracy: 1.0000 - val_loss: 0.4075 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3693 - val_accuracy: 0.9667 - val_loss: 0.4030 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3599 - val_accuracy: 1.0000 - val_loss: 0.3991 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3513 - val_accuracy: 1.0000 - val_loss: 0.3875 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3424 - val_accuracy: 1.0000 - val_loss: 0.3770 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9866 - loss: 0.3336 - val_accuracy: 1.0000 - val_loss: 0.3714 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.3258 - val_accuracy: 1.0000 - val_loss: 0.3644 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.3186 - val_accuracy: 1.0000 - val_loss: 0.3561 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.3109 - val_accuracy: 1.0000 - val_loss: 0.3510 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.3045 - val_accuracy: 1.0000 - val_loss: 0.3429 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.2977 - val_accuracy: 1.0000 - val_loss: 0.3383 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.2914 - val_accuracy: 1.0000 - val_loss: 0.3316 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2855 - val_accuracy: 1.0000 - val_loss: 0.3262 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2795 - val_accuracy: 1.0000 - val_loss: 0.3196 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.2744 - val_accuracy: 1.0000 - val_loss: 0.3175 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2686 - val_accuracy: 1.0000 - val_loss: 0.3092 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.2644 - val_accuracy: 1.0000 - val_loss: 0.3028 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2585 - val_accuracy: 1.0000 - val_loss: 0.3038 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.2545 - val_accuracy: 1.0000 - val_loss: 0.2940 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2504 - val_accuracy: 1.0000 - val_loss: 0.2883 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9909 - loss: 0.2447 - val_accuracy: 1.0000 - val_loss: 0.2889 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9909 - loss: 0.2414 - val_accuracy: 1.0000 - val_loss: 0.2819 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2379 - val_accuracy: 1.0000 - val_loss: 0.2735 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2325 - val_accuracy: 1.0000 - val_loss: 0.2752 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2295 - val_accuracy: 1.0000 - val_loss: 0.2698 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2272 - val_accuracy: 1.0000 - val_loss: 0.2632 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2211 - val_accuracy: 1.0000 - val_loss: 0.2622 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2189 - val_accuracy: 1.0000 - val_loss: 0.2586 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 1.0000 - loss: 0.2157 - val_accuracy: 1.0000 - val_loss: 0.2533 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.2118 - val_accuracy: 1.0000 - val_loss: 0.2504 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.2092 - val_accuracy: 1.0000 - val_loss: 0.2465 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.2058 - val_accuracy: 1.0000 - val_loss: 0.2431 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2029 - val_accuracy: 1.0000 - val_loss: 0.2401 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.2000 - val_accuracy: 1.0000 - val_loss: 0.2371 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1974 - val_accuracy: 1.0000 - val_loss: 0.2338 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1945 - val_accuracy: 1.0000 - val_loss: 0.2310 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1919 - val_accuracy: 1.0000 - val_loss: 0.2280 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1895 - val_accuracy: 1.0000 - val_loss: 0.2249 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1868 - val_accuracy: 1.0000 - val_loss: 0.2219 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1844 - val_accuracy: 1.0000 - val_loss: 0.2196 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1822 - val_accuracy: 1.0000 - val_loss: 0.2171 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1798 - val_accuracy: 1.0000 - val_loss: 0.2137 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1775 - val_accuracy: 1.0000 - val_loss: 0.2117 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1753 - val_accuracy: 1.0000 - val_loss: 0.2087 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1732 - val_accuracy: 1.0000 - val_loss: 0.2069 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1712 - val_accuracy: 1.0000 - val_loss: 0.2044 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1692 - val_accuracy: 1.0000 - val_loss: 0.2021 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1671 - val_accuracy: 1.0000 - val_loss: 0.1993 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 0.1652 - val_accuracy: 1.0000 - val_loss: 0.1976 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1633 - val_accuracy: 1.0000 - val_loss: 0.1953 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1616 - val_accuracy: 1.0000 - val_loss: 0.1933 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1596 - val_accuracy: 1.0000 - val_loss: 0.1911 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1580 - val_accuracy: 1.0000 - val_loss: 0.1891 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1562 - val_accuracy: 1.0000 - val_loss: 0.1872 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1545 - val_accuracy: 1.0000 - val_loss: 0.1855 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1529 - val_accuracy: 1.0000 - val_loss: 0.1834 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1513 - val_accuracy: 1.0000 - val_loss: 0.1812 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1497 - val_accuracy: 1.0000 - val_loss: 0.1799 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1482 - val_accuracy: 1.0000 - val_loss: 0.1777 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1466 - val_accuracy: 1.0000 - val_loss: 0.1764 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1452 - val_accuracy: 1.0000 - val_loss: 0.1744 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1438 - val_accuracy: 1.0000 - val_loss: 0.1730 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1424 - val_accuracy: 1.0000 - val_loss: 0.1716 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1410 - val_accuracy: 1.0000 - val_loss: 0.1692 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1396 - val_accuracy: 1.0000 - val_loss: 0.1678 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1381 - val_accuracy: 1.0000 - val_loss: 0.1661 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1370 - val_accuracy: 1.0000 - val_loss: 0.1651 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1357 - val_accuracy: 1.0000 - val_loss: 0.1640 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 4: 1.0000\n",
|
||
"\n",
|
||
"Fold 5\n",
|
||
"Epoch 1/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 62ms/step - accuracy: 0.1677 - loss: 2.1732 - val_accuracy: 0.2333 - val_loss: 1.8945 - learning_rate: 0.0010\n",
|
||
"Epoch 2/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.2848 - loss: 1.8781 - val_accuracy: 0.3333 - val_loss: 1.6708 - learning_rate: 0.0010\n",
|
||
"Epoch 3/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3994 - loss: 1.6545 - val_accuracy: 0.3333 - val_loss: 1.5049 - learning_rate: 0.0010\n",
|
||
"Epoch 4/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4238 - loss: 1.5057 - val_accuracy: 0.4667 - val_loss: 1.3763 - learning_rate: 0.0010\n",
|
||
"Epoch 5/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5301 - loss: 1.3900 - val_accuracy: 0.6000 - val_loss: 1.2741 - learning_rate: 0.0010\n",
|
||
"Epoch 6/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6468 - loss: 1.2928 - val_accuracy: 0.6333 - val_loss: 1.1863 - learning_rate: 0.0010\n",
|
||
"Epoch 7/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6787 - loss: 1.2099 - val_accuracy: 0.6333 - val_loss: 1.1121 - learning_rate: 0.0010\n",
|
||
"Epoch 8/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6787 - loss: 1.1343 - val_accuracy: 0.6333 - val_loss: 1.0436 - learning_rate: 0.0010\n",
|
||
"Epoch 9/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6787 - loss: 1.0665 - val_accuracy: 0.6333 - val_loss: 0.9801 - learning_rate: 0.0010\n",
|
||
"Epoch 10/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7110 - loss: 1.0064 - val_accuracy: 0.7333 - val_loss: 0.9207 - learning_rate: 0.0010\n",
|
||
"Epoch 11/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7314 - loss: 0.9464 - val_accuracy: 0.8000 - val_loss: 0.8666 - learning_rate: 0.0010\n",
|
||
"Epoch 12/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7970 - loss: 0.8870 - val_accuracy: 0.8333 - val_loss: 0.8117 - learning_rate: 0.0010\n",
|
||
"Epoch 13/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8658 - loss: 0.8293 - val_accuracy: 0.8667 - val_loss: 0.7664 - learning_rate: 0.0010\n",
|
||
"Epoch 14/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8772 - loss: 0.7833 - val_accuracy: 0.8667 - val_loss: 0.7188 - learning_rate: 0.0010\n",
|
||
"Epoch 15/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8953 - loss: 0.7322 - val_accuracy: 0.9000 - val_loss: 0.6799 - learning_rate: 0.0010\n",
|
||
"Epoch 16/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9182 - loss: 0.6923 - val_accuracy: 0.9000 - val_loss: 0.6365 - learning_rate: 0.0010\n",
|
||
"Epoch 17/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9226 - loss: 0.6476 - val_accuracy: 0.9333 - val_loss: 0.6055 - learning_rate: 0.0010\n",
|
||
"Epoch 18/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9412 - loss: 0.6150 - val_accuracy: 0.9333 - val_loss: 0.5686 - learning_rate: 0.0010\n",
|
||
"Epoch 19/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9642 - loss: 0.5759 - val_accuracy: 0.9667 - val_loss: 0.5402 - learning_rate: 0.0010\n",
|
||
"Epoch 20/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9642 - loss: 0.5476 - val_accuracy: 0.9667 - val_loss: 0.5120 - learning_rate: 0.0010\n",
|
||
"Epoch 21/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9642 - loss: 0.5182 - val_accuracy: 0.9667 - val_loss: 0.4903 - learning_rate: 0.0010\n",
|
||
"Epoch 22/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9642 - loss: 0.4923 - val_accuracy: 0.9667 - val_loss: 0.4661 - learning_rate: 0.0010\n",
|
||
"Epoch 23/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.4675 - val_accuracy: 1.0000 - val_loss: 0.4438 - learning_rate: 0.0010\n",
|
||
"Epoch 24/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.4533 - val_accuracy: 0.9667 - val_loss: 0.4250 - learning_rate: 0.0010\n",
|
||
"Epoch 25/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9872 - loss: 0.4255 - val_accuracy: 0.9667 - val_loss: 0.4064 - learning_rate: 0.0010\n",
|
||
"Epoch 26/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.4024 - val_accuracy: 1.0000 - val_loss: 0.3904 - learning_rate: 0.0010\n",
|
||
"Epoch 27/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.3932 - val_accuracy: 0.9667 - val_loss: 0.3767 - learning_rate: 0.0010\n",
|
||
"Epoch 28/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.3738 - val_accuracy: 1.0000 - val_loss: 0.3637 - learning_rate: 0.0010\n",
|
||
"Epoch 29/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.3635 - val_accuracy: 1.0000 - val_loss: 0.3500 - learning_rate: 0.0010\n",
|
||
"Epoch 30/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.3497 - val_accuracy: 1.0000 - val_loss: 0.3406 - learning_rate: 0.0010\n",
|
||
"Epoch 31/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.3409 - val_accuracy: 1.0000 - val_loss: 0.3312 - learning_rate: 0.0010\n",
|
||
"Epoch 32/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.3307 - val_accuracy: 1.0000 - val_loss: 0.3214 - learning_rate: 0.0010\n",
|
||
"Epoch 33/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.3286 - val_accuracy: 1.0000 - val_loss: 0.3207 - learning_rate: 0.0010\n",
|
||
"Epoch 34/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.3265 - val_accuracy: 1.0000 - val_loss: 0.3042 - learning_rate: 0.0010\n",
|
||
"Epoch 35/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9872 - loss: 0.3019 - val_accuracy: 1.0000 - val_loss: 0.2983 - learning_rate: 0.0010\n",
|
||
"Epoch 36/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.2978 - val_accuracy: 1.0000 - val_loss: 0.2847 - learning_rate: 0.0010\n",
|
||
"Epoch 37/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2886 - val_accuracy: 1.0000 - val_loss: 0.2819 - learning_rate: 0.0010\n",
|
||
"Epoch 38/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2825 - val_accuracy: 1.0000 - val_loss: 0.2733 - learning_rate: 0.0010\n",
|
||
"Epoch 39/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2748 - val_accuracy: 1.0000 - val_loss: 0.2660 - learning_rate: 0.0010\n",
|
||
"Epoch 40/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2702 - val_accuracy: 1.0000 - val_loss: 0.2619 - learning_rate: 0.0010\n",
|
||
"Epoch 41/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2635 - val_accuracy: 1.0000 - val_loss: 0.2545 - learning_rate: 0.0010\n",
|
||
"Epoch 42/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2579 - val_accuracy: 1.0000 - val_loss: 0.2491 - learning_rate: 0.0010\n",
|
||
"Epoch 43/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9944 - loss: 0.2525 - val_accuracy: 1.0000 - val_loss: 0.2442 - learning_rate: 0.0010\n",
|
||
"Epoch 44/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9944 - loss: 0.2473 - val_accuracy: 1.0000 - val_loss: 0.2381 - learning_rate: 0.0010\n",
|
||
"Epoch 45/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2421 - val_accuracy: 1.0000 - val_loss: 0.2340 - learning_rate: 0.0010\n",
|
||
"Epoch 46/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2381 - val_accuracy: 1.0000 - val_loss: 0.2290 - learning_rate: 0.0010\n",
|
||
"Epoch 47/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2327 - val_accuracy: 1.0000 - val_loss: 0.2240 - learning_rate: 0.0010\n",
|
||
"Epoch 48/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2293 - val_accuracy: 1.0000 - val_loss: 0.2203 - learning_rate: 0.0010\n",
|
||
"Epoch 49/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.2243 - val_accuracy: 1.0000 - val_loss: 0.2162 - learning_rate: 0.0010\n",
|
||
"Epoch 50/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2207 - val_accuracy: 1.0000 - val_loss: 0.2116 - learning_rate: 0.0010\n",
|
||
"Epoch 51/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2168 - val_accuracy: 1.0000 - val_loss: 0.2092 - learning_rate: 0.0010\n",
|
||
"Epoch 52/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2142 - val_accuracy: 1.0000 - val_loss: 0.2041 - learning_rate: 0.0010\n",
|
||
"Epoch 53/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2087 - val_accuracy: 1.0000 - val_loss: 0.2019 - learning_rate: 0.0010\n",
|
||
"Epoch 54/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2074 - val_accuracy: 1.0000 - val_loss: 0.1976 - learning_rate: 0.0010\n",
|
||
"Epoch 55/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.2025 - val_accuracy: 1.0000 - val_loss: 0.1949 - learning_rate: 0.0010\n",
|
||
"Epoch 56/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.2003 - val_accuracy: 1.0000 - val_loss: 0.1914 - learning_rate: 0.0010\n",
|
||
"Epoch 57/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1966 - val_accuracy: 1.0000 - val_loss: 0.1884 - learning_rate: 0.0010\n",
|
||
"Epoch 58/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1942 - val_accuracy: 1.0000 - val_loss: 0.1857 - learning_rate: 0.0010\n",
|
||
"Epoch 59/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1910 - val_accuracy: 1.0000 - val_loss: 0.1827 - learning_rate: 0.0010\n",
|
||
"Epoch 60/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1880 - val_accuracy: 1.0000 - val_loss: 0.1801 - learning_rate: 0.0010\n",
|
||
"Epoch 61/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1857 - val_accuracy: 1.0000 - val_loss: 0.1767 - learning_rate: 0.0010\n",
|
||
"Epoch 62/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1827 - val_accuracy: 1.0000 - val_loss: 0.1752 - learning_rate: 0.0010\n",
|
||
"Epoch 63/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1804 - val_accuracy: 1.0000 - val_loss: 0.1722 - learning_rate: 0.0010\n",
|
||
"Epoch 64/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1778 - val_accuracy: 1.0000 - val_loss: 0.1697 - learning_rate: 0.0010\n",
|
||
"Epoch 65/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1756 - val_accuracy: 1.0000 - val_loss: 0.1677 - learning_rate: 0.0010\n",
|
||
"Epoch 66/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1731 - val_accuracy: 1.0000 - val_loss: 0.1650 - learning_rate: 0.0010\n",
|
||
"Epoch 67/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1704 - val_accuracy: 1.0000 - val_loss: 0.1627 - learning_rate: 0.0010\n",
|
||
"Epoch 68/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1686 - val_accuracy: 1.0000 - val_loss: 0.1611 - learning_rate: 0.0010\n",
|
||
"Epoch 69/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1669 - val_accuracy: 1.0000 - val_loss: 0.1583 - learning_rate: 0.0010\n",
|
||
"Epoch 70/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1641 - val_accuracy: 1.0000 - val_loss: 0.1565 - learning_rate: 0.0010\n",
|
||
"Epoch 71/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1622 - val_accuracy: 1.0000 - val_loss: 0.1546 - learning_rate: 0.0010\n",
|
||
"Epoch 72/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1605 - val_accuracy: 1.0000 - val_loss: 0.1526 - learning_rate: 0.0010\n",
|
||
"Epoch 73/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1584 - val_accuracy: 1.0000 - val_loss: 0.1507 - learning_rate: 0.0010\n",
|
||
"Epoch 74/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1564 - val_accuracy: 1.0000 - val_loss: 0.1490 - learning_rate: 0.0010\n",
|
||
"Epoch 75/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1551 - val_accuracy: 1.0000 - val_loss: 0.1471 - learning_rate: 0.0010\n",
|
||
"Epoch 76/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1529 - val_accuracy: 1.0000 - val_loss: 0.1456 - learning_rate: 0.0010\n",
|
||
"Epoch 77/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1512 - val_accuracy: 1.0000 - val_loss: 0.1431 - learning_rate: 0.0010\n",
|
||
"Epoch 78/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1489 - val_accuracy: 1.0000 - val_loss: 0.1424 - learning_rate: 0.0010\n",
|
||
"Epoch 79/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1481 - val_accuracy: 1.0000 - val_loss: 0.1399 - learning_rate: 0.0010\n",
|
||
"Epoch 80/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1457 - val_accuracy: 1.0000 - val_loss: 0.1389 - learning_rate: 0.0010\n",
|
||
"Epoch 81/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1449 - val_accuracy: 1.0000 - val_loss: 0.1373 - learning_rate: 0.0010\n",
|
||
"Epoch 82/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1429 - val_accuracy: 1.0000 - val_loss: 0.1354 - learning_rate: 0.0010\n",
|
||
"Epoch 83/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1417 - val_accuracy: 1.0000 - val_loss: 0.1341 - learning_rate: 0.0010\n",
|
||
"Epoch 84/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1399 - val_accuracy: 1.0000 - val_loss: 0.1330 - learning_rate: 0.0010\n",
|
||
"Epoch 85/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1389 - val_accuracy: 1.0000 - val_loss: 0.1312 - learning_rate: 0.0010\n",
|
||
"Epoch 86/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1370 - val_accuracy: 1.0000 - val_loss: 0.1300 - learning_rate: 0.0010\n",
|
||
"Epoch 87/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1360 - val_accuracy: 1.0000 - val_loss: 0.1285 - learning_rate: 0.0010\n",
|
||
"Epoch 88/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1342 - val_accuracy: 1.0000 - val_loss: 0.1271 - learning_rate: 0.0010\n",
|
||
"Epoch 89/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1331 - val_accuracy: 1.0000 - val_loss: 0.1259 - learning_rate: 0.0010\n",
|
||
"Epoch 90/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1315 - val_accuracy: 1.0000 - val_loss: 0.1248 - learning_rate: 0.0010\n",
|
||
"Epoch 91/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1306 - val_accuracy: 1.0000 - val_loss: 0.1230 - learning_rate: 0.0010\n",
|
||
"Epoch 92/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1286 - val_accuracy: 1.0000 - val_loss: 0.1222 - learning_rate: 0.0010\n",
|
||
"Epoch 93/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 1.0000 - loss: 0.1281 - val_accuracy: 1.0000 - val_loss: 0.1206 - learning_rate: 0.0010\n",
|
||
"Epoch 94/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1261 - val_accuracy: 1.0000 - val_loss: 0.1200 - learning_rate: 0.0010\n",
|
||
"Epoch 95/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 1.0000 - loss: 0.1262 - val_accuracy: 1.0000 - val_loss: 0.1184 - learning_rate: 0.0010\n",
|
||
"Epoch 96/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1241 - val_accuracy: 1.0000 - val_loss: 0.1173 - learning_rate: 0.0010\n",
|
||
"Epoch 97/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1234 - val_accuracy: 1.0000 - val_loss: 0.1165 - learning_rate: 0.0010\n",
|
||
"Epoch 98/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1221 - val_accuracy: 1.0000 - val_loss: 0.1148 - learning_rate: 0.0010\n",
|
||
"Epoch 99/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1205 - val_accuracy: 1.0000 - val_loss: 0.1144 - learning_rate: 0.0010\n",
|
||
"Epoch 100/100\n",
|
||
"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 1.0000 - loss: 0.1202 - val_accuracy: 1.0000 - val_loss: 0.1127 - learning_rate: 0.0010\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Accuracy Fold 5: 1.0000\n",
|
||
"\n",
|
||
"Average Accuracy: 1.0000\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Visualisasi akurasi tiap fold\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.plot(range(1, len(accuracies) + 1), accuracies, marker='o', linestyle='-', color='blue', label='Fold Accuracy')\n",
|
||
"plt.axhline(np.mean(accuracies), color='red', linestyle='--', label=f'Average Accuracy: {np.mean(accuracies):.4f}')\n",
|
||
"plt.title(\"Validation Accuracy per Fold\")\n",
|
||
"plt.xlabel(\"Fold\")\n",
|
||
"plt.ylabel(\"Accuracy\")\n",
|
||
"plt.xticks(range(1, len(accuracies) + 1))\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"id": "YloYpnMlR2Cj",
|
||
"outputId": "7444ee22-0484-4ebe-91fb-07ad73e638e3",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 485
|
||
}
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Evaluation and Plotting"
|
||
],
|
||
"metadata": {
|
||
"id": "LcgPa-emwaWe"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"plt.rcParams['font.serif'] = \"Times New Roman\"\n",
|
||
"plt.rcParams['font.family'] = \"serif\"\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 5))\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"# Plot untuk accuracy dan val_accuracy\n",
|
||
"plt.title(\"Accuracy and Val Accuracy\")\n",
|
||
"plt.plot(hist_early.history['accuracy'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
"plt.plot(hist_early.history['val_accuracy'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
"plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Accuracy')\n",
|
||
"\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"# Plot untuk loss dan val_loss\n",
|
||
"plt.title(\"Loss and Val Loss\")\n",
|
||
"plt.plot(hist_early.history['loss'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
"plt.plot(hist_early.history['val_loss'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
"plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Loss')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 5))\n",
|
||
"plt.subplot(1, 2, 1)\n",
|
||
"# Plot untuk accuracy dan val_accuracy\n",
|
||
"plt.title(\"Accuracy and Val Accuracy\")\n",
|
||
"plt.plot(hist.history['accuracy'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
"plt.plot(hist.history['val_accuracy'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
"plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Accuracy')\n",
|
||
"\n",
|
||
"plt.subplot(1, 2, 2)\n",
|
||
"# Plot untuk loss dan val_loss\n",
|
||
"plt.title(\"Loss and Val Loss\")\n",
|
||
"plt.plot(hist.history['loss'], label='Train', color='red') # Gunakan label, bukan labels\n",
|
||
"plt.plot(hist.history['val_loss'], label='Val', color='green') # Gunakan label, bukan labels\n",
|
||
"plt.legend() # Panggil legend setelah semua plot ditambahkan\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Loss')\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 931
|
||
},
|
||
"id": "QisxQIC9wdVG",
|
||
"outputId": "ef980c2b-234c-4be3-ec1b-f6577cbe960d"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
|
||
"\n",
|
||
"# Konversi tf.data.Dataset ke array NumPy\n",
|
||
"X_test_array = np.array(list(X_test.as_numpy_iterator()))\n",
|
||
"y_test_array = np.array(list(y_test.as_numpy_iterator()))\n",
|
||
"\n",
|
||
"# Lakukan prediksi Menggunakan Model Awal\n",
|
||
"print(\"Model Awal\")\n",
|
||
"y_pred = early_model.predict(X_test_array)\n",
|
||
"y_pred = np.argmax(y_pred, axis=1)\n",
|
||
"y_pred = encoder.inverse_transform(y_pred)\n",
|
||
"y_actual = encoder.inverse_transform(y_test_array)\n",
|
||
"print(classification_report(y_actual, y_pred))\n",
|
||
"disp = ConfusionMatrixDisplay(confusion_matrix(y_actual, y_pred), display_labels=classes)\n",
|
||
"disp.plot(cmap=plt.cm.Blues)\n",
|
||
"plt.title(\"Model Awal\")\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Lakukan prediksi Menggunakan Model yang sudah dihyperparameter tune\n",
|
||
"print(\"Model Setelah dilakukan hyperparameter tune\")\n",
|
||
"y_pred = model.predict(X_test_array)\n",
|
||
"y_pred = np.argmax(y_pred, axis=1)\n",
|
||
"y_pred = encoder.inverse_transform(y_pred)\n",
|
||
"y_actual = encoder.inverse_transform(y_test_array)\n",
|
||
"print(classification_report(y_actual, y_pred))\n",
|
||
"disp = ConfusionMatrixDisplay(confusion_matrix(y_actual, y_pred), display_labels=classes)\n",
|
||
"disp.plot(cmap=plt.cm.Blues)\n",
|
||
"plt.title(\"Model Setelah dilakukan hyperparameter tune\")\n",
|
||
"plt.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "tAfS-Rmx0e3W",
|
||
"outputId": "729bb971-88a6-4e2c-d6cd-6fc87d8c9448"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Model Awal\n",
|
||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 366ms/step\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" Bitter 0.75 1.00 0.86 6\n",
|
||
" Ideal 0.67 1.00 0.80 6\n",
|
||
" Strong 1.00 1.00 1.00 6\n",
|
||
"Underdeveloped 1.00 0.33 0.50 6\n",
|
||
" Weak 0.20 0.17 0.18 6\n",
|
||
"\n",
|
||
" accuracy 0.70 30\n",
|
||
" macro avg 0.72 0.70 0.67 30\n",
|
||
" weighted avg 0.72 0.70 0.67 30\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Model Setelah dilakukan hyperparameter tune\n",
|
||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310ms/step\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" Bitter 1.00 1.00 1.00 6\n",
|
||
" Ideal 1.00 1.00 1.00 6\n",
|
||
" Strong 1.00 1.00 1.00 6\n",
|
||
"Underdeveloped 1.00 1.00 1.00 6\n",
|
||
" Weak 1.00 1.00 1.00 6\n",
|
||
"\n",
|
||
" accuracy 1.00 30\n",
|
||
" macro avg 1.00 1.00 1.00 30\n",
|
||
" weighted avg 1.00 1.00 1.00 30\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
],
|
||
"image/png": 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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Save Model"
|
||
],
|
||
"metadata": {
|
||
"id": "I8hjO-wg0Ztt"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# h5 model\n",
|
||
"model.save(\"coffee_random_stratified.h5\")\n",
|
||
"\n",
|
||
"\n",
|
||
"# tflite model\n",
|
||
"converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
|
||
"tflite_model = converter.convert()\n",
|
||
"\n",
|
||
"# Save the model.\n",
|
||
"with open('coffee_random_stratified.tflite', 'wb') as f:\n",
|
||
" f.write(tflite_model)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ZIpZThdV1PoP",
|
||
"outputId": "b119bc30-ea5f-41ac-e8be-0dbce8ddda4c"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Saved artifact at '/tmp/tmpcc6eu5ee'. The following endpoints are available:\n",
|
||
"\n",
|
||
"* Endpoint 'serve'\n",
|
||
" args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 18, 1), dtype=tf.float32, name='keras_tensor_16')\n",
|
||
"Output Type:\n",
|
||
" TensorSpec(shape=(None, 5), dtype=tf.float32, name=None)\n",
|
||
"Captures:\n",
|
||
" 137608608138064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
||
" 137608608138256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
||
" 137608608140176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
||
" 137608608142096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
||
" 137605391604752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n",
|
||
" 137605391602832: TensorSpec(shape=(), dtype=tf.resource, name=None)\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"# Load Model"
|
||
],
|
||
"metadata": {
|
||
"id": "7dNUjIJH1j4a"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from tensorflow.keras.models import load_model\n",
|
||
"from tensorflow.keras.utils import plot_model\n",
|
||
"\n",
|
||
"\n",
|
||
"# Load the model from a file (update the filename as needed)\n",
|
||
"model = load_model(\"coffee_random.h5\") # or \"your_model_directory\" if using SavedModel format\n",
|
||
"# Plot and save the model architecture\n",
|
||
"plot_model(model, to_file=\"model_plot.png\", show_shapes=True, show_layer_names=True, show_layer_activations=True)\n",
|
||
"\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "4YjX3XY71lF-",
|
||
"outputId": "0799f719-9e17-435c-e3fa-c74d9cb597fd"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<IPython.core.display.Image object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 44
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"X = sample.iloc[:, :-2].values\n",
|
||
"y = sample['Label'].values\n",
|
||
"X.shape, y.shape"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "jhWX1FLV_-JZ",
|
||
"outputId": "2e6fb340-6870-4dc5-9799-9a0247bcde98"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"((20, 18), (20,))"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 45
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"classes = ['Bitter','Ideal','Strong','Underdeveloped','Weak']\n",
|
||
"y_pred = model.predict(X)\n",
|
||
"y_pred = np.argmax(y_pred, axis=1)\n",
|
||
"y_pred = np.array([classes[i] for i in y_pred])\n",
|
||
"print(classification_report(y_pred, y))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "jPknxPU3APjh",
|
||
"outputId": "03e95b36-6960-4c3d-fafc-160d731cadcd"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7edbe2aea520> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" Bitter 1.00 1.00 1.00 3\n",
|
||
" Ideal 1.00 1.00 1.00 5\n",
|
||
" Strong 1.00 1.00 1.00 3\n",
|
||
"Underdeveloped 1.00 1.00 1.00 5\n",
|
||
" Weak 1.00 1.00 1.00 4\n",
|
||
"\n",
|
||
" accuracy 1.00 20\n",
|
||
" macro avg 1.00 1.00 1.00 20\n",
|
||
" weighted avg 1.00 1.00 1.00 20\n",
|
||
"\n"
|
||
]
|
||
}
|
||
]
|
||
}
|
||
]
|
||
} |