730 lines
252 KiB
Plaintext
730 lines
252 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "73731ea4",
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"metadata": {},
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"source": [
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"# Practical-2a (Classification using Deep Neural Network - OCR Letter Recognition)\n",
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"\n",
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"---\n",
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"\n",
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"Problem Statement: Multiclass classification using Deep Neural Networks: Example: Use the OCR letter recognition dataset.\n",
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"\n",
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"- Dataset link: https://archive.ics.uci.edu/dataset/59/letter+recognition\n",
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"- Dataset available in [Datasets](https://git.kska.io/sppu-be-comp-content/DeepLearning/src/branch/main/Datasets/) directory.\n",
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"\n",
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"---"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3e8607a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1. Import Libraries\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
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"from sklearn.metrics import confusion_matrix, classification_report\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Input, Dense, Dropout\n",
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"from tensorflow.keras.utils import to_categorical"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "50208e29",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Shape: (20000, 17)\n",
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" letter x-box y-box width high onpix x-bar y-bar x2bar y2bar xybar \\\n",
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"0 T 2 8 3 5 1 8 13 0 6 6 \n",
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"1 I 5 12 3 7 2 10 5 5 4 13 \n",
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"2 D 4 11 6 8 6 10 6 2 6 10 \n",
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"3 N 7 11 6 6 3 5 9 4 6 4 \n",
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"4 G 2 1 3 1 1 8 6 6 6 6 \n",
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"\n",
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" x2ybr xy2br x-ege xegvy y-ege yegvx \n",
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"0 10 8 0 8 0 8 \n",
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"1 3 9 2 8 4 10 \n",
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"2 3 7 3 7 3 9 \n",
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"3 4 10 6 10 2 8 \n",
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"4 5 9 1 7 5 10 \n"
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]
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}
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],
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"source": [
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"# 2. Load Dataset\n",
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"# Dataset has no header row — define column names manually based on UCI documentation\n",
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"col_names = ['letter', 'x-box', 'y-box', 'width', 'high', 'onpix',\n",
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" 'x-bar', 'y-bar', 'x2bar', 'y2bar', 'xybar',\n",
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" 'x2ybr', 'xy2br', 'x-ege', 'xegvy', 'y-ege', 'yegvx']\n",
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"\n",
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"data = pd.read_csv('./letter+recognition/letter-recognition.data', header=None, names=col_names)\n",
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"print(\"Shape:\", data.shape)\n",
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"print(data.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "6cd7bd4e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data Types:\n",
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" letter str\n",
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"x-box int64\n",
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"y-box int64\n",
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"width int64\n",
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"high int64\n",
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"onpix int64\n",
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"x-bar int64\n",
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"y-bar int64\n",
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"x2bar int64\n",
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"y2bar int64\n",
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"xybar int64\n",
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"x2ybr int64\n",
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"xy2br int64\n",
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"x-ege int64\n",
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"xegvy int64\n",
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"y-ege int64\n",
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"yegvx int64\n",
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"dtype: object\n",
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"\n",
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"Missing Values:\n",
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" letter 0\n",
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"x-box 0\n",
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"y-box 0\n",
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"width 0\n",
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"high 0\n",
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"onpix 0\n",
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"x-bar 0\n",
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"y-bar 0\n",
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"x2bar 0\n",
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"y2bar 0\n",
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"xybar 0\n",
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"x2ybr 0\n",
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"xy2br 0\n",
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"x-ege 0\n",
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"xegvy 0\n",
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"y-ege 0\n",
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"yegvx 0\n",
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"dtype: int64\n",
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"\n",
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"Statistical Summary:\n",
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" x-box y-box width high onpix \\\n",
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"count 20000.000000 20000.000000 20000.000000 20000.00000 20000.000000 \n",
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"mean 4.023550 7.035500 5.121850 5.37245 3.505850 \n",
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"std 1.913212 3.304555 2.014573 2.26139 2.190458 \n",
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"min 0.000000 0.000000 0.000000 0.00000 0.000000 \n",
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"25% 3.000000 5.000000 4.000000 4.00000 2.000000 \n",
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"50% 4.000000 7.000000 5.000000 6.00000 3.000000 \n",
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"75% 5.000000 9.000000 6.000000 7.00000 5.000000 \n",
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"max 15.000000 15.000000 15.000000 15.00000 15.000000 \n",
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"\n",
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" x-bar y-bar x2bar y2bar xybar \\\n",
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"count 20000.000000 20000.000000 20000.000000 20000.000000 20000.000000 \n",
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"mean 6.897600 7.500450 4.628600 5.178650 8.282050 \n",
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"std 2.026035 2.325354 2.699968 2.380823 2.488475 \n",
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"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
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"25% 6.000000 6.000000 3.000000 4.000000 7.000000 \n",
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"50% 7.000000 7.000000 4.000000 5.000000 8.000000 \n",
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"75% 8.000000 9.000000 6.000000 7.000000 10.000000 \n",
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"max 15.000000 15.000000 15.000000 15.000000 15.000000 \n",
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"\n",
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" x2ybr xy2br x-ege xegvy y-ege \\\n",
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"count 20000.00000 20000.000000 20000.000000 20000.000000 20000.000000 \n",
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"mean 6.45400 7.929000 3.046100 8.338850 3.691750 \n",
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"std 2.63107 2.080619 2.332541 1.546722 2.567073 \n",
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"min 0.00000 0.000000 0.000000 0.000000 0.000000 \n",
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"25% 5.00000 7.000000 1.000000 8.000000 2.000000 \n",
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"50% 6.00000 8.000000 3.000000 8.000000 3.000000 \n",
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"75% 8.00000 9.000000 4.000000 9.000000 5.000000 \n",
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"max 15.00000 15.000000 15.000000 15.000000 15.000000 \n",
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"\n",
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" yegvx \n",
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"count 20000.00000 \n",
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"mean 7.80120 \n",
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"std 1.61747 \n",
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"min 0.00000 \n",
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"25% 7.00000 \n",
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"50% 8.00000 \n",
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"75% 9.00000 \n",
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"max 15.00000 \n"
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]
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}
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],
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"source": [
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"# 3. Exploratory Data Analysis (EDA)\n",
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"print(\"Data Types:\\n\", data.dtypes)\n",
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"print(\"\\nMissing Values:\\n\", data.isnull().sum())\n",
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"print(\"\\nStatistical Summary:\\n\", data.describe())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "3e8a74ac",
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
||
"text/plain": [
|
||
"<Figure size 1400x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 4. Visualize Class Distribution\n",
|
||
"plt.figure(figsize=(14, 4))\n",
|
||
"data['letter'].value_counts().sort_index().plot(kind='bar')\n",
|
||
"plt.title(\"Number of Samples per Letter Class\")\n",
|
||
"plt.xlabel(\"Letter\")\n",
|
||
"plt.ylabel(\"Count\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "cee0449a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Classes: ['A' 'B' 'C' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N' 'O' 'P' 'Q' 'R'\n",
|
||
" 'S' 'T' 'U' 'V' 'W' 'X' 'Y' 'Z']\n",
|
||
"Number of classes: 26\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 5. Encode Labels and Separate Features\n",
|
||
"label_encoder = LabelEncoder()\n",
|
||
"data['letter'] = label_encoder.fit_transform(data['letter']) # A=0, B=1, ..., Z=25\n",
|
||
"\n",
|
||
"X = data.drop('letter', axis=1).values # 16 numeric features\n",
|
||
"y = data['letter'].values # class index 0–25\n",
|
||
"num_classes = len(label_encoder.classes_)\n",
|
||
"print(\"Classes:\", label_encoder.classes_)\n",
|
||
"print(\"Number of classes:\", num_classes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "d0221a02",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Train samples: 16000\n",
|
||
"Test samples: 4000\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 6. Split into Training and Testing Sets\n",
|
||
"# 80% train, 20% test; stratify ensures balanced class distribution in both sets\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
||
" X, y, test_size=0.2, random_state=42, stratify=y)\n",
|
||
"print(\"Train samples:\", X_train.shape[0])\n",
|
||
"print(\"Test samples: \", X_test.shape[0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "3bca0b00",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 7. Feature Scaling (Standardization)\n",
|
||
"scaler = StandardScaler()\n",
|
||
"X_train = scaler.fit_transform(X_train) # learn mean/std from train, then scale\n",
|
||
"X_test = scaler.transform(X_test) # apply same mean/std to test (no leakage)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "55c7d6f4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 8. One-Hot Encode Labels\n",
|
||
"# e.g. class 2 of 26 -> [0, 0, 1, 0, ..., 0]\n",
|
||
"y_train_cat = to_categorical(y_train, num_classes)\n",
|
||
"y_test_cat = to_categorical(y_test, num_classes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "c54402e6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
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||
"output_type": "stream",
|
||
"text": [
|
||
"E0000 00:00:1777876997.705371 137811 cuda_platform.cc:52] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
|
||
]
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||
},
|
||
{
|
||
"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
|
||
"</pre>\n"
|
||
],
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"text/plain": [
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"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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},
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
||
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
|
||
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
||
"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,352</span> │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,690</span> │\n",
|
||
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
|
||
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
|
||
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
|
||
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m4,352\u001b[0m │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m32,896\u001b[0m │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m8,256\u001b[0m │\n",
|
||
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
|
||
"│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m) │ \u001b[38;5;34m1,690\u001b[0m │\n",
|
||
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">47,194</span> (184.35 KB)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m47,194\u001b[0m (184.35 KB)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">47,194</span> (184.35 KB)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m47,194\u001b[0m (184.35 KB)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 9. Build the Deep Neural Network Model\n",
|
||
"model = Sequential()\n",
|
||
"\n",
|
||
"model.add(Input(shape=(X_train.shape[1],))) # input: 16 features\n",
|
||
"model.add(Dense(256, activation='relu')) # hidden layer 1: 256 neurons\n",
|
||
"model.add(Dropout(0.3)) # drop 30% neurons to reduce overfitting\n",
|
||
"model.add(Dense(128, activation='relu')) # hidden layer 2: 128 neurons\n",
|
||
"model.add(Dropout(0.3))\n",
|
||
"model.add(Dense(64, activation='relu')) # hidden layer 3: 64 neurons\n",
|
||
"model.add(Dense(num_classes, activation='softmax')) # output: probability for each of 26 letters\n",
|
||
"\n",
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "1ccecf3e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 10. Compile the Model\n",
|
||
"# categorical_crossentropy: standard loss for multi-class one-hot classification\n",
|
||
"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "0eb0c7d4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 1/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.5077 - loss: 1.6489 - val_accuracy: 0.7353 - val_loss: 0.8611\n",
|
||
"Epoch 2/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7166 - loss: 0.8976 - val_accuracy: 0.8281 - val_loss: 0.6201\n",
|
||
"Epoch 3/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7714 - loss: 0.7307 - val_accuracy: 0.8572 - val_loss: 0.5081\n",
|
||
"Epoch 4/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7990 - loss: 0.6325 - val_accuracy: 0.8797 - val_loss: 0.4283\n",
|
||
"Epoch 5/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8230 - loss: 0.5591 - val_accuracy: 0.8922 - val_loss: 0.3806\n",
|
||
"Epoch 6/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8377 - loss: 0.5064 - val_accuracy: 0.8991 - val_loss: 0.3535\n",
|
||
"Epoch 7/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8466 - loss: 0.4690 - val_accuracy: 0.9044 - val_loss: 0.3204\n",
|
||
"Epoch 8/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8594 - loss: 0.4359 - val_accuracy: 0.9166 - val_loss: 0.2927\n",
|
||
"Epoch 9/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8690 - loss: 0.4081 - val_accuracy: 0.9159 - val_loss: 0.2742\n",
|
||
"Epoch 10/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8752 - loss: 0.3820 - val_accuracy: 0.9162 - val_loss: 0.2572\n",
|
||
"Epoch 11/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8784 - loss: 0.3669 - val_accuracy: 0.9272 - val_loss: 0.2415\n",
|
||
"Epoch 12/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8878 - loss: 0.3444 - val_accuracy: 0.9256 - val_loss: 0.2359\n",
|
||
"Epoch 13/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8918 - loss: 0.3282 - val_accuracy: 0.9303 - val_loss: 0.2197\n",
|
||
"Epoch 14/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8966 - loss: 0.3230 - val_accuracy: 0.9306 - val_loss: 0.2128\n",
|
||
"Epoch 15/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8979 - loss: 0.3084 - val_accuracy: 0.9331 - val_loss: 0.2073\n",
|
||
"Epoch 16/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9009 - loss: 0.2980 - val_accuracy: 0.9331 - val_loss: 0.2126\n",
|
||
"Epoch 17/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9060 - loss: 0.2936 - val_accuracy: 0.9316 - val_loss: 0.2010\n",
|
||
"Epoch 18/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9069 - loss: 0.2754 - val_accuracy: 0.9375 - val_loss: 0.1862\n",
|
||
"Epoch 19/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9092 - loss: 0.2723 - val_accuracy: 0.9353 - val_loss: 0.1874\n",
|
||
"Epoch 20/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9095 - loss: 0.2622 - val_accuracy: 0.9381 - val_loss: 0.1765\n",
|
||
"Epoch 21/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9138 - loss: 0.2536 - val_accuracy: 0.9428 - val_loss: 0.1801\n",
|
||
"Epoch 22/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9141 - loss: 0.2550 - val_accuracy: 0.9444 - val_loss: 0.1715\n",
|
||
"Epoch 23/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9198 - loss: 0.2374 - val_accuracy: 0.9428 - val_loss: 0.1782\n",
|
||
"Epoch 24/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9196 - loss: 0.2355 - val_accuracy: 0.9481 - val_loss: 0.1629\n",
|
||
"Epoch 25/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9232 - loss: 0.2282 - val_accuracy: 0.9441 - val_loss: 0.1637\n",
|
||
"Epoch 26/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9279 - loss: 0.2143 - val_accuracy: 0.9453 - val_loss: 0.1616\n",
|
||
"Epoch 27/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9274 - loss: 0.2203 - val_accuracy: 0.9447 - val_loss: 0.1612\n",
|
||
"Epoch 28/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9293 - loss: 0.2131 - val_accuracy: 0.9469 - val_loss: 0.1523\n",
|
||
"Epoch 29/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9274 - loss: 0.2168 - val_accuracy: 0.9478 - val_loss: 0.1560\n",
|
||
"Epoch 30/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9284 - loss: 0.2103 - val_accuracy: 0.9516 - val_loss: 0.1450\n",
|
||
"Epoch 31/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9312 - loss: 0.2101 - val_accuracy: 0.9500 - val_loss: 0.1466\n",
|
||
"Epoch 32/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9355 - loss: 0.1934 - val_accuracy: 0.9525 - val_loss: 0.1466\n",
|
||
"Epoch 33/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9323 - loss: 0.2047 - val_accuracy: 0.9538 - val_loss: 0.1422\n",
|
||
"Epoch 34/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9339 - loss: 0.1960 - val_accuracy: 0.9503 - val_loss: 0.1441\n",
|
||
"Epoch 35/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9359 - loss: 0.1889 - val_accuracy: 0.9563 - val_loss: 0.1340\n",
|
||
"Epoch 36/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9397 - loss: 0.1832 - val_accuracy: 0.9550 - val_loss: 0.1459\n",
|
||
"Epoch 37/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9400 - loss: 0.1772 - val_accuracy: 0.9534 - val_loss: 0.1386\n",
|
||
"Epoch 38/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9358 - loss: 0.1921 - val_accuracy: 0.9563 - val_loss: 0.1381\n",
|
||
"Epoch 39/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9370 - loss: 0.1776 - val_accuracy: 0.9519 - val_loss: 0.1407\n",
|
||
"Epoch 40/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9380 - loss: 0.1794 - val_accuracy: 0.9619 - val_loss: 0.1251\n",
|
||
"Epoch 41/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9384 - loss: 0.1788 - val_accuracy: 0.9538 - val_loss: 0.1370\n",
|
||
"Epoch 42/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9410 - loss: 0.1760 - val_accuracy: 0.9578 - val_loss: 0.1344\n",
|
||
"Epoch 43/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9389 - loss: 0.1737 - val_accuracy: 0.9584 - val_loss: 0.1394\n",
|
||
"Epoch 44/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9416 - loss: 0.1713 - val_accuracy: 0.9619 - val_loss: 0.1202\n",
|
||
"Epoch 45/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9445 - loss: 0.1675 - val_accuracy: 0.9569 - val_loss: 0.1290\n",
|
||
"Epoch 46/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9437 - loss: 0.1668 - val_accuracy: 0.9597 - val_loss: 0.1256\n",
|
||
"Epoch 47/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9430 - loss: 0.1697 - val_accuracy: 0.9588 - val_loss: 0.1343\n",
|
||
"Epoch 48/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9441 - loss: 0.1653 - val_accuracy: 0.9600 - val_loss: 0.1265\n",
|
||
"Epoch 49/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9455 - loss: 0.1577 - val_accuracy: 0.9581 - val_loss: 0.1301\n",
|
||
"Epoch 50/50\n",
|
||
"\u001b[1m400/400\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9446 - loss: 0.1615 - val_accuracy: 0.9634 - val_loss: 0.1236\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 11. Train the Model\n",
|
||
"history = model.fit(\n",
|
||
" X_train, y_train_cat,\n",
|
||
" epochs=50,\n",
|
||
" batch_size=32,\n",
|
||
" validation_split=0.2 # use 20% of training data to monitor val loss each epoch\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "d377b34a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9645 - loss: 0.1043\n",
|
||
"Test Loss: 0.1043\n",
|
||
"Test Accuracy: 96.45%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 12. Evaluate the Model on Test Data\n",
|
||
"loss, accuracy = model.evaluate(X_test, y_test_cat)\n",
|
||
"print(f\"Test Loss: {loss:.4f}\")\n",
|
||
"print(f\"Test Accuracy: {accuracy*100:.2f}%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "5fe596e6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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iSqUSu3TpIq5fv77Etb799luxZ8+eopeXl6hUKsWmTZuKr732mpiZmSmKoijqdDrxtddeEyMiIkQXFxfR2dlZjIiIEL/++usKY7zXqVOnxDFjxogBAQGmzz5mzBgxNja23HOio6NFAKIgCOL169fLPOby5cvi+PHjRX9/f1GhUIhBQUHiI488Iq5evdp0TGV/B8XuNxR8woQJpmOLfztbtmwR27ZtKyqVSrFly5ZlDtGvzHchiqKYm5srvvXWW2Ljxo1NZTVy5Ejx8uXLJeIra4j3vb+7tLQ0cerUqWLLli1FZ2dn0c3NTezatav422+/VaociMxNEKta/0hERFYXEhKC1q1bY/369bYOhajWY58bIiIisitMboiIiMiuMLkhIiIiu8I+N0RERGRXWHNDREREdoXJDREREdmVejeJn9FoRGJiIlxcXEotQEdERES1kyiKyMrKQmBgoGkyyvLUu+QmMTERwcHBtg6DiIiIquH69eto0KBBhcfUu+SmeIG769evm1YGNhe9Xo+tW7diwIABUCgUZr02lcbyti6Wt3WxvK2L5W1d1SlvrVaL4ODgSi1UW++Sm+KmKFdXV4skN2q1Gq6urvzHYQUsb+tieVsXy9u6WN7WVZPyrkyXEnYoJiIiIrvC5IaIiIjsCpMbIiIisiv1rs8NERHVnMFggF6vt3UYZqPX6+Hg4ID8/HwYDAZbh2P3yitvR0fH+w7zrgwmN0REVGmiKCI5ORkZGRm2DsWsRFGEv78/rl+/zjnQrKC88pbJZGjcuDEcHR1rdH0mN0REVGnFiY2vry/UarXdJAJGoxHZ2dnQaDRmqTmgipVV3sWT7CYlJaFhw4Y1+m0xuSEiokoxGAymxMbLy8vW4ZiV0WhEQUEBVCoVkxsrKK+8fXx8kJiYiMLCwhoNyec3SERElVLcx0atVts6FLJTxc1RNe33xOSGiIiqxF6aoqj2Mddvi8kNERER2RUmN0RERFUUEhKCL774otLH79q1C4Ig2N0os9qKyQ0REdktQRAq3ObOnVut6x45cgSTJk2q9PHdu3dHUlIS3NzcqnW/ymISJeFoKTMxGEWkZulwK8/WkRARUbGkpCTT85UrV2L27NmIi4sz7dNoNKbnoiiisLCwUnOs+Pj4VCkOR0dH+Pv7V+kcqj7W3JhJUmYeeny0Gx+elNs6FCIiKuLv72/a3NzcIAiC6fX58+fh4uKCTZs2oXPnzvDz88O+fftw+fJlDB06FH5+ftBoNOjcuTO2bdtW4rr/bJYSBAHff/89hg8fDrVajdDQUKxbt870/j9rVJYuXQp3d3ds2bIFYWFh0Gg0GDRoUIlkrLCwEC+++CLc3d3h5eWFN954AxMmTMCwYcOqXR537tzB+PHj4eHhAbVajcGDB+PixYum969du4aoqCh4eHjA2dkZrVq1wsaNG03njhs3Dj4+PnByckJoaCiWLFlS7VgsicmNmbiopPH4elFAQaHR1uEQEVmFKIrILSi0+iaKotk+w5tvvon3338fhw4dQtu2bZGdnY0hQ4Zg+/btOHHiBAYNGoSoqCgkJCRUeJ158+Zh1KhROHXqFIYMGYJx48YhPT293ONzc3PxySef4H//+x/27NmDhIQEvPrqq6b3P/zwQ/zyyy9YsmQJ9u/fD61Wi7Vr19bosz711FM4evQo1q1bhwMHDkAURQwZMsQ0zH/q1KnQ6XTYs2cPYmNj8eGHH5pqt2bNmoWzZ89i06ZNOHfuHBYtWgRvb+8axWMpbJYyE43yblFm6wrh7KS0aTxERNaQpzcgfPYWq9/37DsDoXY0z5+wd955B/3794dWq4Wrqyu8vb0RERFhev+///0v1qxZg3Xr1mHatGnlXuepp57CmDFjAADvv/8+vvzySxw+fBiDBg0q83i9Xo9vvvkGTZs2BQBMmzYN77zzjun9r776CjNnzsTw4cMBAAsWLDDVolTHxYsXsW7dOuzfvx/du3cHAPzyyy8IDg7G2rVr8fjjjyMhIQEjRoxAmzZtAABNmjQxnZ+QkID27dujU6dOQFHtVW3FmhszkcsEqB2lJqlsXaGtwyEiokoq/mNdLDs7G6+++irCwsLg7u4OjUaDc+fO3bfmpm3btqbnzs7OcHV1RWpqarnHq9VqU2IDAAEBAabjMzMzkZKSgi5dupjel8vl6NixY7U+IwCcO3cODg4O6Nq1q2mfl5cXWrRogXPnzgEAXnzxRbz77rvo0aMH5syZg1OnTpmOff7557FixQq0a9cOr7/+Ov7+++9qx2JprLkxI43SAbkFBiY3RFRvOCnkOPvOQJvc11ycnZ1LvH711VcRHR2NTz75BM2aNYOTkxNGjhyJgoKCCq/zz+UCBEGA0Vh+N4Wyjjdnc1t1PPvssxg4cCA2bNiArVu3Yv78+fj000/xwgsvYPDgwbh27Ro2btyI6Oho9O3bF1OnTsUnn3xi05jLwpobMypumsrKZ3JDRPWDIAhQOzpYfbPkLMn79+/HU089heHDh6NNmzbw9/fH1atXLXa/sri5ucHPzw9Hjhwx7TMYDDh+/Hi1rxkWFobCwkIcOnTItO/27duIi4tDeHi4aV9wcDAmT56MP/74A6+88gq+++4703s+Pj6YMGECfv75Z3zxxRdYvHhxteOxJNbcmJFGxWYpIqK6LjQ0FH/88QeioqIgCAJmzZpVYQ2MpbzwwguYP38+mjVrhpYtW+Krr77CnTt3KpXYxcbGwsXFxfRaEARERERg6NCheO655/Dtt9/CxcUFb775JoKCgjB06FAAwPTp0zF48GA0b94cd+7cwc6dOxEWFgYAmD17Njp27IhWrVpBp9Nh/fr1pvdqG5vW3OzZswdRUVEIDAyEIAiV6gWu0+nw1ltvoVGjRlAqlQgJCcGPP/5olXjvp7jmJltXswW/iIjIdj777DN4eHige/fuiIqKwsCBA9GhQwerx/HGG29gzJgxGD9+PCIjI6HRaDBw4ECoVKr7ntuzZ0+0b9/etBX31VmyZAk6duyIRx55BJGRkRBFERs3bjQ1kRkMBkydOhVhYWEYNGgQmjdvjq+//hoomqtn5syZaNu2LXr27Am5XI4VK1ZYuBSqRxBt2MC3adMm7N+/Hx07dsRjjz2GNWvW3Hf8/tChQ5GSkoJ3330XzZo1Q1JSEoxGI3r06FGpe2q1Wri5uSEzMxOurq5m+iSSScuOYOvZVMyNCsNTPZpU4gyqCb1ej40bN2LIkCGl2q7J/Fje1lUbyzs/Px/x8fFo3Lhxpf7A1iVGo9E0Wkomq509NoxGI8LCwjBq1Cj897//tXU4NVJeeVf0G6vK32+bNksNHjwYgwcPrvTxmzdvxu7du3HlyhV4enoCtWwomouqqOaGfW6IiKiGrl27hq1bt6JXr17Q6XRYsGAB4uPjMXbsWFuHVuvVzvS0HOvWrUOnTp3w0UcfISgoCM2bN8err76KvLzasebB3WYpJjdERFQzMpkMS5cuRefOndGjRw/ExsZi27ZttbafS21SpzoUX7lyBfv27YNKpcKaNWuQlpaGKVOm4Pbt2+VOAa3T6aDT6UyvtVotUFTlWzwjo7moHaROXtq8ArNfm0orLmOWtXWwvK2rNpa3Xq+HKIowGo026WBrScU9NIo/X20QFBSEvXv3ltpfW+KrifLK22g0QhRF6PV6yOUlh/tX5d9CnUpujEYjBEHAL7/8YlpZ9bPPPsPIkSPx9ddfw8nJqdQ58+fPx7x580rt37p1K9RqtVnjS0wUAMhxMf46Nm68ZtZrU/mio6NtHUK9wvK2rtpU3g4ODvD390d2dvZ953ypq7KysmwdQr3yz/IuKChAXl4e9uzZg8LCkq0gubm5lb5unUpuAgICEBQUVGLJ+LCwMIiiiBs3biA0NLTUOTNnzsSMGTNMr7VaLYKDgzFgwACzdyjOOHQNf16Lg8bTF0OGWL9nfX2j1+sRHR2N/v3715oOl/aM5W1dtbG88/Pzcf36dWg0GrvrUCyKIrKysuDi4mLROXRIUl555+fnw8nJCT179iyzQ3Fl1ankpkePHli1ahWys7NNC3lduHABMpkMDRo0KPMcpVIJpbL0Ok8KhcLs/8FwU0v3ydUbas1/jOoDS3yXVD6Wt3XVpvI2GAwQBAEymazWjiiqruKmkeLPR5ZVXnnLZDIIglDm774q/w5s+g1mZ2cjJiYGMTExAID4+HjExMSY1u+YOXMmxo8fbzp+7Nix8PLywtNPP42zZ89iz549eO211/DMM8+U2SRlbRolJ/EjIiKyNZsmN0ePHjVNMAQAM2bMQPv27TF79mwAQFJSUomFyjQaDaKjo5GRkYFOnTph3LhxiIqKwpdffmmzz3AvF5WUVXL5BSIiItuxabNU7969K1wkbOnSpaX2tWzZslZ1sLsXa26IiIhsjw2LZsTlF4iI7FPv3r0xffp00+uQkBB88cUXFZ5T2WWF7sdc16lPmNyYUXFyU1BohK6QCQ4Rka1FRUVh0KBBZb63d+9eCIKAU6dOVfm6R44cwaRJk8wQ4V1z585Fu3btSu1PSkqq0mz+1bF06VK4u7tb9B7WxOTGjJyVd1v5uAQDEZHtTZw4EdHR0bhx40ap95YsWYJOnTqhbdu2Vb6uj4+P2edKK4+/v3+Zo36pfExuzEguE6CUSX2I2O+GiMj2HnnkEfj4+JTqw5mdnY1Vq1Zh4sSJuH37NsaOHYvw8HBoNBq0adMGv/76a4XX/Wez1MWLF01zs4SHh5fZN/SNN95A8+bNoVar0aRJE8yaNcs06+7SpUsxb948nDx5EoIgQBAEU8z/bJaKjY3FQw89BCcnJ3h5eWHSpEnIzs42vf/UU09h2LBh+OSTTxAQEAAvLy9MnTq1RrNdJyQkYOjQodBoNHB1dcWoUaOQkpJiev/kyZPo06cPXFxc4Orqio4dO+Lo0aNA0RpZUVFR8PDwgLOzM1q1aoWNGzdWO5bKqFPz3NQFKjmgM3LEFBHVE6II6Cs/c6zZKNRAJSbbc3BwwPjx47F06VK89dZbpgnjVq1aBYPBgDFjxiA7OxsdO3bE1KlTERAQgE2bNuHJJ59E06ZN0aVLl/vew2g04rHHHoOfnx8OHTqEzMzMEv1zirm4uGDp0qUIDAxEbGwsnnvuObi4uOD111/H6NGjcfr0aWzevBnbtm0DgBIT1hbLycnBwIEDERkZiSNHjiA1NRXPPvsspk2bViKB27lzJwICArBz505cunQJo0ePRrt27fDcc8/d9/OU9fmKE5vdu3ejsLAQU6dOxejRo7Fr1y4AwLhx49C+fXssWrQIcrkcMTExpnlppk6dioKCAuzZswfOzs44e/asaa46S2FyY2YqByBTz5obIqon9LnA+4HWv+9/EgFH50od+swzz+Djjz/G7t270bt3b6CoSWrEiBFwc3ODm5sbXnnlFWi1Wri6uuKFF17Ali1b8Ntvv1Uqudm2bRvOnz+PLVu2IDBQKov333+/VD+Zt99+2/Q8JCQEr776KlasWIHXX38dTk5O0Gg0piUuyrN8+XLk5+dj2bJlcHaWPv+CBQsQFRWFDz/8EH5+fgAADw8PLFiwAHK5HC1btsTDDz+M7du3Vyu52b59O2JjYxEfH4/g4GAAwLJly9CqVSscOXIEnTt3RkJCAl577TW0bNkSAEqsGJCQkIARI0agTZs2AIAmTZrAaDRWacbhqmKzlJmpitb5Ys0NEVHt0LJlS3Tv3h0//vgjAODSpUvYu3cvJk6cCBTNvPzuu++ie/fu8Pb2hkajwZYtW0rMs1aRc+fOITg42JTYAEBkZGSp41auXIkePXrA398fGo0Gb7/9dqXvce+9IiIiTIkNimbvNxqNiIuLM+1r1apViYUnAwICkJqaWqV73XvP4OBgU2IDAOHh4XB3d8e5c+eAonnqnn32WfTr1w8ffPABLl++bDr2xRdfxLvvvosePXpgzpw51erAXVWsuTEzlVwEICBbV3tW8iUishiFWqpFscV9q2DixIl44YUXsHDhQixZsgRNmzZFr169AAAff/wxvvzyS7z33nvo0qULXFxcMH36dLMuDnrgwAGMGzcO8+bNw8CBA+Hm5oYVK1bg008/Nds97vXPpQoEQbDoauJz587F2LFjsWHDBmzatAlz5szBihUrMHz4cDz77LMYOHAgNmzYgK1bt2L+/Pn45JNPSqxAYG6suTGz4pobjpYionpBEKTmIWtvVVzcctSoUZDJZFi+fDmWLVuGZ555xtT/Zv/+/Xj00UcxevRoREREoEmTJrhw4UKlrx0WFobr168jKSnJtO/gwYMljvn777/RqFEjvPXWW+jUqRNCQ0Nx7dq1Esc4OjrCYKh4GpGwsDCcPHkSOTk5pn379++HTCZDixYtKh1zVRR/vuvXr5v2nT17FhkZGQgPDzfta968OV5++WVs3boVjz32GJYsWWJ6Lzg4GJMnT8Yff/yBV155Bd9//71FYi3G5MbMTM1S7HNDRFRraDQajB49GjNnzkRSUhKeeuop03uhoaHYtm0bDh06hHPnzuHf//53iZFA99OvXz80b94cEyZMwMmTJ7F371689dZbJY4JDQ1FQkICVqxYgcuXL+PLL7/EmjVrShwTEhJiWmMxLS0NOp2u1L3GjRsHlUqFCRMm4PTp09i5cydeeOEFPPnkk6b+NtVlMBhM6z0Wb+fOnUO/fv3Qpk0bjBs3DsePH8fhw4cxfvx49OrVC506dUJeXh6mTZuGXbt24dq1a9i/fz+OHDmCsLAwAMD06dOxZcsWxMfH4/jx49i5c6epb46lMLkxM1VRQx/73BAR1S4TJ07EnTt3MHDgwBL9Y95++220b98eI0eOxEMPPQR/f38MGzas0teVyWRYs2YN8vLy0KVLFzz77LN47733Shzz6KOP4uWXX8a0adPQrl07/P3335g1a1aJY0aMGIFBgwahT58+8PHxKXM4ulqtxpYtW5Ceno7OnTtj5MiR6Nu3LxYsWFCtMrlXdna2ab3H4i0qKgqCIODPP/+Eh4cHevbsiX79+qFJkyZYuXIlAEAul+P27dsYP348mjdvjlGjRmHw4MGYN28eUJQ0TZ06FWFhYRg0aBCaN2+OhQsX1jjeighiRYs72SGtVgs3NzdkZmbC1dXVrNfW6/WY9u1mbLkhw5PdGuG/w1qb9fpUkl6vx8aNGzFkyJBS7ctkfixv66qN5Z2fn4/4+Hg0btwYKpXK1uGYVfHoHVdXV8hk/P9+SyuvvCv6jVXl7ze/QTOTOhRzKDgREZGtMLkxMw4FJyIisi0mN2bmZEpuOBSciIjIFpjcmJlpKDibpYiIiGyCyY2ZqRzY54aI7Fs9G4dCVmSu3xaTGzNjnxsislfFo7Zyc22wUCbVC8WzQt+7dER1cPkFM+MMxURkr+RyOdzd3U1rFKnVatMsv3Wd0WhEQUEB8vPzORTcCsoqb6PRiFu3bkGtVsPBoWbpCZMbMytObgoMRugKDVA61Cz7JCKqTYpXrK7uIoy1lSiKyMvLg5OTk90kbLVZeeUtk8nQsGHDGn8HTG7MTHlPLpOdXwilhskNEdkPQRAQEBAAX19f6PX2MypUr9djz5496NmzZ62ZNNGelVfejo6OZqk5Y3JjZjIBcFbKkaMzICu/EF4apa1DIiIyO7lcXuN+EbWJXC5HYWEhVCoVkxsrsHR5s2HRAjRKKWfkiCkiIiLrY3JjAcXJDUdMERERWR+TGwtgzQ0REZHtMLmxABdVcc2N/XS2IyIiqiuY3FgAa26IiIhsh8mNBbDPDRERke0wubEATdFkN6y5ISIisj4mNxbAPjdERES2w+TGAkx9btgsRUREZHVMbiyAHYqJiIhsh8mNBRQnN1rW3BAREVmdTZObPXv2ICoqCoGBgRAEAWvXrq30ufv374eDgwPatWtn0Riro7jPDZuliIiIrM+myU1OTg4iIiKwcOHCKp2XkZGB8ePHo2/fvhaLrSbYLEVERGQ7Nl0VfPDgwRg8eHCVz5s8eTLGjh0LuVxepdoea2FyQ0REZDt1rs/NkiVLcOXKFcyZM8fWoZRLc89QcFEUbR0OERFRvWLTmpuqunjxIt58803s3bsXDg6VC12n00Gn05lea7VaAIBer4deb955aIqvp5JLCY3eICInTwelQm7W+5CkuLzN/T1S2Vje1sXyti6Wt3VVp7yrcmydSW4MBgPGjh2LefPmoXnz5pU+b/78+Zg3b16p/Vu3boVarTZzlJL9u3eainbtxi1wUVjkNlQkOjra1iHUKyxv62J5WxfL27qqUt65ubmVPlYQa0m7iSAIWLNmDYYNG1bm+xkZGfDw8IBcfrcWxGg0QhRFyOVybN26FQ899FCp88qquQkODkZaWhpcXV3N+hn0ej2io6PRv39/dP5wD3J0Bmyb/gAaeVkmiarv7i1vhYIZpKWxvK2L5W1dLG/rqk55a7VaeHt7IzMz875/v+tMzY2rqytiY2NL7Pv666+xY8cOrF69Go0bNy7zPKVSCaVSWWq/QqGw2A9YoVDAVaVAjs6AvELwH4qFWfK7pNJY3tbF8rYulrd1VaW8q/K92DS5yc7OxqVLl0yv4+PjERMTA09PTzRs2BAzZ87EzZs3sWzZMshkMrRu3brE+b6+vlCpVKX21wamlcF1bL8lIiKyJpsmN0ePHkWfPn1Mr2fMmAEAmDBhApYuXYqkpCQkJCTYMMLq03AiPyIiIpuwaXLTu3fvCodKL126tMLz586di7lz51ogsprjXDdERES2UefmuakrXFVS22AWa26IiIisismNhbDmhoiIyDaY3FjI3VmKmdwQERFZE5MbC7lbc8PRUkRERNbE5MZCXFhzQ0REZBNMbizEhUPBiYiIbILJjYVolEWjpdihmIiIyKqY3FgIOxQTERHZBpMbCzE1S7FDMRERkVUxubEQFyX73BAREdkCkxsLMa0tpSuscIkJIiIiMi8mNxZSPM+N3iBCV2i0dThERET1BpMbC3F2dIAgSM/ZqZiIiMh6mNxYiEwmQOPI9aWIiIisjcmNBWk4kR8REZHVMbmxoLtLMHA4OBERkbUwubGg4k7FnKWYiIjIepjcWJBGJS3BwGYpIiIi62FyY0GmifxYc0NERGQ1TG4siH1uiIiIrI/JjQWxzw0REZH1MbmxIA4FJyIisj4mNxZkqrlhckNERGQ1TG4syLV4tBSbpYiIiKyGyY0FsVmKiIjI+pjcWBA7FBMREVkfkxsL0nAoOBERkdUxubEgVxUn8SMiIrI2JjcWpFHeXX5BFEVbh0NERFQvMLmxoOJmqUKjCF2h0dbhEBER1QtMbixIrZBDEKTnWva7ISIisgomNxYkkwmmEVMcDk5ERGQdTG4sjCuDExERWZdNk5s9e/YgKioKgYGBEAQBa9eurfD4P/74A/3794ePjw9cXV0RGRmJLVu2WC3e6uBEfkRERNZl0+QmJycHERERWLhwYaWO37NnD/r374+NGzfi2LFj6NOnD6KionDixAmLx1pdLkVLMGiZ3BAREVmFgy1vPnjwYAwePLjSx3/xxRclXr///vv4888/8ddff6F9+/YWiLDmNGyWIiIisqo63efGaDQiKysLnp6etg6lXHebpThaioiIyBpsWnNTU5988gmys7MxatSoco/R6XTQ6XSm11qtFgCg1+uh15s34Si+3r3XdVZI+WNmboHZ71fflVXeZDksb+tieVsXy9u6qlPeVTlWEGvJ1LmCIGDNmjUYNmxYpY5fvnw5nnvuOfz555/o169fucfNnTsX8+bNK/N8tVpdo5grY+1VGXYmyfBQoBFDG3EiPyIiourIzc3F2LFjkZmZCVdX1wqPrZM1NytWrMCzzz6LVatWVZjYAMDMmTMxY8YM02utVovg4GAMGDDgvoVTVXq9HtHR0ejfvz8UCqkj8eWdl7Ez6TJ8gxpiyJBws96vviurvMlyWN7WxfK2Lpa3dVWnvItbXiqjziU3v/76K5555hmsWLECDz/88H2PVyqVUCqVpfYrFAqL/YDvvbabWrp3boGR/2AsxJLfJZXG8rYulrd1sbytqyrlXZXvxabJTXZ2Ni5dumR6HR8fj5iYGHh6eqJhw4aYOXMmbt68iWXLlgFFTUkTJkzA//3f/6Fr165ITk4GADg5OcHNzc1mn6MixZP4ZbFDMRERkVXYdLTU0aNH0b59e9Mw7hkzZqB9+/aYPXs2ACApKQkJCQmm4xcvXozCwkJMnToVAQEBpu2ll16y2We4HxcVh4ITERFZk01rbnr37o2K+jMvXbq0xOtdu3ZZISrzKh4KnsVJ/IiIiKyiTs9zUxdwEj8iIiLrYnJjYS6suSEiIrIqJjcWVry2VLausMImOCIiIjIPJjcWVtwsZTCKyNdzEj8iIiJLY3JjYWpHOQRBep6l43BwIiIiS2NyY2GCIJhqb9jvhoiIyPKY3FiBa3G/GyY3REREFsfkxgo4HJyIiMh6mNxYASfyIyIish4mN1Zwd64bdigmIiKyNCY3VsBmKSIiIuthcmMFpsUz2SxFRERkcUxurMA0FJw1N0RERBbH5MYKipdgYIdiIiIiy2NyYwXsc0NERGQ9TG6sQGPqc8PRUkRERJbG5MYKXLj8AhERkdUwubGC4j43bJYiIiKyPCY3VsAZiomIiKyHyY0VsEMxERGR9TC5sQLTJH66QoiiaOtwiIiI7BqTGysoTm4MRhF5eoOtwyEiIrJrTG6swEkhh0yQnnMJBiIiIsticmMFgiBwCQYiIiIrYXJjJVyCgYiIyDqY3FgJVwYnIiKyDiY3VnJ3ODiXYCAiIrIkJjdWwon8iIiIrIPJjZVouL4UERGRVTC5sRKuL0VERGQdTG6s5N5ZiomIiMhymNxYyd1mKXYoJiIisiQmN1biwg7FREREVmHT5GbPnj2IiopCYGAgBEHA2rVr73vOrl270KFDByiVSjRr1gxLly61Sqw1xZXBiYiIrMOmyU1OTg4iIiKwcOHCSh0fHx+Phx9+GH369EFMTAymT5+OZ599Flu2bLF4rDXFSfyIiIisw8GWNx88eDAGDx5c6eO/+eYbNG7cGJ9++ikAICwsDPv27cPnn3+OgQMHWjDSmtMoufwCERGRNdSpPjcHDhxAv379SuwbOHAgDhw4YLOYKoujpYiIiKzDpjU3VZWcnAw/P78S+/z8/KDVapGXlwcnJ6dS5+h0Ouh0OtNrrVYLANDr9dDrzTtyqfh6ZV1XJZces/LNf9/6qqLyJvNjeVsXy9u6WN7WVZ3yrsqxdSq5qY758+dj3rx5pfZv3boVarXaIveMjo4utS+zAAAckJWvx4YNGyEIFrl1vVRWeZPlsLyti+VtXSxv66pKeefm5lb62DqV3Pj7+yMlJaXEvpSUFLi6upZZawMAM2fOxIwZM0yvtVotgoODMWDAALi6upo1Pr1ej+joaPTv3x8KhaLEe7kFhZh9bAdECOjdbwCclXWq6GulisqbzI/lbV0sb+tieVtXdcq7uOWlMurUX9jIyEhs3LixxL7o6GhERkaWe45SqYRSqSy1X6FQWOwHXNa1XR0cIJcJMBhF6IwC3PmPx2ws+V1SaSxv62J5WxfL27qqUt5V+V5s2qE4OzsbMTExiImJAYqGesfExCAhIQEoqnUZP3686fjJkyfjypUreP3113H+/Hl8/fXX+O233/Dyyy/b7DNUliAIXDyTiIjICmya3Bw9ehTt27dH+/btAQAzZsxA+/btMXv2bABAUlKSKdEBgMaNG2PDhg2Ijo5GREQEPv30U3z//fe1fhh4MU7kR0REZHk2bZbq3bs3RFEs9/2yZh/u3bs3Tpw4YeHILOPuEgzsjU9ERGQpdWqem7qOsxQTERFZHpMbKzL1uWGzFBERkcUwubEijYpLMBAREVkakxsrMnUoZnJDRERkMUxurMjVtL4UOxQTERFZCpMbK+JQcCIiIstjcmNFmqKaGy2bpYiIiCymWsnN9evXcePGDdPrw4cPY/r06Vi8eLE5Y7M7LkUditnnhoiIyHKqldyMHTsWO3fuBAAkJyejf//+OHz4MN566y2888475o7RbrBZioiIyPKqldycPn0aXbp0AQD89ttvaN26Nf7++2/88ssvZc4qTBJO4kdERGR51Upu9Hq9aaXtbdu24dFHHwUAtGzZEklJSeaN0I7cXTiTo6WIiIgspVrJTatWrfDNN99g7969iI6OxqBBgwAAiYmJ8PLyMneMdsO0thSbpYiIiCymWsnNhx9+iG+//Ra9e/fGmDFjEBERAQBYt26dqbmKStOo7va5qWjBUCIiIqq+aq0K3rt3b6SlpUGr1cLDw8O0f9KkSVCr1eaMz664KKXRUqII5BYY4Ky06aLsREREdqlaNTd5eXnQ6XSmxObatWv44osvEBcXB19fX3PHaDdUChnkMgHg+lJEREQWU63kZujQoVi2bBkAICMjA127dsWnn36KYcOGYdGiReaO0W4IgnC33w07FRMREVlEtZKb48eP48EHHwQArF69Gn5+frh27RqWLVuGL7/80twx2pVANycAwIWUbFuHQkREZJeqldzk5ubCxcUFALB161Y89thjkMlk6NatG65du2buGO1K1yaeAIADV9JsHQoREZFdqlZy06xZM6xduxbXr1/Hli1bMGDAAABAamoqXF1dzR2jXenWRBoqf/BKuq1DISIiskvVSm5mz56NV199FSEhIejSpQsiIyOBolqc9u3bmztGu9K1sScEAbiUmo1bWTpbh0NERGR3qpXcjBw5EgkJCTh69Ci2bNli2t+3b198/vnn5ozP7rirHRHmL9VuHYq/betwiIiI7E61khsA8Pf3R/v27ZGYmGhaIbxLly5o2bKlOeOzS8VNUwcuM7khIiIyt2olN0ajEe+88w7c3NzQqFEjNGrUCO7u7vjvf/8Lo9Fo/ijrEAdD7n2P6VbUqfjgFSY3RERE5latKXLfeust/PDDD/jggw/Qo0cPAMC+ffswd+5c5Ofn47333jN3nLXfrQtwWDIYfQv0QNTICg/t2tgLggBcvpWD1Kx8+LqorBYmERGRvatWcvPTTz/h+++/N60GDgBt27ZFUFAQpkyZUj+TG9dACLlpUAHQ56QB7gHlHuqmViA8wBVnErU4dCUdURGBVg2ViIjInlWrWSo9Pb3MvjUtW7ZEeno9HeKs1EB0DwEACLfO3ffwu0PC2TRFRERkTtVKbiIiIrBgwYJS+xcsWIC2bduaI646SfSREj7h1vn7HmvqVMzkhoiIyKyq1Sz10Ucf4eGHH8a2bdtMc9wcOHAA169fx8aNG80dY50h+oYDFzdDSD1732O7hEjz3Vy5lYNUbT58XdnvhoiIyByqVXPTq1cvXLhwAcOHD0dGRgYyMjLw2GOP4cyZM/jf//5n/ijriOKaG1Si5sZNrUCrQGm+m4Px9bQpj4iIyAKqVXMDAIGBgaU6Dp88eRI//PADFi9ebI7Y6hzRNxwo7nMjioAgVHh8t8ZeOH1Ti4NXbuNRdiomIiIyi2pP4kdl8GwKoyCHUJANZF6/7+GmTsWczI+IiMhsmNyYk1yBLGVRDUzK/fvddC5aZ+pKWg5StPmWj4+IiKgeYHJjZllOQdKTSnQqdnO6p98NR00RERGZRZX63Dz22GMVvp+RkVGtIBYuXIiPP/4YycnJiIiIwFdffYUuXbqUe/wXX3yBRYsWISEhAd7e3hg5ciTmz58Plcr2I460qmAAByuV3ABAZJPifjfpGNouyOLxERER2bsqJTdubm73fX/8+PFVCmDlypWYMWMGvvnmG3Tt2hVffPEFBg4ciLi4OPj6+pY6fvny5XjzzTfx448/onv37rhw4QKeeuopCIKAzz77rEr3tgStUwPpSer9J/JDUb+b7/bGs+aGiIjITKqU3CxZssTsAXz22Wd47rnn8PTTTwMAvvnmG2zYsAE//vgj3nzzzVLH//333+jRowfGjh0LAAgJCcGYMWNw6NAhs8dWHVpVUXJzKw4w6AG5osLjO4V4QiYA8Wk5SM7Mh7+b7WufiIiI6jKb9rkpKCjAsWPH0K9fv7sByWTo168fDhw4UOY53bt3x7Fjx3D48GEAwJUrV7Bx40YMGTLEanFXJM/RC6KjM2DUA7cv3/d4qd+NVCN2KJ61N0RERDVV7XluzCEtLQ0GgwF+fn4l9vv5+eH8+bInwhs7dizS0tLwwAMPQBRFFBYWYvLkyfjPf/5T5vE6nQ46nc70WqvVAgD0ej30er1ZP49erwcEGYzeLSFPPIbCpFMQPZre97wuIe6IvZmJvy/dwpBWpZviqGzF35+5v0cqG8vbulje1sXytq7qlHdVjrVpclMdu3btwvvvv4+vv/4aXbt2xaVLl/DSSy/hv//9L2bNmlXq+Pnz52PevHml9m/duhVqtdoiMV7XaRAC4PLff+H8VeV9j5ffEQDIsf30DXRXXLNITPYsOjra1iHUKyxv62J5WxfL27qqUt65ubmVPlYQRVGsZkw1VlBQALVajdWrV2PYsGGm/RMmTEBGRgb+/PPPUuc8+OCD6NatGz7++GPTvp9//hmTJk1CdnY2ZLKSLW1l1dwEBwcjLS0Nrq6uZv08er0e0dHRGOxxFY47ZsPYfDAMj99/OYqsfD06vb8TRhHY82pPBLDfTaUUl3f//v2hUFTct4lqjuVtXSxv62J5W1d1ylur1cLb2xuZmZn3/ftt05obR0dHdOzYEdu3bzclN0ajEdu3b8e0adPKPCc3N7dUAiOXywEAZeVpSqUSSmXp2hOFQmGxH7AsoLX0eOs8ZJW4h6dCgdZBbjh1IxPHr2sxzNvFInHZK0t+l1Qay9u6WN7WxfK2rqqUd1W+F5tP4jdjxgx89913+Omnn3Du3Dk8//zzyMnJMY2eGj9+PGbOnGk6PioqCosWLcKKFSsQHx+P6OhozJo1C1FRUaYkx9ZEnzDpyZ2rQEFOpc6JLF6KgUPCiYiIasTmfW5Gjx6NW7duYfbs2UhOTka7du2wefNmUyfjhISEEjU1b7/9NgRBwNtvv42bN2/Cx8cHUVFRpRbxtClnH2nLuSWtEB7U8b6ndGvihW/3XMEBJjdEREQ1YvPkBgCmTZtWbjPUrl27Srx2cHDAnDlzMGfOHCtFV02+YUD8LWkyv0okN51CPCATgGu3c5GYkYdAdyerhElERGRvbN4sZbd8W0mPlVhAEwBcVAq0CeJ8N0RERDXF5MZSfIv63VRyjSkA6Na0qN/N5XRLRUVERGT3mNxYil9RzU1VkpuiTsXsd0NERFR9TG4sxaeF9JidAuRULlnp1MgDcpmAhPRc3MzIs2x8REREdorJjaUoXQD3RtLzStbeuKik+W4A4BBrb4iIiKqFyY0l+YZLj6nnKn0K57shIiKqGSY3luRXnNycqfQp3Zp4Aux3Q0REVG1MbiypGjU3nUI8IZcJuJ6ehxt3Kr9IGBEREUmY3FjSvclNJdcn1SgdTPPd7L5wy5LRERER2SUmN5bk1QyQOQA6LZB5o9KnDWnjDwD4+WBCmYuBEhERUfmY3FiSgyPg3Vx6XoX5bkZ1CoZKIcO5JC2OXL1jufiIiIjsEJMbS6vGTMXuakcMbx8EAPjp76uWioyIiMguMbmxtOJ+N5VcY6rYhO4hAIDNZ5KRlMkJ/YiIiCqLyY2lVWPEFAC09HdF18aeMBhF/HIwwTKxERER2SEmN5ZWPNdNWhxgKKzSqU8V1d4sP5yAfL3BEtERERHZHSY3lubWEFA4A4YCIP1ylU7tH+6HQDcV0nMKsP5UksVCJCIisidMbixNJqtWp2IAcJDLMK6btD7VT39f5bBwIiKiSmByYw3FyU0VOxUDwJguDeHoIEPszUwcT8gwf2xERER2hsmNNfi1kh6rWHMDAJ7Ojng0IhDgsHAiIqJKYXJjDdVslipW3LF4Y2wSUrX55oyMiIjI7jC5sQbfopqb9HigoOqLYbYOckOnRh4oNIr45RCHhRMREVWEyY01aHwAtTcAEbh1vlqXmHDPsPCCQqOZAyQiIrIfTG6sxdQ0VbXJ/IoNau0PP1clbmXpsOk0h4UTERGVh8mNtdSgUzEAKOQyjOsqDQtfyo7FRERE5WJyYy017FSM4mHhchlOJGTg1A0OCyciIioLkxtrKe5UXI25bor5uCjxcNsAgLU3RERE5WJyYy2+LaXH7GQgN73alynuWLz+ZBLSsnXmio6IiMhuMLmxFqUL4N5Qel6Dpql2we5oF+yOAoMRv3JYOBERUSlMbqzJt2iF8GqOmCpWPKnfz4euQW/gsHAiIqJ7MbmxpuLkJuVMjS4zpE0AvDVKpGh12Hw62TyxERER2QkmN9ZkppobRwcZxnWVmrj+b/tF1t4QERHdg8mNNfndk9yIYo0u9cwDjeHp7IhLqdlYzr43REREJkxurMkrFJA5ALpMIPN6jS7l5qTAjP7NAQCfb7uAjNwCMwVJRERUt9WK5GbhwoUICQmBSqVC165dcfjw4QqPz8jIwNSpUxEQEAClUonmzZtj48aNVou32hwcgYB20vPzG2p8uSc6B6OFnwsycvX4v+0Xax4fERGRHbB5crNy5UrMmDEDc+bMwfHjxxEREYGBAwciNTW1zOMLCgrQv39/XL16FatXr0ZcXBy+++47BAUFWT32aol4QnqM+aXGl3KQy/D2I9LMx/87cA2XUrNrfE0iIqK6zubJzWeffYbnnnsOTz/9NMLDw/HNN99ArVbjxx9/LPP4H3/8Eenp6Vi7di169OiBkJAQ9OrVCxEREVaPvVpajwDkjkByrLTV0IOhPugX5otCo4j3N9asozIREZE9cLDlzQsKCnDs2DHMnDnTtE8mk6Ffv344cOBAmeesW7cOkZGRmDp1Kv7880/4+Phg7NixeOONNyCXy0sdr9PpoNPdnclXq9UCAPR6PfR6vVk/T/H1KryuwgXy0EGQnV8Hw/GfYez/bo3v+/qAUOyKu4Ud51Ox42wSHgz1rvE164JKlTeZDcvbulje1sXytq7qlHdVjrVpcpOWlgaDwQA/P78S+/38/HD+/Pkyz7ly5Qp27NiBcePGYePGjbh06RKmTJkCvV6POXPmlDp+/vz5mDdvXqn9W7duhVqtNuOnuSs6OrrC9/0KmqIbAP3x5dha0AWiUPOv4QE/GXYlyfCfVcfweoQBcqHGl6wz7lfeZF4sb+tieVsXy9u6qlLeubm5lT7WpslNdRiNRvj6+mLx4sWQy+Xo2LEjbt68iY8//rjM5GbmzJmYMWOG6bVWq0VwcDAGDBgAV1dXs8am1+sRHR2N/v37Q6FQlH+goT/Er36BKicVQ0IdITYfVON798jTo/8X+5Ccq4fWu7VpHhx7VunyJrNgeVsXy9u6WN7WVZ3yLm55qQybJjfe3t6Qy+VISUkpsT8lJQX+/v5lnhMQEACFQlGiCSosLAzJyckoKCiAo6NjieOVSiWUSmWp6ygUCov9gO97bYUCaDsKOLAADqdXAq2ianxPb4U0NHzWn2fwfzsuY3iHhnBT149/oJb8Lqk0lrd1sbyti+VtXVUp76p8LzbtUOzo6IiOHTti+/btpn1GoxHbt29HZGRkmef06NEDly5dgtF4d1beCxcuICAgoFRiU6tFjJEe4zbXaJXwe43p0hChvhrcydXjyx0cGk5ERPWTzUdLzZgxA9999x1++uknnDt3Ds8//zxycnLw9NNPAwDGjx9fosPx888/j/T0dLz00ku4cOECNmzYgPfffx9Tp0614aeoBv/WQEAEYNQDsavNckkHuQyzHpFmQf7p76u4cotDw4mIqP6xeXIzevRofPLJJ5g9ezbatWuHmJgYbN682dTJOCEhAUlJSabjg4ODsWXLFhw5cgRt27bFiy++iJdeeglvvvmmDT9FNUWMlR7NMOdNsZ7NffBQSw4NJyKi+qtWdCieNm0apk2bVuZ7u3btKrUvMjISBw8etEJkFtbmcWDr20BSDJBy9u7aUzX0nyFh2HPhFradS8Xei7fwYKiPWa5LRERUF9i85qZec/YCmg+Unp9cbrbLNvPV4MnIRgCAd9efQyFXDScionqEyY2ttStqmjq5EjAUmu2yL/UNhbtagbiULPx6pGaLdBIREdUlTG5sLXQAoPYGclKByzvMdll3tSNe7ietGj5/4znEJWeZ7dpERES1GZMbW5MrpL43MG/HYgAY17UhejTzQm6BAf/+31Fk5nFacSIisn9MbmqD4qapuI1mm/MGRUPDvxrTAUHuTrh6Oxcvr4yB0Sia7fpERES1EZOb2iCgLeDXBjAUAGf+MOulPZ0d8e2THaF0kGHH+VR8sZ2T+xERkX1jclNbtCuasTjGfKOmirUOcsP7w9sAAL7cfhHRZ1Puew4REVFdxeSmtmgzCpA5ADePAbfizH75ER0b4KnuIQCAGStjcJmzFxMRkZ1iclNbaHyAZv2l5xaovQGAtx4OQ5cQT2TpCvHv/x1Dts58Q8+JiIhqCyY3tUlxx+JTKwGjweyXV8hlWDiuA/xdVbiUmo1XfmMHYyIisj9MbmqT5oMAJ08gKwm4stMit/BxUWLRvzrAUS7DljMpWLT7skXuQ0REZCtMbmoTB0egzUjpuYWapgCgfUMPzBvaCgDwydY47IpLtdi9iIiIrI3JTW1T3DR1bj2Ql2Gx24zp0hBjujSEKAIv/noC127nWOxeRERE1sTkprYJaAf4hgMGHXDiZ4veau6j4Wjf0B3a/EI8+9NRpGjzLXo/IiIia2ByU9sIAtB1svR832eAznJrQikd5Fg0riN8XZS4mJqNx77+G5dSuQYVERHVbUxuaqN24wDPpkDubeDgIoveyt9Nhd+f744m3s64mZGHEYsO4MhV8y0BQUREZG1MbmojuQPw0FvS87+/Mut6U2UJ9lRj9fPd0b6hOzLz9PjX94ew+XSyRe9JRERkKUxuaqvw4YB/G0CnBfZ9bvHbeTo7Yvmz3dAvzBe6QiOe/+UY/nfgqsXvS0REZG5MbmormQx4aLb0/PBiQJtk8Vs6Ocrxzb86mkZRzfrzDD7afB6iyIn+iIio7mByU5uF9geCuwGF+cCej6xySwe5DO8Pb40Z/ZsDAL7edRmvrDoJvcFolfsTERHVFJOb2kwQgH5zpOfHlwHpV6x0WwEv9g3FRyPaQi4T8Mfxm5j401GuRUVERHUCk5varlF3oFk/wFgI7PrAqrce1TkY34/vBCeFHHsu3MITiw8gMSPPqjEQERFVFZObuuChWdLjqd+AlDNWvXWflr74dVI3eDo74vRNLaK+2ocDl29bNQYiIqKqYHJTFwS2A8KHARCBHe9Z/fbtgt3x59QeCA9wxe2cAvzrh0P4fu8VdjQmIqJaiclNXfHQ24AgA+I2ANePWP32wZ5q/P58dwxvHwSDUcS7G87hpRUxyC1gPxwiIqpdmNzUFd6hdxfV3PGOTUJwcpTjs1ERmBsVDgeZgHUnE/HY139z0U0iIqpVmNzUJb3eBOSOQPwe4Moum4QgCAKe6tEYvzzbFd4aJc4nZyHqq33YGZdqk3iIiIj+iclNXeIeDHSaKD3f/g5gwz4vXZt4Yf0LD5hWFX9m6RF8uf0ijEb2wyEiItticlPXPPgKoHAGbh4Dzm+waSj+biqsmNQN47pKMxp/Fn0B//75GDJyC2waFxER1W9MbuoajQ8QOUV6vuO/gNFg03CUDnK8N7wNPhrRFo5yGaLPpqD/53sQfTbFpnEREVH9xeSmLoqcBqjcgVvngRP/s3U0QNGEf6ufj0RTH2fcytLhuWVHMX3FCdzJYS0OERFZF5ObusjJXWqeAoCNrwHX/rZ1RACAtg3cseHFB/HvXk0gE4C1MYno//kebDmTbOvQiIioHmFyU1dFTgXCogBDAfDrGODWBVtHBABQKeSYOTgMvz/fHc18NUjL1uHf/zuGF39lLQ4REVlHrUhuFi5ciJCQEKhUKnTt2hWHDx+u1HkrVqyAIAgYNmyYxWOsdWRy4LHvgAadgfwM4JcRQFbt6efSvqEH1r/wAJ7v3RQyAVh3MhH9P9+NzaeTbB0aERHZOZsnNytXrsSMGTMwZ84cHD9+HBERERg4cCBSUyueN+Xq1at49dVX8eCDD1ot1lpH4QSMWQF4NgEyEoDlo4CC2jOhnkohxxuDWmLNlB4I9dUgLbsAk38+jmnLj+N2ts7W4RERkZ2yeXLz2Wef4bnnnsPTTz+N8PBwfPPNN1Cr1fjxxx/LPcdgMGDcuHGYN28emjRpYtV4ax1nb2DcakDtBSTFAKueBgy1a0mEiGB3rH/xAUzt0xRymYD1p5LQ97Pd+O3oda5PRUREZudgy5sXFBTg2LFjmDlzpmmfTCZDv379cODAgXLPe+edd+Dr64uJEydi7969Fd5Dp9NBp7tbS6DVagEAer0eer3eLJ+jWPH1zH3d+3JtCGHUL5D/PBzCxS0wrJ8B4+BPAEGwbhwVkAGY/lBT9G3hjf+sOYPzKdl4ffUprDp6He9EhaGZr6bK17RZeddTLG/rYnlbF8vbuqpT3lU51qbJTVpaGgwGA/z8/Ers9/Pzw/nz58s8Z9++ffjhhx8QExNTqXvMnz8f8+bNK7V/69atUKvV1Yy8YtHR0Ra57v34B09Cl/gvIT/xE+KSc3HRP8omcdzPpBBgt0rApusyHLl6B48s2I++gSL6BxnhKK/69WxV3vUVy9u6WN7WxfK2rqqUd25ubqWPtWlyU1VZWVl48skn8d1338Hb27tS58ycORMzZswwvdZqtQgODsaAAQPg6upq1vj0ej2io6PRv39/KBQKs167cobAeMQf8q0zEZ60Cs279IXYeqQN4ri/KAAvZ+Rh7l/nsOtCGrbeFBCXp8G8R8PQo6lXpa5h+/KuX1je1sXyti6Wt3VVp7yLW14qw6bJjbe3N+RyOVJSSo7ySUlJgb+/f6njL1++jKtXryIq6m6NhNFoBAA4ODggLi4OTZs2LXGOUqmEUqksdS2FQmGxH7Alr31f3acAWTeBAwvg8NcLgHsQ0LinbWK5jxAfBZY83QWbTydj7l9ncC09F08tPYZh7QLx9iPh8NaU/t7KYtPyrodY3tbF8rYulrd1VaW8q/K92LRDsaOjIzp27Ijt27eb9hmNRmzfvh2RkZGljm/ZsiViY2MRExNj2h599FH06dMHMTExCA4OtvInqKX6/xcIHwoY9cCKfwEpZ20dUbkEQcDgNgHYNqMXnuoeAqFo8r+HPtmFhTsvITEjz9YhEhFRHWPzZqkZM2ZgwoQJ6NSpE7p06YIvvvgCOTk5ePrppwEA48ePR1BQEObPnw+VSoXWrVuXON/d3R0ASu2v12QyYPhiad6b6weB7/sBfWcDXZ6T5sephVxUCsx9tBWGtw/Cf9bE4kyiFh9vicMnW+PQtbEnhrcPwuA2AXBV8f+oiIioYjZPbkaPHo1bt25h9uzZSE5ORrt27bB582ZTJ+OEhATIZDYfsV73KFTAmF+Blf8Cru0HNr8BnF4NPLoA8G1p6+jKFRHsjj+n9sAfJ25i9bEbOByfjoNXpG3Wn2fQP8wPw9oHoVdzH9SesWBERFSb2Dy5AYBp06Zh2rRpZb63a9euCs9dunSphaKyA2pPYMJ64NgSIHoOcOMI8M0DQM/XgAdeBhwcbR1hmRzkMozqFIxRnYJx404u/oxJxJoTN3EpNRsbYpOwITYJ7moFhrT2QyBbrYiI6B9YJWLvZDKg80Rg6kGg+SCpH86u94HFvYAbx2wd3X018FBjap9miH65J9a/8ACefaAxfFyUyMjVY/nhG/gk1gEvrDiJK7eybR0qERHVEkxu6gu3BtJSDSN+ANTeQOpZ4Id+wOb/1KolG8ojCAJaB7nh7UfCcXBmX/xvYhc83MYfAkRsPpOC/p/vwX/WxCJVm2/rUImIyMaY3NQnggC0GQlMPQy0HQ2IRuDgQuDrSODKbltHV2lymYAHQ33wxai2eL2tAX1aeMNgFLH8UAJ6frwTH285D20+ZxklIqqvmNzUR85ewGOLpTWp3IKBjGvAsqHAno+BonmD6opAZ2Dxvzrgt39HokNDd+TrjVi48zJ6frQT3+25gny9wdYhEhGRlTG5qc9C+wNTDgAdxgMQgR3vAr89CeRXfhbI2qJLY0/8/nx3LH6yI5r5apCRq8d7G8/hoU92YeWRBCY5RET1CJOb+k7pAjz6lbTJHYHz64HvHgJuxdk6sioTBAEDWvlj80sP4qMRbeHvqkJiZj7e+D0Wnd/bhrfXxiL2RiZXIicisnNMbkjSYTzw9GbANQi4fVFKcM79ZeuoqsVBLsOozsHY9Vpv/GdISwS5OyErvxA/H0xA1IJ9GPLlPizdH4+M3AJbh0pERBbA5IbuatARmLQbaPQAUJAtTQC4/R3AWDebdFQKOSb1bIq9r/fBzxO7IioiEI5yGc4laTH3r7Po8v52vPDrCey7mAajkbU5RET2olZM4ke1iMYHGL9WmvTv4EJg76dAYgww4ntpUsA6SCYT8ECoNx4I9UZGbgHWnriJlUdv4FySFn+dTMRfJxPh76pCqJ8GDTzUaODhVLRJz300SshknA+ZiKiuYHJDpckVwKD3gcD2wLoXgMvbgcW9gSd+Afzb2Dq6GnFXO+KpHo3xVI/GOH0zEyuPXMfamJtI1uYjuZw5chzlMgQVJTw9Q33wSEQAAtycrB47ERFVDpMbKl/bx6V1qFaMk4aLf98PeOhtoNuUWrsAZ1W0DnJD6yA3vPVwGI4n3MGNO3m4eScPN+7k4cadXNy4k4ekzDwUGIyIT8tBfFoO9l5Mw/ubzqFLiCcebReIIa0D4OFcO5exICKqr5jcUMX82wCTdgFr/g1c3ApsfRs4+ycw9GvAp7mtozMLlUKO7k29y3xPbzAiOTMfN+7kIS5Zi42xyTh8NR2H4qVtzp9n0LO5Dx6NCET/cD84K/lPiojI1vhfYro/tScw9jfg+DIpuSlegLPPf4DIaYDcfn9GCrkMwZ5qBHuqEdnUC0/1aIybGXlYfzIR604m4kyiFjvOp2LH+VSoFDL0C/ND/3A/dG3sBX83la3DJyKql+z3rxKZlyAAHScAzfoCf70EXNoGbJsj1eIM+xrwDbN1hFYT5O6Ef/dqin/3aopLqdlYV9QpOT4tB+tPJWH9qSQAQIiXGt2aeKFrE090beyFQHf20yEisgYmN1Q1bg2kZRtilgObZwKJx4FvewK93gB6TLfrWpyyNPPVYEb/5ni5XyhO39Ri/alE/H35Ns4kZuLq7VxcvZ2LFUeuAwAaeqrRrSjRadPADX6uKriqHCAIHIlFRGRO9esvEZmHIADtxwFN+wDrXwYubAZ2/Bc4t07qi+Pf2tYRWp0gCGjTwA1tGrgBALT5ehy9mo5DV9Jx8MptxN7MREJ6LhLSc/Hb0Rum85wUcvi7qeDnqoS/qwp+bioEuKrg76ZCM18Nmvm62PBTERHVTUxuqPpcA4ExK4BTvwGbXgeSTgKLewGtRwKRU4CACFtHaDOuKgUeaumHh1r6AQCy8vU4eu0ODl65jUNX0nHlVja0+YXI0xtMI7HK0jnEA0/3aIwB4X5wkHPOTSKiymByQzUjCEDEaKBJL2DDK9LaVKdWSFujHtKw8RaD7WLoeE24qBTo08IXfVr4mvblFhQiRatDcmY+Uorm2bn3eeyNTBy5egdHrt5BkLsTnoxshCc6B8NdzaHnREQVYXJD5uHiL03yd/MYcHARcGYNcG2/tHmEAF0nA+3/JS3USQAAtaMDGns7oLG3c5nvp2jz8fPBa1h+KAE3M/Lwwabz+GLbBTzWoQGe7h6CUD+WJRFRWVjPTeYV1FFaquGlU8ADMwAnD+DOVWDzm8Bn4cDm/wB3rtk6yjrBz1WFVwa0wP43H8JHI9siLMAV+Xojlh9KQP/P9+DJHw5hy5lkXEjJQmpWPvQGo61DJiKqFVhzQ5bhFgT0mwP0fA04+atUm3P7orRe1aFFQPNBQKdngKZ9ARlz7IqoFHKM6hSMxzs2wKH4dCzZH4/osynYezENey+mlThWo3SAu1oBD7Wj6dHT2RGtg9zQtbEnGng4cXQWEdk9JjdkWY5qoPNEoOPT0hpVBxYCV3YCcRulzb0h0PEpoP2TgMa3EhesvwRBQLcmXujWxAvX03Px099XsSMuFek5BcjM00MUgWxdIbJ1hbhxJ6/MawS4qdA5xBNdGnuia2NPNPPVMNkhIrvD5IasQyYDQvtL260LwLElQMwvQEYCsP0dYOd8IOwRqTYn5EGpozKVK9hTjbcfCcfbj4QDAAxGEdo8Pe7kFuBOrh6ZeQW4kyO9TtHm43hCBk7dyEBSZj7WFc2uDACezo7oHOKBLo290KGhO8ICXKFS1O/O30RU9zG5IevzaQ4Mmg/0nS11PD76o7Skw5k10uYVCnR6GogYIy39QPcllwnwcHascBHPvAIDTly/g8Px6ThyNR3Hrt1Bek4BtpxJwZYzKQAAB5mAFv4uaNvADW2C3NG2gRta+LtAwWHoRFSHMLkh21E4Ae3GSlvSKak259RvUt+cLf8Bts2VhpG3Gyf1zalnsx+bm5OjtEBo8SKhBYVGnE7MlJKd+HScvJGBtOwCnEnU4kyiFr9CmlnZ0UGG8ABXtA50QUaSgAvbL6HQCOgKjcjXG6ArNEJXaEC+Xnp0VSkwsmMD9G7hC7mMNXBEZH38a0G1Q0Bb4JHPgf7vALGrgKNLgORT0tpVZ/8ENH5A29FSouPb0tbR2gVHBxk6NPRAh4YemNyrKURRRFJmPk7dyMCpG5mIvZmJUzcykZmnR8z1DMRczwAgBxKu3Pfam04no4GHE57s1gijOgVXWKNERGRuTG6odlG6SP1uOj0DJMdKa1idWglkpwB/fyltQR2l2p4WQ20drV0RBAGB7k4IdHfCoNYBAABRFJGQnotTNzIRk5CO0xfiEdqkEZwcHaB0kEOlkEHpIIdSIYOq6PH0zUz8dvQGbtzJw/xN5/FZ9AUMbReI8ZEhaB3kZuuPSUT1AJMbqr3820h9c/rNAy5FAyd+AS5ukSYKvHkMDpv/g0h1M8g27QC8mkiTBbo3kh6d3G0dvV0QBAGNvJzRyMsZg8J9sNF4GUOGhEGhUJR7ztB2QZjRvwXWnbyJn/6+hrNJWvx29AZ+O3oDHRq6Y0L3EAxuHQBHB/bjISLLYHJDtZ+DI9DyYWnLviU1W8X8AiHlNHyzzgDHz5Q+R+UuJTkeIYBvOND2ccCziS2ir5ecHOUY3bkhRnUKxvGEO/jp72vYGJuE4wkZOJ4Qg1mq0/BxUcJZ6QAnhRxqRznUSgeoFXJpn6McGqUDGnmp0dLfBSFezlxbi4gqjckN1S0aH2lRzsgp0N+IQezWnxHR0BXyzOvSTMh3rgI5qUB+BpAUI21n1wK73peGmHcYD4RFSZ2ZyeIEQUDHRp7o2MgTbz8ShhWHr+OXQ9eQotVBm19Y6es4ymVo6qtBS38XNPdzkR79XRDopuI8PURUCpMbqrv8WuG614No02sI5Pc2kxTkSEs83LkK3IkHLm0HLu8Aru6VNpUb0OZxKdGpxyuXW5uviwov9g3F872bIi45C9m6QuQVGJBTUIjcAgNydYXI1RuQqzMgt8CAzDw9Lt/KxoWULOQWGHAuSYtzSdoS13RROaBrY08Mbh2AfuF+cHMqv7mMiOoPJjdkfxydAb9waQOAyKlAxnVp0sATvwCZCcCR76XNv62U5LQZKa2DRRankMuq1LHYaBRxMyMP55OzcCElC+eTsxCXrMWVWznIyi/EtnOp2HYuFQq5gAdDfTCkTQD6VzHRySswIF9v4KguIjvB5IbqB/dgoPebQM/XgfhdwPFlwPkN0nDzja9KC3t6NQN8WgA+LYsewwCvpoCD0tbR12symYBgTzWCPdXoH+5n2l9QaMSFlCxEn03BxtgkXEzNxo7zqdhxXkp0HmjmjcFtAjAw3B9uagVydIW4djsXV2/n4OrtHFxLk55fu52LZG0+UDRjc3M/DVr4Sc1eLfxcEOrnUmGiVGgwIj23AGlZBUjL1iEjTw9/VxWa+jjD09mRzWZENlArkpuFCxfi448/RnJyMiIiIvDVV1+hS5cuZR773XffYdmyZTh9+jQAoGPHjnj//ffLPZ6oBJkMaPqQtOWmS8PMjy8DUs8Ct85LG/68e7wglzoi+7SQNs8mdzsquwQAskouVVBYAGhvSM1lGQnSkPcmvTkDcw04Okg1QK2D3PBy/+a4mJKFDbFJ2BibhAsp2dgZdws7427hP7JYuKsdkZatu+8103MKcPBKOg5eSS+x399Vheb+Lgj2cII2vxC3s3VIy9YhLbsAd3ILIIplX89drUBTHw2a+jgXPWrQ1FeDYA8ndpAmsiCbJzcrV67EjBkz8M0336Br16744osvMHDgQMTFxcHXt/RCirt27cKYMWPQvXt3qFQqfPjhhxgwYADOnDmDoKAgm3wGqqPUnkC354Guk4HMG8CtuLsJTvFznVaaMfn2ReD8+pLnyx2lhT/vHYLu3lDq85NRlMQUJzNZiYBo/EcAAhDYHmjWD2jWFwjqxFmYayDUzwXT/VwwvV9zXErNwsbYZGyMTcL55CxTYuOhViDE2xkhXs5o5KUu8ahSyHEpNRtxKVLz14WULFxIzkJiZj6Stfmm2p2yyASp1sdbo4SrSoHEzDzczMhDRq4ex67dwbFrd0oc7yiXwVkphyAIkCZxFiAI0nWEoucA4CzKoQtIxKPtG3DNL6IqEESxvP/nsI6uXbuic+fOWLBgAQDAaDQiODgYL7zwAt588837nm8wGODh4YEFCxZg/Pjx9z1eq9XCzc0NmZmZcHV1NctnKKbX67Fx40YMGTKkwnlAyDwsXt6iCGQlA7fOFSU7cXdHZGVeB4yVH+0DAHBQScmPe0NAmyjVFt1L6QY06SktNdGsr3RcLVJXf99X03Kgzdejkacz3NRVj1ubr8fFlCxcSMlGYkYe3JwU8HFRwstZCW8XKaHxUDuWWmoir8CA+LQcXL6VXbTl4HJqNq6kZSNf/89Et2KuKgc81qEBnugSjJb+lfvvVqHBiJjrGdh7MQ0XU7PQzEeDtg3c0TbYDb4uqirdvz6oq7/vuqo65V2Vv982/d/EgoICHDt2DDNnzjTtk8lk6NevHw4cOFCpa+Tm5kKv18PTs+zqfZ1OB53ubnW0ViuNttDr9dDr9TX+DPcqvp65r0tls0p5O3kDDR+UtnsZCwFtIoSMa0DGNQh3rkHIKEp6HJ0Bt4YQ3RtBdA8G3BtBdAsGnH1LrnauTYIQvxOyKzsgXNkFIT8DOPeXtAEQvVvAGD4cxtYjAI/GlvuMlVRXf99Bbo4IcpM6Clcndic50DbQBW0DXco9xmgohNFQcp+DAIT6OCHUxwmAz91jjSKStPnILTAAIiBChFGUcmkRovQoAjq9Hv/behgntM5IzMzH0r+vYunfV9Eu2A2jOzXAkNZ+UDve/U+4KIq4lp6L/ZduY9+l2zgYfwfZurIT8AA3FdoGuaJNkBvaNnBF60A3uKjqd61hXf1911XVKe+qHGvTmpvExEQEBQXh77//RmRkpGn/66+/jt27d+PQoUP3vcaUKVOwZcsWnDlzBipV6f8bmTt3LubNm1dq//Lly6FWq83wKYjMQDTCIzcePtpY+GbFwiPnMmS4+3/36eqmuOHZHTfdu6JAYd4aR6rdjCJwIVPA3ykCYu8IMIpSgqySi+jkLSLERcRlrYC4TAHpupK1R84OIpq7iQh2FpGcJyAhW0BKHiCi5HECRPg6AYFqEQFqEf5OgL9ahLcKkLM/NNUSubm5GDt2bKVqbup0cvPBBx/go48+wq5du9C2bdsyjymr5iY4OBhpaWkWaZaKjo5G//79Wa1pBXZd3vmZEC5sguz0aghX90Ao6q8jCnKIjXvD2HoExBZDAEeN1UKy6/Kuhcoq71tZOvxxIhErj97A9Tt5pc5RyAV0bOiOHk298EAzb4QHuED2j+aybF0hziRqcepmJmJvSI83M8ruT+ToIEMTb2eE+joj1FeDUF8Nmng7I8jDCUoLLZ9RUGjE3otpWHcqCWcSs9ApxB2PtAlAt8YeFu2Ezd+3dVWnvLVaLby9vWt/s5S3tzfkcjlSUlJK7E9JSYG/v3+F537yySf44IMPsG3btnITGwBQKpVQKksP5VUoFBb7AVvy2lSaXZa3whvo+KS0ZSUDZ9YAp36DkHgcwpXtkF3ZDijUQJM+0vIUhbp7tnzAcM9zoxHwbw007gU06SUtR1GD4cl2Wd612L3lHeipwLS+zTGlTygOXLmNXw8n4Hp6LjqFeOKBUG90bexZoqmqLB4KBR5o7oQHmt8dVp+WrUPszUxcSJb6Fl1IycKl1Gzk6Q04nyzNLXQvQQAC3ZzQ0FONEG81GnpKHbMbeqrRyEsNF1XVfh9Go4ij1+5gbcxNbIxNQkbu3eaHa+m5+P14Irw1jhjSJgCPRgSiQ0OPUkmbufD3bV1VKe+qfC82TW4cHR3RsWNHbN++HcOGDQOKOhRv374d06ZNK/e8jz76CO+99x62bNmCTp06WTFiIhtw8ZdGdXV7Hki7JK2tFfsbkH4FiNtQuWtobwAXNkvPnX2Axj3vJjseIeWfZzQAeXeAnFsQMpPgnnNZSpj4H3+bkskE9GjmjR7NvM1yPW+NEn1a+KJPi7sjVI1GETfu5EmjxlKzcDElG3HJWbh2Owc5BQbczJBGhB24crvU9bycHRHiLSU8jb2c0cjbGY29nBHiXTLxOZ+sxdoTifjrZCJuZtytifJ1USIqIhBdGnti94Vb2BSbhLTsAiw7cA3LDlxDkLsTHmkbgKiIQLQKdC1zLiFRFJGvNyK3oBB5egNEEZDLBDjIBMiKHuX3bEaDsdwh/VT32LwH2YwZMzBhwgR06tQJXbp0wRdffIGcnBw8/fTTAIDx48cjKCgI8+fPBwB8+OGHmD17NpYvX46QkBAkJycDADQaDTQa61XRE9mEdzOgz0xpQsLE40DCQUDmIE00KFdKj8WbXCmN0BIN0nHxu4FrB4CcW8Dp36UNkIaxN+4pzb2TnSq9n5MmrdGVe9s0hN0BQC8A4sfvSbU/ge2lLaiD9FpeRsIjitIw+7Q44NaFu4956dIQ+IgnpNXfqdaRyQQ09FKjoZca/e6ZPFEURaRlFyAhPadoUsRcJNzOkR7Tc5GeU4DbRds/h8DjnsQnR1dYokZIo3TAoNb+GNYuCJFNvUyjzwa28se8R1th36U0/HUyEVvPpOBmRh6+3XMF3+65ghAvNdzVjsgrMCBXLy3pkVtgMCU0VSEX5HgndifcnRzh6qSA2z82VycHuDkpoFEq4KJygEblABel9KhROsDZ0cFiNUpUNTZPbkaPHo1bt25h9uzZSE5ORrt27bB582b4+Un/mBISEiCT3W1nXbRoEQoKCjBy5MgS15kzZw7mzp1r9fiJbEIQgKCO0lYZDbsBD0yXmqpuHAGu7JaSnZvHpDl5Tvyv4vOdPCGqvVCQmQxlYZY0s3PyKeD4T9L7cqXU9BXYAdD4SfMC3YoD0i4C+pyyr3nrPHBgAeDXRkpy2jwOuPiVfSzVGoIgwMdFCR8XJTo2Kj1KVZuvR8LtXMSn5eDa7RzEp+XiWtGs0GnZdxMfFPUR6t3CF8PaBaFvmG+5c/ko5DJTzVK+3oBdcalYdzIR28+l4urtXOB2boUxKx1kkAkCDEYRBlGEwVh21mMQBaTn6JGeU/0RUxqllOi4qIo3RcnnyrvPPTWO8NEo4a1RwkvjCIUF+xSJolivZsu2+Tw31sZ5buwHy9sMdFlSbc61/dJrZx9A4ws4e0vPnX0AtRcgV0jlvWEDhjzQForUWCDxxN0tP7P8e8gcAM+mgE9zwLtopme5o1RzdGEzYJD+0EGQSXP8RDwBtHy43q/cbo+/76x8Pa4VJT4Go4jeLXzgrq7+el5Z+XocupIOoyhC7egAJ0c51EWb9NwBTgp5qTmIxKIEp9AowihKj3n5Bdi0dRs6Rj6IHL2IzDw9tHn6Eo+ZeXpo8wuRrStEVn4hsnV6ZOdLzwvLSZiqws1JAW+NNHeSt4sSPhol1I7yoiY0GRzkd5vTFHKZqZlNbzAiI1eKL6M41lw9MvIKpH25ehQaRfi6KOHnqoK/qwr+bir4uarg56qEv6sKfm4qBLip7ttny1zsep4bIrIxpQvQfIC0VYYgAG7BgHcTIHyotE8Upf4/xYlO3h1pTS7vFoB3c8CzcdlNVq2GSUtgnFkDnFwB3DgMXIqWNqUrEBYlJVoGvTSvkKGg7OdyR0CpARxdih41pV87qKSlMgRBSqJKbHLpUeki9W+qR/93a20uKoVpyQxzXe/eJrPKEgRBShTuqShykgMeSqClv0uVk0lRFKErNBYlPIXIypeSHm2+9Dwr/24yVPxcm6/H7WxpPbLbOQUwGEVTAnX5Vjm1nTWUlJmPpMzyZ9oGgAYeTmjp74qwABeEBbiipb8LGnk5l0oQy2IwiriTW4Db2QXI1xsQEexuxuirhskNEdWMIEjJjFdTaXX1qlB7Ap0nStvty1KSc2qFtGRFzC+Wirh8Gn+gQSepH1FQJ6lPkYrzClHFBEGASiGHSiGHj0vVF9o1FiU2adk63Cpas6x4/bK8AiMMRiP0RhEGgwi90WiqdSo0SM/lMgHuTo5wVyvgppb6B7k7OUqPRa/lMgGpWTokZ+YjpWg5kZSipUVStPlI0eqQrSvEjTt5uHEnD9vO3R3FrFLI0MJPSnZC/VygNxiRlqUzJWa3sqSY03N0KK7AauDhhH1vPGTOYq4SJjdEVDt4NQUeegvoPRNIOCA1WRkLpWYtuaNU+1PquUJalLQgC9BlAwXZUlObLqvoedG+Qp3UMVoUix4NRY/3bPlaIDtZWkPMtI6YINU+FSc8vuHStXNuFXW+Lup4XdwJOzsVyM+QVpZv0gto3BtoFCnNWk1UDplMgIezIzycHRHqV/5M2DUV6O4EBJf/fkZugTT0P0mL88lZOJekRVxKFvL1Rpy8kYmTNypofr6Hh1oBjxo0N5oDkxsiql1kMiCkh7RZU0EukHRS6mR982hRZ+sEaYRXWlzVapKKO1z//RUgUwANOhclO72kRKmsZjoiG3NXO6JbEy90a+Jl2mcwirh2OwfnkrJwPlmLS6nZcFLI4e2iNPUP8tJIz300Sng6O9aKFe+Z3BARAYCjWqplaXR3tnRkpwI3j99Ndm5fApw8ijpbF3W81vje7Xzt7CPV0tw8DlzZJY1Iy7wOJPwtbbvmAwpnoFF3aVSZPhfQ50mPhfn3vM6Dgz4XQ3T5cDjrAKDoj4Wp20PRE0GQRqq5+AEugYBrAOBStN373MlDqgUryJHuUZBzz/NcqXbLUCDVUvm3kfonVVXmTeDyduDyDumavi0B31aAb5jUidyh6s01ZHtymYAmPho08dHg4bYBtg6n0pjcEBGVR+MLtBgkbVXh1RRo+/jdztbxu4uG3++R5vi5FH3fSwgAFACgu++hUnNa0skKLiYzzVd0X44uQMOuQMNIKQkL7AAoylhFvLAAuH4QuBgNXNoOpJ4p+f7FLffcXy6ViW+4tPmFS4vByhXSe7KiTZBLzY2yok7eKBrRl58hjcgr3vLuea3PAYK7AuHDbNs/yqAHspKAjOtSshjyAJsjbYjJDRGRpdzb2brTM9JSGCmnpaH3+jxpCQ2F0z2PKtNzveCI3Xv3o1evnlA4KHB3Rrqix+LX+hwgKwXISgS0SdJjVvLd53l3SiY2glwaQeaolu7lqJZeCzIgORbQaYFL26QNRXMYBXWQEp3grtKkjJe2SYlaQfa9H1aad6lZP0DjA6Sek7aUM1JyknZB2s6uNX85n/gZ2Pi6NMKu3Rip+a+ytU/pV4CL2yC/GI2eiZcgz/gBcHIHVG7SqD1V0XOVq/QIQZrxO/OGlMhk3pBq57KSSpazkyfQ5TmgyySphs+Wiifn9GpWb2rQmNwQEVmLTAYEtJW2+9HrkaO8JM0RVJN5bvT5Um2Rg0qqSZA7lj/c3WgoSr4OSM1o1w5IHaYTDkjbPzn7SMlMs35A04ek0W//JIpSspV6pijZOQuknpWSAtEgJXzGwqLnhVIMuGfOGAfVPQmG293Eo3iDAJxbJyVOsb9Jm2sQ0HY00G4s4B1aMp6CXODqvqJpB7ZJyU1Rw58HAMRfqX5Zyx2lexsKAO1NYPeHwP7/A9qNAyKnSkluVejzpe+qKglJfqY0JcPN49Is5jdPSMkYiuac8m4hTbjp3wbwaw34twWcvSq+pj6vqPP8Lem3pPGXPkstnouKyQ0RkT1TqABFYOWOlcmBgAhp6zZZSkxuX76b6Nw4ItVCNOsLNOsv/WGU3afzqCBI/X9cA6QkqDKMxSPaRGlh2Pt56G3pj3nML9LkkNqbwL7PpK1BZ2liyEKdlMxc3S8tLGv6zAqgYTcYmvTB0atadGzdHA76HECXWbIpLF8rPRoLAbcGRVuw9OjeUHp09pXKw2iQEq79X0oJxtEfgGNLpJql7i8BDcqYWdxokGbtvlHUv+vmcSkJFA1SU6Gzt7SpvaVkRF080aa31EyXePxuv7DSX4JUC6XLLEoyzwCnVt592yVQSni8QqXauOJEpngUYEFW2dd0D5bO8W4uLQ3j3VzaNH42ny+KyQ0REZVNEIr+aDUDOoy33n1lsrudqCtDEKSEoUFHYNB8IG4TELNcSmZuHJG2e7kFS4lWaH/TumpGvR7JdzZCbDOk5gvDyuRAq+FSP6Cr+4C/vwQubgXO/iltjR4AIqdI/XRuHpO2xJjylyopyJK2O/GVu797I6kpMbCD1FQYECHV2mkTpabHlFjpMTlWqrnKSpS2i1vLv6bcUUqmVO5S8pifIY0mzEiQOpLfy9FF6lD+zNb7J78WwuSGiIjsh4NSmv261TCpL1Lsb1JC4aiRkplm/aTaBWvULAgC0PhBaUs5K00NELsKuLZP2v7JUVO0GG1HacqAwA5Sn6ic21ItSm6aVJOSmybtK37toLq7iG1gh/KbmdyCpO3eDvK6LKlfVHIscOeq1NR37/IrxbVDSte7ZSaK0qK6xf2o0i4WbRekteoKsqS4bJTYgMkNERHZLRc/oPsL0mZrfuHA8EVSE9qhRUDs71LS0KDT3UVwvZuX3RHayUOqPbMEpYu0sG7DbpU/RxDuNpM16l7yvUKdVBtU0XpzVsDkhoiIyFrcgoAB70qbPXJQSnMb2ZjtpxEkIiIiMiMmN0RERGRXmNwQERGRXWFyQ0RERHaFyQ0RERHZFSY3REREZFeY3BAREZFdYXJDREREdoXJDREREdkVJjdERERkV5jcEBERkV1hckNERER2hckNERER2RUmN0RERGRXHGwdgLWJoggA0Gq1Zr+2Xq9Hbm4utFotFAqF2a9PJbG8rYvlbV0sb+tieVtXdcq7+O928d/xitS75CYrKwsAEBwcbOtQiIiIqIqysrLg5uZW4TGCWJkUyI4YjUYkJibCxcUFgiCY9dparRbBwcG4fv06XF1dzXptKo3lbV0sb+tieVsXy9u6qlPeoigiKysLgYGBkMkq7lVT72puZDIZGjRoYNF7uLq68h+HFbG8rYvlbV0sb+tieVtXVcv7fjU2xdihmIiIiOwKkxsiIiKyK0xuzEipVGLOnDlQKpW2DqVeYHlbF8vbulje1sXyti5Ll3e961BMRERE9o01N0RERGRXmNwQERGRXWFyQ0RERHaFyQ0RERHZFSY3ZrJw4UKEhIRApVKha9euOHz4sK1Dsht79uxBVFQUAgMDIQgC1q5dW+J9URQxe/ZsBAQEwMnJCf369cPFixdtFm9dNn/+fHTu3BkuLi7w9fXFsGHDEBcXV+KY/Px8TJ06FV5eXtBoNBgxYgRSUlJsFnNdtmjRIrRt29Y0kVlkZCQ2bdpkep9lbVkffPABBEHA9OnTTftY5uYzd+5cCIJQYmvZsqXpfUuWNZMbM1i5ciVmzJiBOXPm4Pjx44iIiMDAgQORmppq69DsQk5ODiIiIrBw4cIy3//oo4/w5Zdf4ptvvsGhQ4fg7OyMgQMHIj8/3+qx1nW7d+/G1KlTcfDgQURHR0Ov12PAgAHIyckxHfPyyy/jr7/+wqpVq7B7924kJibiscces2ncdVWDBg3wwQcf4NixYzh69CgeeughDB06FGfOnAFY1hZ15MgRfPvtt2jbtm2J/Sxz82rVqhWSkpJM2759+0zvWbSsRaqxLl26iFOnTjW9NhgMYmBgoDh//nybxmWPAIhr1qwxvTYajaK/v7/48ccfm/ZlZGSISqVS/PXXX20Upf1ITU0VAYi7d+8WxaKyVSgU4qpVq0zHnDt3TgQgHjhwwIaR2g8PDw/x+++/Z1lbUFZWlhgaGipGR0eLvXr1El966SVR5O/b7ObMmSNGRESU+Z6ly5o1NzVUUFCAY8eOoV+/fqZ9MpkM/fr1w4EDB2waW30QHx+P5OTkEuXv5uaGrl27svzNIDMzEwDg6ekJADh27Bj0en2J8m7ZsiUaNmzI8q4hg8GAFStWICcnB5GRkSxrC5o6dSoefvjhEmUL/r4t4uLFiwgMDESTJk0wbtw4JCQkAFYo63q3cKa5paWlwWAwwM/Pr8R+Pz8/nD9/3mZx1RfJyclAUXnfy8/Pz/QeVY/RaMT06dPRo0cPtG7dGigqb0dHR7i7u5c4luVdfbGxsYiMjER+fj40Gg3WrFmD8PBwxMTEsKwtYMWKFTh+/DiOHDlS6j3+vs2ra9euWLp0KVq0aIGkpCTMmzcPDz74IE6fPm3xsmZyQ0Rlmjp1Kk6fPl2ijZzMr0WLFoiJiUFmZiZWr16NCRMmYPfu3bYOyy5dv34dL730EqKjo6FSqWwdjt0bPHiw6Xnbtm3RtWtXNGrUCL/99hucnJwsem82S9WQt7c35HJ5qR7eKSkp8Pf3t1lc9UVxGbP8zWvatGlYv349du7ciQYNGpj2+/v7o6CgABkZGSWOZ3lXn6OjI5o1a4aOHTti/vz5iIiIwP/93/+xrC3g2LFjSE1NRYcOHeDg4AAHBwfs3r0bX375JRwcHODn58cytyB3d3c0b94cly5dsvjvm8lNDTk6OqJjx47Yvn27aZ/RaMT27dsRGRlp09jqg8aNG8Pf379E+Wu1Whw6dIjlXw2iKGLatGlYs2YNduzYgcaNG5d4v2PHjlAoFCXKOy4uDgkJCSxvMzEajdDpdCxrC+jbty9iY2MRExNj2jp16oRx48aZnrPMLSc7OxuXL19GQECA5X/fNe6STOKKFStEpVIpLl26VDx79qw4adIk0d3dXUxOTrZ1aHYhKytLPHHihHjixAkRgPjZZ5+JJ06cEK9duyaKoih+8MEHoru7u/jnn3+Kp06dEocOHSo2btxYzMvLs3Xodc7zzz8vurm5ibt27RKTkpJMW25urumYyZMniw0bNhR37NghHj16VIyMjBQjIyNtGndd9eabb4q7d+8W4+PjxVOnTolvvvmmKAiCuHXrVlFkWVvFvaOlRJa5Wb3yyivirl27xPj4eHH//v1iv379RG9vbzE1NVUULVzWTG7M5KuvvhIbNmwoOjo6il26dBEPHjxo65Dsxs6dO0UApbYJEyaIYtFw8FmzZol+fn6iUqkU+/btK8bFxdk67DqprHIGIC5ZssR0TF5enjhlyhTRw8NDVKvV4vDhw8WkpCSbxl1XPfPMM2KjRo1ER0dH0cfHR+zbt68psRFZ1lbxz+SGZW4+o0ePFgMCAkRHR0cxKChIHD16tHjp0iXT+5Ysa0GU/oNGREREZBfY54aIiIjsCpMbIiIisitMboiIiMiuMLkhIiIiu8LkhoiIiOwKkxsiIiKyK0xuiIiIyK4wuSEiAiAIAtauXWvrMIjIDJjcEJHNPfXUUxAEodQ2aNAgW4dGRHWQg60DICICgEGDBmHJkiUl9imVSpvFQ0R1F2tuiKhWUCqV8Pf3L7F5eHgARU1GixYtwuDBg+Hk5IQmTZpg9erVJc6PjY3FQw89BCcnJ3h5eWHSpEnIzs4uccyPP/6IVq1aQalUIiAgANOmTSvxflpaGoYPHw61Wo3Q0FCsW7fOCp+ciMyNyQ0R1QmzZs3CiBEjcPLkSYwbNw5PPPEEzp07BwDIycnBwIED4eHhgSNHjmDVqlXYtm1bieRl0aJFmDp1KiZNmoTY2FisW7cOzZo1K3GPefPmYdSoUTh16hSGDBmCcePGIT093eqflYhqyCzLbxIR1cCECRNEuVwuOjs7l9jee+89USxarXzy5Mklzunatav4/PPPi6IoiosXLxY9PDzE7Oxs0/sbNmwQZTKZmJycLIqiKAYGBopvvfVWuTEAEN9++23T6+zsbBGAuGnTJrN/XiKyLPa5IaJaoU+fPli0aFGJfZ6enqbnkZGRJd6LjIxETEwMAODcuXOIiIiAs7Oz6f0ePXrAaDQiLi4OgiAgMTERffv2rTCGtm3bmp47OzvD1dUVqampNf5sRGRdTG6IqFZwdnYu1UxkLk5OTpU6TqFQlHgtCAKMRqNFYiIiy2GfGyKqEw4ePFjqdVhYGAAgLCwMJ0+eRE5Ojun9/fv3QyaToUWLFnBxcUFISAi2b99u9biJyPpYc0NEtYJOp0NycnKJfQ4ODvD29gYArFq1Cp06dcIDDzyAX375BYcPH8YPP/wAABg3bhzmzJmDCRMmYO7cubh16xZeeOEFPPnkk/Dz8wMAzJ07F5MnT4avry8GDx6MrKws7N+/Hy+88IINPi0RWRKTGyKqFTZv3oyAgIAS+1q0aIHz588DRSOZVqxYgSlTpiAgIAC//vorwsPDAQBqtRpbtmzBSy+9hM6dO0OtVmPEiBH47LPPTNeaMGEC8vPz8fnnn+PVV1+Ft7c3Ro4caeVPSUTWIIjSKAEiolpLEASsWbMGw4YNs3UoRFQHsM8NERER2RUmN0RERGRX2OeGiGo9tp4TUVWw5oaIiIjsCpMbIiIisitMboiIiMiuMLkhIiIiu8LkhoiIiOwKkxsiIiKyK0xuiIiIyK4wuSEiIiK7wuSGiIiI7Mr/A+89khsfKWdUAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 13. Plot Training vs Validation Accuracy\n",
|
||
"plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
|
||
"plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
|
||
"plt.title('Model Accuracy Over Epochs')\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Accuracy')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# 14. Plot Training vs Validation Loss\n",
|
||
"plt.plot(history.history['loss'], label='Training Loss')\n",
|
||
"plt.plot(history.history['val_loss'], label='Validation Loss')\n",
|
||
"plt.title('Model Loss Over Epochs')\n",
|
||
"plt.xlabel('Epoch')\n",
|
||
"plt.ylabel('Loss')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "cbc75b13",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1m125/125\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1600x1400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Classification Report:\n",
|
||
"\n",
|
||
" precision recall f1-score support\n",
|
||
"\n",
|
||
" A 1.00 0.99 0.99 158\n",
|
||
" B 0.89 0.95 0.92 153\n",
|
||
" C 0.98 0.97 0.98 147\n",
|
||
" D 0.98 0.98 0.98 161\n",
|
||
" E 0.94 0.96 0.95 154\n",
|
||
" F 0.97 0.95 0.96 155\n",
|
||
" G 0.94 0.96 0.95 155\n",
|
||
" H 0.93 0.93 0.93 147\n",
|
||
" I 0.93 0.92 0.93 151\n",
|
||
" J 0.95 0.95 0.95 149\n",
|
||
" K 0.97 0.97 0.97 148\n",
|
||
" L 0.98 0.97 0.97 152\n",
|
||
" M 1.00 0.96 0.98 158\n",
|
||
" N 0.97 0.94 0.95 157\n",
|
||
" O 0.97 0.98 0.98 150\n",
|
||
" P 0.97 0.98 0.97 161\n",
|
||
" Q 0.97 0.99 0.98 157\n",
|
||
" R 0.94 0.94 0.94 151\n",
|
||
" S 0.99 0.97 0.98 150\n",
|
||
" T 0.95 0.97 0.96 159\n",
|
||
" U 0.99 0.99 0.99 163\n",
|
||
" V 0.99 0.96 0.97 153\n",
|
||
" W 0.94 1.00 0.97 150\n",
|
||
" X 0.97 0.96 0.96 157\n",
|
||
" Y 0.98 0.94 0.96 157\n",
|
||
" Z 1.00 0.98 0.99 147\n",
|
||
"\n",
|
||
" accuracy 0.96 4000\n",
|
||
" macro avg 0.96 0.96 0.96 4000\n",
|
||
"weighted avg 0.96 0.96 0.96 4000\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 15. Confusion Matrix and Classification Report\n",
|
||
"y_pred = np.argmax(model.predict(X_test), axis=1) # predicted class index\n",
|
||
"\n",
|
||
"cm = confusion_matrix(y_test, y_pred)\n",
|
||
"plt.figure(figsize=(16, 14))\n",
|
||
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
|
||
" xticklabels=label_encoder.classes_,\n",
|
||
" yticklabels=label_encoder.classes_)\n",
|
||
"plt.title('Confusion Matrix')\n",
|
||
"plt.ylabel('Actual')\n",
|
||
"plt.xlabel('Predicted')\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"print(\"\\nClassification Report:\\n\")\n",
|
||
"print(classification_report(y_test, y_pred, target_names=label_encoder.classes_))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9a2cc334-788f-4bc0-ac74-bdc6b3e7d126",
|
||
"metadata": {},
|
||
"source": [
|
||
"---"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|