{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "d18e160a",
"metadata": {
"id": "d18e160a"
},
"source": [
"# Learning-Based Closed Domain Chatbot (Deep Learning)\n",
"---\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9ZsdBYNrTfgg",
"metadata": {
"id": "9ZsdBYNrTfgg"
},
"source": [
"# 01 Import Library"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "MZMVLNyqYTCK",
"metadata": {
"id": "MZMVLNyqYTCK"
},
"source": [
"Tahapan pertama sebelum melakukan eksplorasi dan praproses pada data adalah memasukan library yang akan digunakan untuk menganalisa dataset dengan menggunakan metode Deep Learning seperti Neural Network dalam pengolahan teks, Chatbot dll. Library yang saya gunakan yaitu NumPy untuk komputasi matematika, Matplotlib untuk visualisasi model data, Natural Language Toolkit atau NLTK untuk pengolahan teks, Pandas untuk membaca data, serta Tensorflow untuk model pada data menggunakan algoritma LSTM dan Jaringan Syaraf Tiruan (Neural Network)."
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "df6e6af3",
"metadata": {
"id": "df6e6af3"
},
"outputs": [],
"source": [
"# Import Libraries\n",
"import json\n",
"import nltk\n",
"import time\n",
"import random\n",
"import string\n",
"import pickle\n",
"import numpy as np\n",
"import pandas as pd\n",
"from gtts import gTTS\n",
"from io import BytesIO\n",
"import tensorflow as tf\n",
"import IPython.display as ipd\n",
"import speech_recognition as sr \n",
"import matplotlib.pyplot as plt\n",
"from nltk.stem import WordNetLemmatizer\n",
"from tensorflow.keras.models import Model\n",
"from keras.utils.vis_utils import plot_model\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.layers import Input, Embedding, LSTM\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from tensorflow.keras.layers import Flatten, Dense, GlobalMaxPool1D"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "XFqXivzfTGhq",
"metadata": {
"id": "XFqXivzfTGhq"
},
"source": [
"Download NLTK Package"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "ZFHfBZ3mO1QE",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZFHfBZ3mO1QE",
"outputId": "1a8b138a-6720-480a-f8b7-36de4dbf655a"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to\n",
"[nltk_data] C:\\Users\\hilya\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to\n",
"[nltk_data] C:\\Users\\hilya\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n",
"[nltk_data] Downloading package omw-1.4 to\n",
"[nltk_data] C:\\Users\\hilya\\AppData\\Roaming\\nltk_data...\n",
"[nltk_data] Package omw-1.4 is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 27,
"metadata": {},
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}
],
"source": [
"# Package sentence tokenizer\n",
"nltk.download('punkt') \n",
"# Package lemmatization\n",
"nltk.download('wordnet')\n",
"# Package multilingual wordnet data\n",
"nltk.download('omw-1.4')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "xaowz3BO9B8l",
"metadata": {
"id": "xaowz3BO9B8l"
},
"source": [
"# 02 Data Acquisition\n",
"\n",
"Setelah kita mengetahui apa saja alur yang digunakan untuk membuat proyek AI Chatbot maka tahapan selanjutnya adalah mengunduh atau load dataset safebot.json"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "gLGpuyY9aMwW",
"metadata": {
"id": "gLGpuyY9aMwW"
},
"source": [
"**Load Dataset Json**\n",
"\n",
"Setelah import library, tahapan selanjutnya adalah me-load dataset yang telah disediakan. Dataset yang digunakan berupa format **.json** yang sangat cocok untuk membuat model Chatbot. \n",
"\n",
"Data Json merupakan data yang termasuk dalam *semi structured* yang dimana data ini menampung beberapa bagian data seperti **tag**, **pattern**, **context**, dan **response**. Data yang dipakai dalam proyek ini menggunakan dataset manual."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "JD4ILKqFZ_hf",
"metadata": {
"id": "JD4ILKqFZ_hf"
},
"outputs": [],
"source": [
"# Importing the dataset\n",
"with open('safebot.json') as content:\n",
" data1 = json.load(content)\n",
"\n",
"# Mendapatkan semua data ke dalam list\n",
"tags = [] # data tag\n",
"inputs = [] # data input atau pattern\n",
"responses = {} # data respon\n",
"words = [] # Data kata \n",
"classes = [] # Data Kelas atau Tag\n",
"documents = [] # Data Kalimat Dokumen\n",
"ignore_words = ['?', '!'] # Mengabaikan tanda spesial karakter\n",
"\n",
"for intent in data1['intents']:\n",
" responses[intent['tag']]=intent['responses']\n",
" for lines in intent['patterns']:\n",
" inputs.append(lines)\n",
" tags.append(intent['tag'])\n",
" for pattern in intent['patterns']:\n",
" w = nltk.word_tokenize(pattern)\n",
" words.extend(w)\n",
" documents.append((w, intent['tag']))\n",
" # add to our classes list\n",
" if intent['tag'] not in classes:\n",
" classes.append(intent['tag'])\n",
"\n",
"# Konversi data json ke dalam dataframe\n",
"data = pd.DataFrame({\"patterns\":inputs, \"tags\":tags})"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "5N0s7BObcv5-",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "5N0s7BObcv5-",
"outputId": "b068e17c-1af6-410e-b61e-423c7e229b41"
},
"outputs": [
{
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"215 Bagaimana cara beraktivitas di dunia maya deng... safe_online_activities\n",
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},
"execution_count": 29,
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}
],
"source": [
"# Cetak data keseluruhan\n",
"data "
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "5DFoJwcVdP52",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "5DFoJwcVdP52",
"outputId": "cc878daa-17f7-4b68-abb6-6a3c38bec113"
},
"outputs": [
{
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"output_type": "execute_result"
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],
"source": [
"# Cetak data baris pertama sampai baris kelima\n",
"data.head() "
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "HksM_GGVdenI",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "HksM_GGVdenI",
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"outputs": [
{
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"\n",
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" \n",
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" | \n",
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\n",
" \n",
" \n",
" \n",
" | 214 | \n",
" Bagaimana mencegah serangan pada WiFi rumah? | \n",
" jaringan_wifi | \n",
"
\n",
" \n",
" | 215 | \n",
" Bagaimana cara beraktivitas di dunia maya deng... | \n",
" safe_online_activities | \n",
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\n",
" \n",
" | 216 | \n",
" Bagaimana cara menjaga keamanan saat berintera... | \n",
" safe_online_activities | \n",
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" patterns tags\n",
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]
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Cetak data baris ke-70 sampai baris akhir\n",
"data.tail() "
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "vHnr2WFvebeJ",
"metadata": {
"id": "vHnr2WFvebeJ"
},
"source": [
"# 03 Preprocessing The Data\n",
"\n",
"Setelah kita meload data dan mengonversi data json menjadi dataframe. Tahapan selanjutnya adalah praproses pada dataset yang kita gunakan saat ini yaitu dengan cara:\n",
"\n",
"\n",
"\n",
"1. Remove Punctuations (Menghapus Punktuasi)\n",
"2. Lematization (Lematisasi)\n",
"3. Tokenization (Tokenisasi)\n",
"4. Apply Padding (Padding)\n",
"5. Encoding the Outputs (Konversi Keluaran Enkoding)\n",
"\n",
"Kelima tahapan pemrosesan teks ini dijelaskan pada bagian langkah selanjutnya."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "u_04NeXTIImg",
"metadata": {
"id": "u_04NeXTIImg"
},
"source": [
"## Remove Punctuations\n",
"\n",
"Tahapan praproses pada data teks yang pertama adalah menghapus punktuasi atau tanda baca seperti *special character* yaitu **'!'** (**tanda seru**) **','** (**tanda koma**) **'.'** (**tanda titik sebagai berhenti**) '**?**' (**tanda tanya**) dan tanda baca yang lain. Tahapan ini gunanya untuk mempermudah pemrosesan data teks yang akan kita olah."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "Gh-7EtrfhQgY",
"metadata": {
"id": "Gh-7EtrfhQgY"
},
"outputs": [],
"source": [
"# Removing Punctuations (Menghilangkan Punktuasi)\n",
"data['patterns'] = data['patterns'].apply(lambda wrd:[ltrs.lower() for ltrs in wrd if ltrs not in string.punctuation])\n",
"data['patterns'] = data['patterns'].apply(lambda wrd: ''.join(wrd))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5brR-qBLJDa_",
"metadata": {
"id": "5brR-qBLJDa_"
},
"source": [
"## Lemmatization (Lematisasi)\n",
"\n",
"Setelah menghapus punktuasi atau tanda baca, tahapan selanjutnya yaitu Lematisasi atau Lemmatization. **Apa itu Lematisasi?**\n",
"\n",
"Lematisasi atau Lemmatization adalah proses dimana merujuk pada melakukan sesuatu menggunakan vocabulary atau kosakata dan analisis morfologi kata-kata untuk menghilangkan *inflectional endings only* dan untuk mengembalikan bentuk *dictionary* (kata dalam kamus) dari sebuah kata yang dikenal sebagai ***lemma***. \n",
"\n",
"Contoh Lematisasi : **Menggunakan** (Kata Imbuhan) -> **Guna** (Kata Dasar) \n",
"\n",
"Dalam contoh berikut proses lematisasi awalnya data teks menggunakan kata imbuhan yaitu **Menggunakan** dimana **meng-** + **guna** (kata dasar yang berawalan vokal g) + **kan** (sebagai akhiran) diubah menjadi kata dasar yaitu '**Guna**'. \n",
"\n",
"Proses ini dimana menghilangkan Prefiks pada imbuhan (**Meng-**) dan Suffiks pada (**-kan**)."
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "22MVRGBsO9gX",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "22MVRGBsO9gX",
"outputId": "1ccd3caa-1b13-43d0-f97a-0bd37f8606dd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"139 unique lemmatized words ['(', ')', ',', '.', 'ada', 'afternoon', 'aja', 'aman', 'anak', 'apa', 'atau', 'bagaimana', 'bai', 'banyak', 'bentuk', 'beraktivitas', 'berikan', 'berinteraksi', 'bersifat', 'bikinan', 'bisa', 'bro', 'buat', 'buatan', 'bye', 'byee', 'cara', 'ciri-ciri', 'contoh', 'cyberbullying', 'cybercrime', 'dadah', 'dah', 'dari', 'data', 'ddos', 'dengan', 'denial-of-service', 'di', 'dilakukan', 'diri', 'distributed', 'dunia', 'email', 'emang', 'good', 'hai', 'hallo', 'halo', 'harus', 'hei', 'hi', 'hy', 'identitas', 'ilegal', 'informasi', 'itu', 'jaringan', 'jenis', 'jika', 'jumpa', 'kamu', 'kasih', 'kawan', 'keamanan', 'kejahatan', 'keuangan', 'kita', 'konten', 'lakukan', 'lo', 'makasih', 'malam', 'malware', 'maya', 'melalui', 'melindungi', 'memastikan', 'meminta', 'mencegah', 'menerima', 'mengatasi', 'mengetahui', 'menghentikan', 'menghindari', 'menjaga', 'merima', 'minta', 'morning', 'ok', 'online', 'pada', 'pagi', 'palsu', 'pelanggaran', 'pembuatmu', 'penciptamu', 'pencurian', 'penipuan', 'penyebaran', 'peretasan', 'pesan', 'phishing', 'pribadi', 'privasi', 'ransomware', 'rumah', 'saat', 'safebot', 'saja', 'sampai', 'saya', 'sebutkan', 'secara', 'see', 'seksual', 'selamat', 'sensitif', 'seperti', 'serangan', 'si', 'siang', 'siapa', 'siber', 'sih', 'sore', 'terhindar', 'terima', 'termasuk', 'teroris', 'thank', 'thanks', 'tinggal', 'tip', 'tolong', 'untuk', 'wifi', 'yang', 'you']\n"
]
}
],
"source": [
"lemmatizer = WordNetLemmatizer()\n",
"words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
"words = sorted(list(set(words)))\n",
"\n",
"print (len(words), \"unique lemmatized words\", words)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "zmZpqovQE1Zb",
"metadata": {
"id": "zmZpqovQE1Zb"
},
"source": [
"### Menyortir Data Kelas Tags"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "TK_v4Zw5P8rn",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TK_v4Zw5P8rn",
"outputId": "739a5170-9d3f-45bf-e19b-a73fe6de5e2d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"44 classes ['Safebot', 'ciri_ddos', 'ciri_malware', 'ciri_phishing', 'ciri_ransomware', 'contoh_cybercrime', 'cyberbullying', 'cyberbullying_prevention', 'cybercrime', 'data_pribadi', 'ddos', 'ddos_prevented', 'goodbye', 'greeting', 'identitas_palsu', 'identity_theft_prevented', 'illegal_content_prevented', 'jaringan_wifi', 'kejahatan_seks', 'kejahatan_siber_anak_prevention', 'keuangan_online', 'konten_ilegal', 'malware', 'malware_prevented', 'network_hacking_prevention', 'online_financial_fraud_prevention', 'online_scam_prevented', 'online_sexual_crime_prevention', 'online_terrorism_prevention', 'pelanggaran_privacy', 'pencipta', 'pencurian_identitas', 'penipuan_online', 'phishing', 'phishing_case', 'phishing_prevented', 'privacy_breach_prevented', 'ransomware', 'ransomware_prevented', 'retas_jaringan', 'safe_online_activities', 'siber_anak', 'terimakasih', 'teroris_online']\n"
]
}
],
"source": [
"# sort classes\n",
"classes = sorted(list(set(classes)))\n",
"print (len(classes), \"classes\", classes)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "u8s3ZKI9FCwo",
"metadata": {
"id": "u8s3ZKI9FCwo"
},
"source": [
"### Mencari Jumlah Keseluruhan Data Teks"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "Tv5lLFn1QCDP",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Tv5lLFn1QCDP",
"outputId": "6b2ed1d9-c4f8-42f9-a0bb-acae5db50f7b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1441 documents\n"
]
}
],
"source": [
"# documents = combination between patterns and intents\n",
"print (len(documents), \"documents\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "sVdv1gW5N7a6",
"metadata": {
"id": "sVdv1gW5N7a6"
},
"source": [
"## Tokenization (Tokenisasi)\n",
"\n",
"Setelah proses lematisasi dan mencari tahu data classes dan jumlah keseluruhan data patterns dengan intents-nya. Maka, tahapan selanjutnya proses tokenisasi. **Apa itu Tokenisasi?**\n",
"\n",
"Tokenisasi adalah suatu proses memberikan urutan karakter dan sebuah unit dokumen terdefinisi. Tokenisasi juga merupakan tugas untuk memecah kalimat menjadi bagian-bagian yang disebut dengan '**Token**' dan menghilangkan bagian tertentu seperti tanda baca.\n",
"\n",
"Contohnya: **Aku Pergi Ke Makassar** -> '**Aku**' '**Pergi**' '**Ke**' '**Makassar**'"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "Xr5aehymeQdi",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Xr5aehymeQdi",
"outputId": "de5aa76e-f55d-487f-e3b2-0773eccf9190"
},
"outputs": [
{
"data": {
"text/plain": [
"[[84],\n",
" [85],\n",
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" [92],\n",
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" [99],\n",
" [100],\n",
" [60],\n",
" [101],\n",
" [102, 60],\n",
" [103, 104],\n",
" [105, 106],\n",
" [107],\n",
" [108, 71],\n",
" [109, 60],\n",
" [110],\n",
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" [72, 73],\n",
" [112],\n",
" [72, 73, 113],\n",
" [1, 2, 74],\n",
" [49, 74],\n",
" [49, 114],\n",
" [49, 3, 9, 115, 75],\n",
" [75, 116, 49],\n",
" [117, 118, 49],\n",
" [49, 119],\n",
" [29, 2, 1],\n",
" [4, 10, 2, 1, 3],\n",
" [5, 29, 2, 1, 3],\n",
" [1, 2, 29],\n",
" [4, 10, 5, 1],\n",
" [4, 25, 26, 5, 1],\n",
" [1, 2, 4, 25, 26],\n",
" [29, 2, 1, 3],\n",
" [4, 10, 2, 1, 3],\n",
" [12, 50, 2, 1],\n",
" [12, 50, 2, 1, 3],\n",
" [5, 12, 50, 2, 1, 3],\n",
" [12, 50, 5, 1],\n",
" [12, 50, 5, 1, 3],\n",
" [1, 2, 12, 50],\n",
" [1, 30, 9, 61, 53, 43, 9, 54, 55],\n",
" [1, 30, 3, 9, 61, 53, 43, 9, 54, 55],\n",
" [53, 43, 9, 54, 55, 2, 1, 30],\n",
" [31, 44, 1, 30, 9, 61, 53, 43, 9, 54, 55],\n",
" [53, 43, 9, 54, 55, 2, 1, 30, 3],\n",
" [1, 32, 3, 62, 8, 29],\n",
" [1, 32, 3, 62, 8, 4, 25, 26],\n",
" [1, 32, 3, 62, 8, 4, 10],\n",
" [1, 32, 3, 63, 8, 29],\n",
" [1, 32, 3, 63, 8, 4, 25, 26],\n",
" [1, 32, 3, 63, 8, 4, 10],\n",
" [29, 2, 64, 1],\n",
" [4, 10, 2, 64, 1],\n",
" [4, 25, 26, 2, 64, 1],\n",
" [1, 32, 3, 51, 8, 29],\n",
" [31, 44, 51, 8, 29],\n",
" [1, 32, 3, 51, 8, 4, 25, 26],\n",
" [31, 44, 51, 8, 4, 25, 26],\n",
" [1, 32, 3, 51, 8, 4, 10],\n",
" [31, 44, 51, 8, 4, 10],\n",
" [16, 7, 2, 1],\n",
" [16, 7, 2, 1, 3],\n",
" [5, 16, 7, 2, 1, 3],\n",
" [16, 7, 5, 1],\n",
" [16, 7, 5, 1, 3],\n",
" [1, 2, 16, 7],\n",
" [6, 11, 76, 16, 7],\n",
" [6, 14, 16, 7],\n",
" [6, 17, 8, 16, 7],\n",
" [33, 12, 2, 1],\n",
" [33, 12, 2, 1, 3],\n",
" [5, 33, 12, 2, 1, 3],\n",
" [33, 12, 5, 1],\n",
" [33, 12, 5, 1, 3],\n",
" [1, 2, 33, 12],\n",
" [6, 11, 15, 24, 8, 33, 12],\n",
" [6, 17, 8, 33, 12],\n",
" [6, 14, 33, 12],\n",
" [34, 2, 1],\n",
" [34, 2, 1, 3],\n",
" [5, 34, 2, 1, 3],\n",
" [34, 5, 1],\n",
" [34, 5, 1, 3],\n",
" [1, 2, 34],\n",
" [6, 11, 120, 34],\n",
" [6, 15, 24, 8, 34],\n",
" [1, 9, 56, 52, 45, 121, 34],\n",
" [46, 35, 2, 1],\n",
" [46, 35, 2, 1, 3],\n",
" [5, 46, 35, 2, 1, 3],\n",
" [46, 35, 5, 1],\n",
" [46, 35, 5, 1, 3],\n",
" [1, 2, 46, 35],\n",
" [6, 11, 15, 35, 47],\n",
" [6, 17, 8, 46, 35],\n",
" [6, 14, 46, 35],\n",
" [36, 37, 38, 2, 1],\n",
" [36, 37, 38, 2, 1, 3],\n",
" [5, 36, 37, 38, 2, 1, 3],\n",
" [36, 37, 38, 5, 1],\n",
" [36, 37, 38, 5, 1, 3],\n",
" [1, 2, 36, 37, 38],\n",
" [6, 11, 14, 36, 37, 38],\n",
" [6, 15, 24, 8, 36, 37, 38],\n",
" [6, 17, 8, 36, 37, 38],\n",
" [39, 27, 2, 1],\n",
" [39, 27, 2, 1, 3],\n",
" [5, 39, 27, 2, 1, 3],\n",
" [39, 27, 5, 1],\n",
" [39, 27, 5, 1, 3],\n",
" [1, 2, 39, 27],\n",
" [6, 11, 15, 24, 8, 39, 27],\n",
" [6, 17, 8, 13, 39, 27],\n",
" [1, 9, 56, 52, 45, 14, 39, 27],\n",
" [4, 40, 7, 2, 1],\n",
" [4, 40, 7, 2, 1, 3],\n",
" [5, 4, 40, 7, 2, 1, 3],\n",
" [4, 40, 7, 5, 1],\n",
" [4, 40, 7, 5, 1, 3],\n",
" [1, 2, 4, 40, 7],\n",
" [6, 11, 15, 24, 8, 16, 40, 7],\n",
" [6, 17, 8, 16, 40, 7],\n",
" [1, 9, 56, 52, 45, 14, 16, 40, 7],\n",
" [4, 10, 28, 2, 1],\n",
" [4, 10, 28, 2, 1, 3],\n",
" [5, 4, 10, 28, 2, 1, 3],\n",
" [4, 10, 28, 5, 1],\n",
" [4, 10, 28, 5, 1, 3],\n",
" [1, 2, 4, 10, 28],\n",
" [6, 11, 15, 28, 8, 4, 10, 28],\n",
" [6, 14, 4, 10, 28],\n",
" [1, 9, 56, 52, 45, 76, 4, 10, 28],\n",
" [4, 41, 7, 2, 1],\n",
" [4, 41, 7, 2, 1, 3],\n",
" [5, 4, 41, 7, 2, 1, 3],\n",
" [4, 41, 7, 5, 1],\n",
" [4, 41, 7, 5, 1, 3],\n",
" [1, 2, 4, 41, 7],\n",
" [6, 11, 15, 24, 8, 4, 41, 7],\n",
" [6, 17, 8, 4, 41, 7],\n",
" [1, 9, 57, 52, 45, 14, 4, 41, 7],\n",
" [4, 42, 7, 2, 1],\n",
" [4, 42, 7, 2, 1, 3],\n",
" [5, 4, 42, 7, 2, 1, 3],\n",
" [4, 42, 7, 5, 1],\n",
" [4, 42, 7, 5, 1, 3],\n",
" [1, 2, 4, 42, 7],\n",
" [6, 11, 15, 24, 8, 4, 42, 7],\n",
" [6, 17, 8, 4, 42, 7],\n",
" [1, 9, 57, 52, 45, 14, 4, 42, 7],\n",
" [18, 2, 1],\n",
" [18, 2, 1, 3],\n",
" [5, 18, 2, 1, 3],\n",
" [18, 5, 1],\n",
" [18, 5, 1, 3],\n",
" [1, 2, 18],\n",
" [6, 58, 59, 18],\n",
" [1, 30, 48, 18],\n",
" [31, 44, 48, 18],\n",
" [6, 11, 15, 24, 8, 13, 18],\n",
" [6, 17, 8, 13, 18],\n",
" [6, 14, 13, 18],\n",
" [47, 122, 65, 9, 77, 66, 43, 47, 1, 9, 57, 47, 78],\n",
" [79, 47, 123, 65, 9, 124, 66, 43, 47, 1, 9, 57, 47, 78],\n",
" [6, 79, 125, 9, 77, 66, 43, 47, 126, 127, 128, 65],\n",
" [19, 2, 1],\n",
" [19, 2, 1, 3],\n",
" [5, 19, 2, 1, 3],\n",
" [19, 5, 1],\n",
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" [31, 44, 48, 19],\n",
" [6, 11, 15, 24, 8, 13, 19],\n",
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" [6, 14, 13, 19],\n",
" [20, 21, 22, 2, 1],\n",
" [20, 21, 22, 2, 1, 3],\n",
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" [20, 21, 22, 5, 1],\n",
" [20, 21, 22, 5, 1, 3],\n",
" [1, 2, 20, 21, 22],\n",
" [6, 11, 15, 24, 8, 13, 20, 21, 22],\n",
" [6, 17, 8, 13, 20, 21, 22],\n",
" [6, 14, 13, 20, 21, 22],\n",
" [6, 58, 59, 4, 20, 21, 22],\n",
" [1, 30, 48, 20, 21, 22],\n",
" [31, 44, 48, 20, 21, 22],\n",
" [23, 2, 1],\n",
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" [23, 5, 1],\n",
" [23, 5, 1, 3],\n",
" [1, 2, 23],\n",
" [6, 11, 15, 24, 8, 13, 23],\n",
" [6, 17, 8, 13, 23],\n",
" [6, 14, 13, 23],\n",
" [6, 58, 59, 4, 23],\n",
" [1, 30, 48, 23],\n",
" [31, 44, 48, 23],\n",
" [6, 11, 129, 80, 27, 81, 82],\n",
" [6, 14, 13, 130, 81, 82],\n",
" [6, 11, 67, 68, 25, 26, 69, 70],\n",
" [6, 11, 131, 80, 132, 133, 134, 7],\n",
" [1, 83, 45, 67, 68, 25, 26, 69, 70],\n",
" [31, 135, 83, 45, 67, 68, 25, 26, 69, 70]]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Tokenize the data (Tokenisasi Data)\n",
"tokenizer = Tokenizer(num_words=2000)\n",
"tokenizer.fit_on_texts(data['patterns'])\n",
"train = tokenizer.texts_to_sequences(data['patterns'])\n",
"train"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "LfEpFf0hPOUZ",
"metadata": {
"id": "LfEpFf0hPOUZ"
},
"source": [
"## Add Padding\n",
"\n",
"Setelah memproses tokenisasi yang dimana memecah kalimat menajdi bagian-bagian yang disebut token yang digunakan untuk mengolah data teks pada AI Chatbot maka tahapan selanjutnya adalah Padding.\n",
"**Apa itu Padding?**\n",
"\n",
"**Padding** adalah Suatu proses untuk mengubah setiap sequence agar memiliki panjang yang sama. Pada padding, setiap sequence dibuat sama panjang dengan menambahkan nilai 0 secara suffiks atau prefiks hingga mencapai panjang maksimum sequence. Selain itu padding juga dapat memotong sequence hingga panjangnya sesuai dengan panjang maksimum sequence. \n",
"\n",
"Padding juga adalah proses untuk membuat setiap kalimat pada teks memiliki panjang yang seragam. Sama seperti melakukan resize gambar, agar resolusi setiap gambar sama besar. Untuk menggunakan padding bisa impor library **pad_sequence**. Kemudian buat panggil fungsi pad_sequence() dan masukkan sequence hasil tokenisasi sebagai parameternya.\n",
"\n",
"Contohnya: `sequences_samapanjang = pad_sequences(sequences)`\n",
"\n",
"Yang nantinya akan dikeluarkan menjadi angka dengan awalan 0 seperti gambar dibawah ini.\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "5BQdPUTNvS1t",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5BQdPUTNvS1t",
"outputId": "3bc9e985-590e-44d4-e034-1af96b3a56e8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0 ... 0 0 84]\n",
" [ 0 0 0 ... 0 0 85]\n",
" [ 0 0 0 ... 0 0 86]\n",
" ...\n",
" [ 0 0 0 ... 133 134 7]\n",
" [ 0 0 0 ... 26 69 70]\n",
" [ 0 0 0 ... 26 69 70]]\n"
]
}
],
"source": [
"# Apply padding \n",
"x_train = pad_sequences(train)\n",
"print(x_train) # Padding Sequences"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "1Khg-ygkb0nD",
"metadata": {
"id": "1Khg-ygkb0nD"
},
"source": [
"Hasil setelah padding adalah setiap sequence memiliki panjang yang sama. Padding dapat melakukan ini dengan menambahkan 0 secara default pada awal sequence yang lebih pendek."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "qY0vxxwBPeJC",
"metadata": {
"id": "qY0vxxwBPeJC"
},
"source": [
"## Encoding Text\n",
"\n",
"Setelah tahapan proses Padding pada suatu teks maka proses terakhir dalam pemrosesan teks adalah tahapan Encoding. **Apa itu Encoding?**\n",
"\n",
"Encoding merupakan suatu konversi atau pengkodean yang dimana data kategorik seperti huruf atau data teks menjadi data numerik atau angka menyesuaikan dengan data label yang digunakan. Pada proses tahapan ini, encoding mengubah data teks pada kolom data tags menjadi data numerik dengan bahasa biner komputer yaitu 0 dan 1. \n",
"\n",
"Tujuan dari encoding ini adalah mempermudah saat proses komputasi data teks dan modelling."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "sczq--IpTYWa",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sczq--IpTYWa",
"outputId": "1d580bd4-927d-4690-c8af-eb31020d1687"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 12 12 12 12 12 12 12 12 12\n",
" 12 42 42 42 42 42 0 0 30 30 30 30 30 8 8 8 8 8 8 8 8 8 14 14\n",
" 14 14 14 14 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
" 32 32 32 32 32 32 26 26 26 31 31 31 31 31 31 15 15 15 6 6 6 6 6 6\n",
" 7 7 7 29 29 29 29 29 29 36 36 36 21 21 21 21 21 21 16 16 16 39 39 39\n",
" 39 39 39 24 24 24 20 20 20 20 20 20 25 25 25 41 41 41 41 41 41 19 19 19\n",
" 18 18 18 18 18 18 27 27 27 43 43 43 43 43 43 28 28 28 33 33 33 33 33 33\n",
" 3 3 3 35 35 35 34 34 34 22 22 22 22 22 22 2 2 2 23 23 23 10 10 10\n",
" 10 10 10 11 11 11 1 1 1 37 37 37 37 37 37 38 38 38 4 4 4 17 17 40\n",
" 40 40 40]\n"
]
}
],
"source": [
"# Encoding the outputs \n",
"le = LabelEncoder()\n",
"y_train = le.fit_transform(data['tags'])\n",
"print(y_train) #Label Encodings"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "D9rKggGCgnjT",
"metadata": {
"id": "D9rKggGCgnjT"
},
"source": [
"Tokenizer pada Tensorflow memberikan token unik untuk setiap kata yang berbeda. Dan juga padding dilakukan untuk mendapatkan semua data dengan panjang yang sama sehingga dapat mengirimkannya ke lapisan atau layer RNN. variabel target juga dikodekan menjadi nilai desimal."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "_hE21zfRhiNS",
"metadata": {
"id": "_hE21zfRhiNS"
},
"source": [
"# 04 Input Length, Output Length and Vocabulary\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "dbtBZXFvgvCB",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dbtBZXFvgvCB",
"outputId": "45fcb544-0e8e-43a0-f2b5-46ec2c54d22d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14\n"
]
}
],
"source": [
"# input length\n",
"input_shape = x_train.shape[1]\n",
"print(input_shape)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "310y6oNLhuzv",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "310y6oNLhuzv",
"outputId": "8359d50e-0610-452e-8cb0-faff24962fbd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of unique words : 135\n",
"output length: 44\n"
]
}
],
"source": [
"# define vocabulary\n",
"vocabulary = len(tokenizer.word_index)\n",
"print(\"number of unique words : \", vocabulary)\n",
"\n",
"# output length\n",
"output_length = le.classes_.shape[0]\n",
"print(\"output length: \", output_length)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "AInHhmVGict2",
"metadata": {
"id": "AInHhmVGict2"
},
"source": [
"**Input length** dan **output length** terlihat sangat jelas hasilnya. Mereka adalah untuk bentuk input dan bentuk output dari data train atau latih yang akan diproses pada algoritma Neural Network atau Jaringan Syaraf Tiruan.\n",
"\n",
"**Vocabulary Size** adalah untuk lapisan penyematan untuk membuat representasi vektor unik untuk setiap kata."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "1LxIElYjQbB7",
"metadata": {
"id": "1LxIElYjQbB7"
},
"source": [
"## Save Model Words & Classes\n",
"\n",
"Setelah dilakukan pemrosesan teks yang dilakukan lima tahap maka kita bisa simpan model pemrosesan teks tersebut dengan menggunakan format pickle. \n",
"\n",
"Hal ini biasanya digunakan untuk membuat hubungan model yang telah dilatih dengan model pemrosesan teks. "
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "wYV77QFbQVLP",
"metadata": {
"id": "wYV77QFbQVLP"
},
"outputs": [],
"source": [
"pickle.dump(words, open('words.pkl','wb'))\n",
"pickle.dump(classes, open('classes.pkl','wb'))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "dY9COWCwexgZ",
"metadata": {
"id": "dY9COWCwexgZ"
},
"source": [
"## Save Label Encoder & Tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "saM3QTSjewR4",
"metadata": {
"id": "saM3QTSjewR4"
},
"outputs": [],
"source": [
"pickle.dump(le, open('label_encoder.pkl','wb'))\n",
"pickle.dump(tokenizer, open('tokenizers.pkl','wb'))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "BI7OvNariInQ",
"metadata": {
"id": "BI7OvNariInQ"
},
"source": [
"# 05 Neural Network Model (LSTM)\n",
"\n",
"Setelah menyimpan model untuk pemrosesan teks, tahapan selanjutnya adalah melakukan modelling untuk Chatbot dengan menggunakan algoritma Neural Network atau Jaringan Syaraf Tiruan dengan algoritma LSTM (Long Short Term Memory). **Apa itu Algoritma LSTM?**\n",
"\n",
"**LSTM (Long Short Term Memory)** merupakan algoritma Deep Learning yang populer dan cocok digunakan untuk membuat prediksi dan klasifikasi yang berhubungan dengan waktu dan data teks. \n",
"\n",
"Algoritma ini bisa dikatakan pengembangan atau salah satu jenis dari algoritma RNN (Recurrent Neural Network). Dalam algoritma RNN, output dari langkah terakhir diumpankan kembali sebagai input pada langkah yang sedang aktif. Namun, algoritma RNN memiliki kekurangan yaitu tidak dapat memprediksi kata yang disimpan dalam memori jangka panjang.\n",
"\n",
"Nah, algoritma LSTM dirancang untuk mengatasi kelemahan tersebut, namun tetap mempertahankan kelebihan yang ada pada algoritma RNN dimana RNN mampu memberikan prediksi yang lebih akurat dari informasi terbaru.\n",
"\n",
"Algoritma LSTM pertama kali dikembangkan oleh Hochreiter dan Schmidhuber. Algoritma ini mampu menyimpan informasi untuk jangka waktu yang lama. Hal ini kemudian dapat digunakan untuk memproses, memprediksi, dan mengklasifikasikan informasi berdasarkan data deret waktu.\n",
"\n",
"Struktur algoritma LSTM terdiri atas neural network dan beberapa blok memori yang berbeda. Blok memori ini disebut sebagai cell. State dari cell dan hidden state akan diteruskan ke cell berikutnya.\n",
"\n",
"Seperti yang ditunjukkan pada gambar di bawah, bangun berbentuk persegi panjang berwarna biru adalah **ilustrasi cell** pada LSTM.\n",
"\n",
"\n",
"\n",
"Informasi yang dikumpulkan oleh algoritma LSTM kemudian akan disimpan oleh cell dan manipulasi memori dilakukan oleh komponen yang disebut dengan gate. Ada tiga jenis gate pada algoritma LSTM, di antaranya Forget gate, Input gate, dan Output gate. Sumber : [Trivusi](https://www.trivusi.web.id/2022/07/algoritma-lstm.html)\n",
"\n",
"Jaringan syaraf dalam kasus chatbot ini yang terdiri dari lapisan atau *layer* embedding yang merupakan salah satu hal yang paling kuat di bidang pemrosesan bahasa alami atau NLP. output atau keluaran dari lapisan (*layer*) embedding adalah input (masukan) data teks dari lapisan berulang (*recurrent*) dengan layer LSTM gate (Lapisan Gerbang **Long Shot Term Memory)**. Kemudian, output atau keluaran diratakan dan lapisan Dense digunakan dengan fungsi aktivasi **Softmax** yang dimana implementasi chatbot ini memiliki data label lebih dari dua kelas.\n",
"\n",
"Bagian utama dalam pemodelan chatbot ini adalah lapisan embedding yang memberikan nilai vektor yang sesuai untuk setiap kata dalam data teks yang telah dimasukkan."
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "2XBG3_reh2KY",
"metadata": {
"id": "2XBG3_reh2KY"
},
"outputs": [],
"source": [
"# Creating the model (Membuat Modeling)\n",
"i = Input(shape=(input_shape,))\n",
"x = Embedding(vocabulary+1,10)(i) # Layer Embedding\n",
"x = LSTM(10, return_sequences=True)(x) # Layer Long Short Term Memory\n",
"x = Flatten()(x) # Layer Flatten\n",
"x = Dense(output_length, activation=\"softmax\")(x) # Layer Dense\n",
"model = Model(i,x)\n",
"\n",
"# Compiling the model (Kompilasi Model)\n",
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "SO1blkS7ZzuH",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 533
},
"id": "SO1blkS7ZzuH",
"outputId": "4abb8624-1d6b-4cfa-f2fb-047153b1d904"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n"
]
}
],
"source": [
"# Visualization Plot Architecture Model (Visualisasi Plot Arsitektur Model)\n",
"plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "4hab_JHoopQI",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4hab_JHoopQI",
"outputId": "c2e95e71-0cb5-4bfc-c96f-1724b0cdb857"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model_1\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_2 (InputLayer) [(None, 14)] 0 \n",
" \n",
" embedding_1 (Embedding) (None, 14, 10) 1360 \n",
" \n",
" lstm_1 (LSTM) (None, 14, 10) 840 \n",
" \n",
" flatten_1 (Flatten) (None, 140) 0 \n",
" \n",
" dense_1 (Dense) (None, 44) 6204 \n",
" \n",
"=================================================================\n",
"Total params: 8,404\n",
"Trainable params: 8,404\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Menampilkan Parameter Model\n",
"model.summary() "
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "AHtOlCb8kGgZ",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AHtOlCb8kGgZ",
"outputId": "ee1389fb-cb3c-4673-826c-93e55d1a86a5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/400\n",
"7/7 [==============================] - 3s 8ms/step - loss: 3.7862 - accuracy: 0.0137 \n",
"Epoch 2/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 3.7766 - accuracy: 0.0274\n",
"Epoch 3/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 3.7683 - accuracy: 0.0457\n",
"Epoch 4/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 3.7550 - accuracy: 0.0548\n",
"Epoch 5/400\n",
"7/7 [==============================] - 0s 15ms/step - loss: 3.7381 - accuracy: 0.0548\n",
"Epoch 6/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 3.7127 - accuracy: 0.0685\n",
"Epoch 7/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 3.6768 - accuracy: 0.1050\n",
"Epoch 8/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 3.6352 - accuracy: 0.0685\n",
"Epoch 9/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 3.6040 - accuracy: 0.0685\n",
"Epoch 10/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 3.5821 - accuracy: 0.0685\n",
"Epoch 11/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 3.5601 - accuracy: 0.0685\n",
"Epoch 12/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 3.5319 - accuracy: 0.0685\n",
"Epoch 13/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 3.5060 - accuracy: 0.1461\n",
"Epoch 14/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 3.4778 - accuracy: 0.1050\n",
"Epoch 15/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 3.4414 - accuracy: 0.0822\n",
"Epoch 16/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 3.4014 - accuracy: 0.1324\n",
"Epoch 17/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 3.3601 - accuracy: 0.1461\n",
"Epoch 18/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 3.3136 - accuracy: 0.1507\n",
"Epoch 19/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 3.2651 - accuracy: 0.1142\n",
"Epoch 20/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 3.2158 - accuracy: 0.1142\n",
"Epoch 21/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 3.1665 - accuracy: 0.1142\n",
"Epoch 22/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 3.1219 - accuracy: 0.1142\n",
"Epoch 23/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 3.0729 - accuracy: 0.1187\n",
"Epoch 24/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 3.0261 - accuracy: 0.1735\n",
"Epoch 25/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 2.9801 - accuracy: 0.1918\n",
"Epoch 26/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 2.9324 - accuracy: 0.2055\n",
"Epoch 27/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 2.8878 - accuracy: 0.2009\n",
"Epoch 28/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 2.8413 - accuracy: 0.2009\n",
"Epoch 29/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 2.7944 - accuracy: 0.2100\n",
"Epoch 30/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 2.7494 - accuracy: 0.2192\n",
"Epoch 31/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 2.6981 - accuracy: 0.2237\n",
"Epoch 32/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 2.6561 - accuracy: 0.2329\n",
"Epoch 33/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 2.6142 - accuracy: 0.2511\n",
"Epoch 34/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 2.5661 - accuracy: 0.2740\n",
"Epoch 35/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 2.5187 - accuracy: 0.2831\n",
"Epoch 36/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 2.4767 - accuracy: 0.3059\n",
"Epoch 37/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 2.4294 - accuracy: 0.2968\n",
"Epoch 38/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 2.3876 - accuracy: 0.3288\n",
"Epoch 39/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 2.3481 - accuracy: 0.3607\n",
"Epoch 40/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 2.3066 - accuracy: 0.3699\n",
"Epoch 41/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 2.2642 - accuracy: 0.3607\n",
"Epoch 42/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 2.2236 - accuracy: 0.3790\n",
"Epoch 43/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 2.1819 - accuracy: 0.4338\n",
"Epoch 44/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 2.1419 - accuracy: 0.4247\n",
"Epoch 45/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 2.1052 - accuracy: 0.4155\n",
"Epoch 46/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 2.0636 - accuracy: 0.4201\n",
"Epoch 47/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 2.0394 - accuracy: 0.4201\n",
"Epoch 48/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 2.0011 - accuracy: 0.4566\n",
"Epoch 49/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.9674 - accuracy: 0.4749\n",
"Epoch 50/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.9322 - accuracy: 0.4612\n",
"Epoch 51/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.8930 - accuracy: 0.4795\n",
"Epoch 52/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.8618 - accuracy: 0.4840\n",
"Epoch 53/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.8282 - accuracy: 0.4932\n",
"Epoch 54/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.7980 - accuracy: 0.5205\n",
"Epoch 55/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.7663 - accuracy: 0.5251\n",
"Epoch 56/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.7406 - accuracy: 0.5114\n",
"Epoch 57/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.7131 - accuracy: 0.5251\n",
"Epoch 58/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.6811 - accuracy: 0.5753\n",
"Epoch 59/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.6570 - accuracy: 0.5616\n",
"Epoch 60/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.6195 - accuracy: 0.5799\n",
"Epoch 61/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 1.5957 - accuracy: 0.5982\n",
"Epoch 62/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.5654 - accuracy: 0.6164\n",
"Epoch 63/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 1.5427 - accuracy: 0.6073\n",
"Epoch 64/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 1.5173 - accuracy: 0.6301\n",
"Epoch 65/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.4885 - accuracy: 0.6438\n",
"Epoch 66/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.4643 - accuracy: 0.6575\n",
"Epoch 67/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 1.4448 - accuracy: 0.6575\n",
"Epoch 68/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.4184 - accuracy: 0.6758\n",
"Epoch 69/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 1.3996 - accuracy: 0.6895\n",
"Epoch 70/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 1.3737 - accuracy: 0.6941\n",
"Epoch 71/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.3498 - accuracy: 0.7169\n",
"Epoch 72/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.3220 - accuracy: 0.7260\n",
"Epoch 73/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.3009 - accuracy: 0.7443\n",
"Epoch 74/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.2806 - accuracy: 0.7352\n",
"Epoch 75/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.2589 - accuracy: 0.7443\n",
"Epoch 76/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 1.2339 - accuracy: 0.7489\n",
"Epoch 77/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 1.2139 - accuracy: 0.7671\n",
"Epoch 78/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 1.1919 - accuracy: 0.7626\n",
"Epoch 79/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 1.1718 - accuracy: 0.7626\n",
"Epoch 80/400\n",
"7/7 [==============================] - 0s 21ms/step - loss: 1.1546 - accuracy: 0.7626\n",
"Epoch 81/400\n",
"7/7 [==============================] - 0s 28ms/step - loss: 1.1402 - accuracy: 0.7580\n",
"Epoch 82/400\n",
"7/7 [==============================] - 0s 14ms/step - loss: 1.1211 - accuracy: 0.7900\n",
"Epoch 83/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 1.0972 - accuracy: 0.8037\n",
"Epoch 84/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 1.0821 - accuracy: 0.8174\n",
"Epoch 85/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.0632 - accuracy: 0.8265\n",
"Epoch 86/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.0446 - accuracy: 0.8311\n",
"Epoch 87/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.0291 - accuracy: 0.8128\n",
"Epoch 88/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 1.0091 - accuracy: 0.8174\n",
"Epoch 89/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.9929 - accuracy: 0.8356\n",
"Epoch 90/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.9792 - accuracy: 0.8356\n",
"Epoch 91/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.9624 - accuracy: 0.8356\n",
"Epoch 92/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.9497 - accuracy: 0.8356\n",
"Epoch 93/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.9367 - accuracy: 0.8402\n",
"Epoch 94/400\n",
"7/7 [==============================] - 0s 16ms/step - loss: 0.9232 - accuracy: 0.8402\n",
"Epoch 95/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.9100 - accuracy: 0.8539\n",
"Epoch 96/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.8911 - accuracy: 0.8447\n",
"Epoch 97/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.8747 - accuracy: 0.8493\n",
"Epoch 98/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.8605 - accuracy: 0.8539\n",
"Epoch 99/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.8454 - accuracy: 0.8539\n",
"Epoch 100/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.8369 - accuracy: 0.8539\n",
"Epoch 101/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.8221 - accuracy: 0.8630\n",
"Epoch 102/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.8091 - accuracy: 0.8584\n",
"Epoch 103/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.7959 - accuracy: 0.8721\n",
"Epoch 104/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.7858 - accuracy: 0.8721\n",
"Epoch 105/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.7729 - accuracy: 0.8767\n",
"Epoch 106/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.7639 - accuracy: 0.8721\n",
"Epoch 107/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.7501 - accuracy: 0.8813\n",
"Epoch 108/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.7401 - accuracy: 0.8813\n",
"Epoch 109/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.7310 - accuracy: 0.8721\n",
"Epoch 110/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.7177 - accuracy: 0.8904\n",
"Epoch 111/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.7069 - accuracy: 0.8813\n",
"Epoch 112/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.6980 - accuracy: 0.8995\n",
"Epoch 113/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.6882 - accuracy: 0.8950\n",
"Epoch 114/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.6874 - accuracy: 0.8904\n",
"Epoch 115/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.6753 - accuracy: 0.8995\n",
"Epoch 116/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.6601 - accuracy: 0.8950\n",
"Epoch 117/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.6495 - accuracy: 0.9087\n",
"Epoch 118/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.6415 - accuracy: 0.9178\n",
"Epoch 119/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.6313 - accuracy: 0.9224\n",
"Epoch 120/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.6231 - accuracy: 0.8995\n",
"Epoch 121/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.6133 - accuracy: 0.9132\n",
"Epoch 122/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.6049 - accuracy: 0.9315\n",
"Epoch 123/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5953 - accuracy: 0.9224\n",
"Epoch 124/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5885 - accuracy: 0.9269\n",
"Epoch 125/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5811 - accuracy: 0.9315\n",
"Epoch 126/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5727 - accuracy: 0.9406\n",
"Epoch 127/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.5671 - accuracy: 0.9361\n",
"Epoch 128/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5558 - accuracy: 0.9269\n",
"Epoch 129/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.5473 - accuracy: 0.9406\n",
"Epoch 130/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.5403 - accuracy: 0.9315\n",
"Epoch 131/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.5319 - accuracy: 0.9361\n",
"Epoch 132/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.5261 - accuracy: 0.9406\n",
"Epoch 133/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.5212 - accuracy: 0.9406\n",
"Epoch 134/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5108 - accuracy: 0.9498\n",
"Epoch 135/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.5044 - accuracy: 0.9543\n",
"Epoch 136/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.5000 - accuracy: 0.9498\n",
"Epoch 137/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.4932 - accuracy: 0.9498\n",
"Epoch 138/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4841 - accuracy: 0.9498\n",
"Epoch 139/400\n",
"7/7 [==============================] - 0s 19ms/step - loss: 0.4805 - accuracy: 0.9498\n",
"Epoch 140/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.4759 - accuracy: 0.9452\n",
"Epoch 141/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.4752 - accuracy: 0.9589\n",
"Epoch 142/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.4606 - accuracy: 0.9589\n",
"Epoch 143/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4544 - accuracy: 0.9635\n",
"Epoch 144/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4468 - accuracy: 0.9589\n",
"Epoch 145/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.4432 - accuracy: 0.9589\n",
"Epoch 146/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4357 - accuracy: 0.9635\n",
"Epoch 147/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.4299 - accuracy: 0.9498\n",
"Epoch 148/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4240 - accuracy: 0.9635\n",
"Epoch 149/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.4183 - accuracy: 0.9589\n",
"Epoch 150/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4151 - accuracy: 0.9635\n",
"Epoch 151/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4109 - accuracy: 0.9726\n",
"Epoch 152/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.4056 - accuracy: 0.9635\n",
"Epoch 153/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3983 - accuracy: 0.9726\n",
"Epoch 154/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 0.3961 - accuracy: 0.9680\n",
"Epoch 155/400\n",
"7/7 [==============================] - 0s 5ms/step - loss: 0.3887 - accuracy: 0.9680\n",
"Epoch 156/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3827 - accuracy: 0.9726\n",
"Epoch 157/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.3782 - accuracy: 0.9772\n",
"Epoch 158/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.3725 - accuracy: 0.9772\n",
"Epoch 159/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.3686 - accuracy: 0.9772\n",
"Epoch 160/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.3631 - accuracy: 0.9863\n",
"Epoch 161/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.3568 - accuracy: 0.9772\n",
"Epoch 162/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3550 - accuracy: 0.9817\n",
"Epoch 163/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3490 - accuracy: 0.9817\n",
"Epoch 164/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3438 - accuracy: 0.9817\n",
"Epoch 165/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3397 - accuracy: 0.9817\n",
"Epoch 166/400\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"7/7 [==============================] - 0s 8ms/step - loss: 0.3352 - accuracy: 0.9863\n",
"Epoch 167/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3315 - accuracy: 0.9772\n",
"Epoch 168/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3274 - accuracy: 0.9817\n",
"Epoch 169/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.3235 - accuracy: 0.9863\n",
"Epoch 170/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3191 - accuracy: 0.9863\n",
"Epoch 171/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.3160 - accuracy: 0.9817\n",
"Epoch 172/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3122 - accuracy: 0.9817\n",
"Epoch 173/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3094 - accuracy: 0.9863\n",
"Epoch 174/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3043 - accuracy: 0.9772\n",
"Epoch 175/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.3006 - accuracy: 0.9863\n",
"Epoch 176/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2983 - accuracy: 0.9817\n",
"Epoch 177/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.2949 - accuracy: 0.9863\n",
"Epoch 178/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2907 - accuracy: 0.9863\n",
"Epoch 179/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2864 - accuracy: 0.9863\n",
"Epoch 180/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2835 - accuracy: 0.9863\n",
"Epoch 181/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2792 - accuracy: 0.9863\n",
"Epoch 182/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2779 - accuracy: 0.9817\n",
"Epoch 183/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.2710 - accuracy: 0.9863\n",
"Epoch 184/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.2685 - accuracy: 0.9909\n",
"Epoch 185/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.2657 - accuracy: 0.9863\n",
"Epoch 186/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.2624 - accuracy: 0.9863\n",
"Epoch 187/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2585 - accuracy: 0.9863\n",
"Epoch 188/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2560 - accuracy: 0.9863\n",
"Epoch 189/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2533 - accuracy: 0.9863\n",
"Epoch 190/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.2502 - accuracy: 0.9863\n",
"Epoch 191/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.2487 - accuracy: 0.9909\n",
"Epoch 192/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.2457 - accuracy: 0.9817\n",
"Epoch 193/400\n",
"7/7 [==============================] - 0s 6ms/step - loss: 0.2425 - accuracy: 0.9909\n",
"Epoch 194/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.2385 - accuracy: 0.9909\n",
"Epoch 195/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.2354 - accuracy: 0.9909\n",
"Epoch 196/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.2332 - accuracy: 0.9863\n",
"Epoch 197/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.2295 - accuracy: 0.9863\n",
"Epoch 198/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2278 - accuracy: 0.9863\n",
"Epoch 199/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.2254 - accuracy: 0.9909\n",
"Epoch 200/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.2243 - accuracy: 0.9909\n",
"Epoch 201/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.2191 - accuracy: 0.9863\n",
"Epoch 202/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.2188 - accuracy: 0.9863\n",
"Epoch 203/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.2192 - accuracy: 0.9909\n",
"Epoch 204/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2182 - accuracy: 0.9817\n",
"Epoch 205/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.2109 - accuracy: 0.9863\n",
"Epoch 206/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.2082 - accuracy: 0.9863\n",
"Epoch 207/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.2062 - accuracy: 0.9909\n",
"Epoch 208/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2041 - accuracy: 0.9909\n",
"Epoch 209/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.2020 - accuracy: 0.9863\n",
"Epoch 210/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1978 - accuracy: 0.9909\n",
"Epoch 211/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.1971 - accuracy: 0.9909\n",
"Epoch 212/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.1943 - accuracy: 0.9909\n",
"Epoch 213/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1931 - accuracy: 0.9863\n",
"Epoch 214/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1899 - accuracy: 0.9863\n",
"Epoch 215/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1890 - accuracy: 0.9909\n",
"Epoch 216/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1864 - accuracy: 0.9909\n",
"Epoch 217/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.1838 - accuracy: 0.9909\n",
"Epoch 218/400\n",
"7/7 [==============================] - 0s 14ms/step - loss: 0.1821 - accuracy: 0.9909\n",
"Epoch 219/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1800 - accuracy: 0.9909\n",
"Epoch 220/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1788 - accuracy: 0.9909\n",
"Epoch 221/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1767 - accuracy: 0.9863\n",
"Epoch 222/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1755 - accuracy: 0.9863\n",
"Epoch 223/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1731 - accuracy: 0.9863\n",
"Epoch 224/400\n",
"7/7 [==============================] - 0s 14ms/step - loss: 0.1728 - accuracy: 0.9863\n",
"Epoch 225/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1692 - accuracy: 0.9863\n",
"Epoch 226/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1675 - accuracy: 0.9863\n",
"Epoch 227/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1660 - accuracy: 0.9909\n",
"Epoch 228/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.1641 - accuracy: 0.9909\n",
"Epoch 229/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.1624 - accuracy: 0.9909\n",
"Epoch 230/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1613 - accuracy: 0.9909\n",
"Epoch 231/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1595 - accuracy: 0.9863\n",
"Epoch 232/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1580 - accuracy: 0.9909\n",
"Epoch 233/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1561 - accuracy: 0.9909\n",
"Epoch 234/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1542 - accuracy: 0.9909\n",
"Epoch 235/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1533 - accuracy: 0.9909\n",
"Epoch 236/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1518 - accuracy: 0.9909\n",
"Epoch 237/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1521 - accuracy: 0.9909\n",
"Epoch 238/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1491 - accuracy: 0.9863\n",
"Epoch 239/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1469 - accuracy: 0.9954\n",
"Epoch 240/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1459 - accuracy: 0.9909\n",
"Epoch 241/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1442 - accuracy: 0.9909\n",
"Epoch 242/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1430 - accuracy: 0.9954\n",
"Epoch 243/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1410 - accuracy: 0.9954\n",
"Epoch 244/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1403 - accuracy: 0.9954\n",
"Epoch 245/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1391 - accuracy: 0.9954\n",
"Epoch 246/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1381 - accuracy: 0.9909\n",
"Epoch 247/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1367 - accuracy: 0.9909\n",
"Epoch 248/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1348 - accuracy: 0.9909\n",
"Epoch 249/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1340 - accuracy: 0.9909\n",
"Epoch 250/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1322 - accuracy: 0.9909\n",
"Epoch 251/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1310 - accuracy: 0.9954\n",
"Epoch 252/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1315 - accuracy: 0.9954\n",
"Epoch 253/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1297 - accuracy: 0.9909\n",
"Epoch 254/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1281 - accuracy: 0.9909\n",
"Epoch 255/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1264 - accuracy: 0.9954\n",
"Epoch 256/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1252 - accuracy: 0.9954\n",
"Epoch 257/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1243 - accuracy: 0.9954\n",
"Epoch 258/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1234 - accuracy: 0.9909\n",
"Epoch 259/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1223 - accuracy: 0.9954\n",
"Epoch 260/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1211 - accuracy: 0.9954\n",
"Epoch 261/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1207 - accuracy: 0.9954\n",
"Epoch 262/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1190 - accuracy: 0.9954\n",
"Epoch 263/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1182 - accuracy: 0.9954\n",
"Epoch 264/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1163 - accuracy: 0.9954\n",
"Epoch 265/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1156 - accuracy: 0.9954\n",
"Epoch 266/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1144 - accuracy: 0.9954\n",
"Epoch 267/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1137 - accuracy: 1.0000\n",
"Epoch 268/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1124 - accuracy: 0.9954\n",
"Epoch 269/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1114 - accuracy: 0.9954\n",
"Epoch 270/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1107 - accuracy: 1.0000\n",
"Epoch 271/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.1114 - accuracy: 0.9954\n",
"Epoch 272/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1086 - accuracy: 0.9954\n",
"Epoch 273/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1083 - accuracy: 1.0000\n",
"Epoch 274/400\n",
"7/7 [==============================] - 0s 14ms/step - loss: 0.1078 - accuracy: 0.9954\n",
"Epoch 275/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1063 - accuracy: 1.0000\n",
"Epoch 276/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.1052 - accuracy: 0.9954\n",
"Epoch 277/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1042 - accuracy: 0.9954\n",
"Epoch 278/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.1031 - accuracy: 1.0000\n",
"Epoch 279/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1025 - accuracy: 1.0000\n",
"Epoch 280/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1019 - accuracy: 0.9954\n",
"Epoch 281/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.1004 - accuracy: 1.0000\n",
"Epoch 282/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.1002 - accuracy: 1.0000\n",
"Epoch 283/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0992 - accuracy: 0.9954\n",
"Epoch 284/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0979 - accuracy: 1.0000\n",
"Epoch 285/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0974 - accuracy: 0.9954\n",
"Epoch 286/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0979 - accuracy: 0.9954\n",
"Epoch 287/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0958 - accuracy: 0.9954\n",
"Epoch 288/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0952 - accuracy: 0.9954\n",
"Epoch 289/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0945 - accuracy: 1.0000\n",
"Epoch 290/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0945 - accuracy: 1.0000\n",
"Epoch 291/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0936 - accuracy: 0.9954\n",
"Epoch 292/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0932 - accuracy: 0.9954\n",
"Epoch 293/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.0911 - accuracy: 0.9954\n",
"Epoch 294/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.0905 - accuracy: 0.9954\n",
"Epoch 295/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0903 - accuracy: 1.0000\n",
"Epoch 296/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.0895 - accuracy: 1.0000\n",
"Epoch 297/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0883 - accuracy: 1.0000\n",
"Epoch 298/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0874 - accuracy: 1.0000\n",
"Epoch 299/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0869 - accuracy: 0.9954\n",
"Epoch 300/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0860 - accuracy: 1.0000\n",
"Epoch 301/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0851 - accuracy: 1.0000\n",
"Epoch 302/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0847 - accuracy: 1.0000\n",
"Epoch 303/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0842 - accuracy: 1.0000\n",
"Epoch 304/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0832 - accuracy: 1.0000\n",
"Epoch 305/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0828 - accuracy: 1.0000\n",
"Epoch 306/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0823 - accuracy: 1.0000\n",
"Epoch 307/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0813 - accuracy: 1.0000\n",
"Epoch 308/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0810 - accuracy: 1.0000\n",
"Epoch 309/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0803 - accuracy: 1.0000\n",
"Epoch 310/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.0793 - accuracy: 1.0000\n",
"Epoch 311/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0789 - accuracy: 1.0000\n",
"Epoch 312/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0784 - accuracy: 1.0000\n",
"Epoch 313/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0777 - accuracy: 1.0000\n",
"Epoch 314/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0775 - accuracy: 1.0000\n",
"Epoch 315/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0766 - accuracy: 1.0000\n",
"Epoch 316/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0758 - accuracy: 1.0000\n",
"Epoch 317/400\n",
"7/7 [==============================] - 0s 13ms/step - loss: 0.0757 - accuracy: 1.0000\n",
"Epoch 318/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0749 - accuracy: 1.0000\n",
"Epoch 319/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0740 - accuracy: 1.0000\n",
"Epoch 320/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0734 - accuracy: 1.0000\n",
"Epoch 321/400\n",
"7/7 [==============================] - 0s 12ms/step - loss: 0.0732 - accuracy: 1.0000\n",
"Epoch 322/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0726 - accuracy: 1.0000\n",
"Epoch 323/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0721 - accuracy: 1.0000\n",
"Epoch 324/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0711 - accuracy: 1.0000\n",
"Epoch 325/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0708 - accuracy: 1.0000\n",
"Epoch 326/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0704 - accuracy: 1.0000\n",
"Epoch 327/400\n",
"7/7 [==============================] - 0s 15ms/step - loss: 0.0700 - accuracy: 1.0000\n",
"Epoch 328/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0694 - accuracy: 1.0000\n",
"Epoch 329/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0686 - accuracy: 1.0000\n",
"Epoch 330/400\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"7/7 [==============================] - 0s 11ms/step - loss: 0.0689 - accuracy: 1.0000\n",
"Epoch 331/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0674 - accuracy: 1.0000\n",
"Epoch 332/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0669 - accuracy: 1.0000\n",
"Epoch 333/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0676 - accuracy: 1.0000\n",
"Epoch 334/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0659 - accuracy: 1.0000\n",
"Epoch 335/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0661 - accuracy: 1.0000\n",
"Epoch 336/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0654 - accuracy: 1.0000\n",
"Epoch 337/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0656 - accuracy: 1.0000\n",
"Epoch 338/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0655 - accuracy: 1.0000\n",
"Epoch 339/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0634 - accuracy: 1.0000\n",
"Epoch 340/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0634 - accuracy: 1.0000\n",
"Epoch 341/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0629 - accuracy: 1.0000\n",
"Epoch 342/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0620 - accuracy: 1.0000\n",
"Epoch 343/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0620 - accuracy: 1.0000\n",
"Epoch 344/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0614 - accuracy: 1.0000\n",
"Epoch 345/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0610 - accuracy: 1.0000\n",
"Epoch 346/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0604 - accuracy: 1.0000\n",
"Epoch 347/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0601 - accuracy: 1.0000\n",
"Epoch 348/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0594 - accuracy: 1.0000\n",
"Epoch 349/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0591 - accuracy: 1.0000\n",
"Epoch 350/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0588 - accuracy: 1.0000\n",
"Epoch 351/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0583 - accuracy: 1.0000\n",
"Epoch 352/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0580 - accuracy: 1.0000\n",
"Epoch 353/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0573 - accuracy: 1.0000\n",
"Epoch 354/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0571 - accuracy: 1.0000\n",
"Epoch 355/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0566 - accuracy: 1.0000\n",
"Epoch 356/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 1.0000\n",
"Epoch 357/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0558 - accuracy: 1.0000\n",
"Epoch 358/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0556 - accuracy: 1.0000\n",
"Epoch 359/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0554 - accuracy: 1.0000\n",
"Epoch 360/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0552 - accuracy: 1.0000\n",
"Epoch 361/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0543 - accuracy: 1.0000\n",
"Epoch 362/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0543 - accuracy: 1.0000\n",
"Epoch 363/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0537 - accuracy: 1.0000\n",
"Epoch 364/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0532 - accuracy: 1.0000\n",
"Epoch 365/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0528 - accuracy: 1.0000\n",
"Epoch 366/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0524 - accuracy: 1.0000\n",
"Epoch 367/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 1.0000\n",
"Epoch 368/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0516 - accuracy: 1.0000\n",
"Epoch 369/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0514 - accuracy: 1.0000\n",
"Epoch 370/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0509 - accuracy: 1.0000\n",
"Epoch 371/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0508 - accuracy: 1.0000\n",
"Epoch 372/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0503 - accuracy: 1.0000\n",
"Epoch 373/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0499 - accuracy: 1.0000\n",
"Epoch 374/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0496 - accuracy: 1.0000\n",
"Epoch 375/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0491 - accuracy: 1.0000\n",
"Epoch 376/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0488 - accuracy: 1.0000\n",
"Epoch 377/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0486 - accuracy: 1.0000\n",
"Epoch 378/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0484 - accuracy: 1.0000\n",
"Epoch 379/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0480 - accuracy: 1.0000\n",
"Epoch 380/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0475 - accuracy: 1.0000\n",
"Epoch 381/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0470 - accuracy: 1.0000\n",
"Epoch 382/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0469 - accuracy: 1.0000\n",
"Epoch 383/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0467 - accuracy: 1.0000\n",
"Epoch 384/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0466 - accuracy: 1.0000\n",
"Epoch 385/400\n",
"7/7 [==============================] - 0s 15ms/step - loss: 0.0461 - accuracy: 1.0000\n",
"Epoch 386/400\n",
"7/7 [==============================] - 0s 16ms/step - loss: 0.0455 - accuracy: 1.0000\n",
"Epoch 387/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0453 - accuracy: 1.0000\n",
"Epoch 388/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0449 - accuracy: 1.0000\n",
"Epoch 389/400\n",
"7/7 [==============================] - 0s 11ms/step - loss: 0.0448 - accuracy: 1.0000\n",
"Epoch 390/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 1.0000\n",
"Epoch 391/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0444 - accuracy: 1.0000\n",
"Epoch 392/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0438 - accuracy: 1.0000\n",
"Epoch 393/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0436 - accuracy: 1.0000\n",
"Epoch 394/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0435 - accuracy: 1.0000\n",
"Epoch 395/400\n",
"7/7 [==============================] - 0s 7ms/step - loss: 0.0435 - accuracy: 1.0000\n",
"Epoch 396/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0429 - accuracy: 1.0000\n",
"Epoch 397/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0424 - accuracy: 1.0000\n",
"Epoch 398/400\n",
"7/7 [==============================] - 0s 8ms/step - loss: 0.0422 - accuracy: 1.0000\n",
"Epoch 399/400\n",
"7/7 [==============================] - 0s 9ms/step - loss: 0.0420 - accuracy: 1.0000\n",
"Epoch 400/400\n",
"7/7 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 1.0000\n"
]
}
],
"source": [
"# Training the model (Latih model data sampai 400 kali)\n",
"train = model.fit(x_train, y_train, epochs=400)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "BfQP5TaPkGzB",
"metadata": {
"id": "BfQP5TaPkGzB"
},
"source": [
"# 06 Model Analysis\n",
"\n",
"Setelah menjalankan pelatihan model dengan algoritma Neural Neural dan LSTM serta telah mengetahui hasil akurasi pada step terakhir. Maka, tahapan selanjutnya adalah menganalisa model dengan visualisasi plot akurasi dan loss untuk melihat hasil akurasi dari algoritma pelatihan model Neural Network dengan LSTM tersebut."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "FEz3P5djksfa",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 459
},
"id": "FEz3P5djksfa",
"outputId": "4e27fba0-9523-4fc2-8e46-350f0c3baae8"
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting model Accuracy and Loss (Visualisasi Plot Hasil Akurasi dan Loss)\n",
"# Plot Akurasi\n",
"plt.figure(figsize=(14, 5))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(train.history['accuracy'],label='Training Set Accuracy')\n",
"plt.legend(loc='lower right')\n",
"plt.title('Accuracy')\n",
"# Plot Loss\n",
"plt.subplot(1, 2, 2)\n",
"plt.plot(train.history['loss'],label='Training Set Loss')\n",
"plt.legend(loc='upper right')\n",
"plt.title('Loss')\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "odsh6gL8CAfE",
"metadata": {
"id": "odsh6gL8CAfE"
},
"source": [
"Terlihat bahwa model pelatihan chatbot dengan algoritma Neural Network + LSTM menghasilkan model yang baik dan tidak terjadi overfitting atau underfitting. Sehingga, model ini layak dilakukan pengujian dan evaluasi model chatbot yang diperoleh."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "WcOYpQfuln4T",
"metadata": {
"id": "WcOYpQfuln4T"
},
"source": [
"# 07 Testing Chatbot Dan Tambahkan Suara Pada Chatbot\n",
"\n",
"Setelah mengetahui hasil dari akurasi dan loss pada model yang telah ditetapkan dengan algoritma Neural Network dan LSTM. Maka, tahapan selanjutnya adalah menguji atau testing pada chatbot yang telah dilatih sebelumnya dan melihat apakah sesuai atau tidak pada saat kita masukan teks kalimat pertanyaannya. \n",
"\n",
"Pada pengujian ini menggunakan metode input atau masukan sesuai dengan kalimat pertanyaan yang dimasukkan."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "fao4rG4rlh1D",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 753
},
"id": "fao4rG4rlh1D",
"outputId": "b26f1f8f-257f-411d-c21f-e3949d0daf6e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"👨🦰 Kamu : halo\n",
"1/1 [==============================] - 0s 406ms/step\n",
"🤖 SafeBot : Hai! Safebot di sini. Mau tau informasi tentang apa nih?\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"\n",
"👨🦰 Kamu : apa itu cybercrime\n",
"1/1 [==============================] - 0s 26ms/step\n",
"🤖 SafeBot : Cybercrime (kejahatan dunia maya) adalah kejahatan yang dilakukan di dunia maya (internet) dan mengakibatkan kerugian atau dampak negatif pada korban. Kejahatan dunia maya dapat dilakukan oleh individu, kelompok atau organisasi dan dapat melibatkan pencurian identitas, penipuan, peretasan, serangan malware, pencurian data, dan aktivitas ilegal lainnya yang dilakukan melalui komputer atau jaringan internet.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"\n",
"👨🦰 Kamu : bagaimana menghindari penipuan online?\n",
"1/1 [==============================] - 0s 25ms/step\n",
"🤖 SafeBot : Untuk menghindari penipuan online, berikut adalah beberapa langkah yang dapat Anda ikuti:\n",
"\n",
"1. Berhati-hatilah terhadap penawaran yang terlalu bagus untuk menjadi kenyataan. Jika sesuatu terdengar terlalu bagus untuk dipercaya, mungkin itu bukanlah kenyataan.\n",
"\n",
"2. Selalu periksa reputasi dan keandalan penjual sebelum melakukan pembelian online. Baca ulasan dari pelanggan sebelumnya untuk mendapatkan informasi yang lebih baik.\n",
"\n",
"3. Jangan pernah berikan informasi pribadi atau keuangan Anda kepada seseorang yang tidak Anda kenal atau melalui situs web yang tidak terpercaya.\n",
"\n",
"4. Hati-hati terhadap email atau pesan yang mencoba meminta informasi pribadi atau mengarahkan Anda ke situs web yang mencurigakan. Jangan klik tautan yang mencurigakan atau unduh lampiran dari sumber yang tidak terpercaya.\n",
"\n",
"5. Gunakan metode pembayaran yang aman dan dapat dipercaya saat melakukan transaksi online. Hindari menggunakan transfer bank langsung kepada pihak yang tidak Anda kenal.\n",
"\n",
"6. Selalu periksa tagihan kartu kredit dan transaksi keuangan Anda secara teratur untuk mendeteksi aktivitas yang mencurigakan.\n",
"\n",
"7. Gunakan kecerdasan dan kewaspadaan saat berinteraksi dengan orang asing atau penjual online. Jangan terburu-buru dalam mengambil keputusan dan pastikan Anda merasa nyaman sebelum melakukan transaksi.\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"\n",
"👨🦰 Kamu : bye\n",
"1/1 [==============================] - 0s 26ms/step\n",
"🤖 SafeBot : Semoga harimu menyenangkan!\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================================\n",
"\n"
]
}
],
"source": [
"# Membuat Input Chat\n",
"while True:\n",
" texts_p = []\n",
" prediction_input = input('👨🦰 Kamu : ')\n",
" \n",
" # Menghapus punktuasi dan konversi ke huruf kecil\n",
" prediction_input = [letters.lower() for letters in prediction_input if letters not in string.punctuation]\n",
" prediction_input = ''.join(prediction_input)\n",
" texts_p.append(prediction_input)\n",
"\n",
" # Tokenisasi dan Padding\n",
" prediction_input = tokenizer.texts_to_sequences(texts_p)\n",
" prediction_input = np.array(prediction_input).reshape(-1)\n",
" prediction_input = pad_sequences([prediction_input],input_shape)\n",
"\n",
" # Mendapatkan hasil keluaran pada model \n",
" output = model.predict(prediction_input)\n",
" output = output.argmax()\n",
"\n",
" # Menemukan respon sesuai data tag dan memainkan voice bot\n",
" response_tag = le.inverse_transform([output])[0]\n",
" print(\"🤖 SafeBot : \", random.choice(responses[response_tag]))\n",
" tts = gTTS(random.choice(responses[response_tag]), lang='id')\n",
" # Simpan model voice bot ke dalam Google Drive\n",
" tts.save('SafeBot.wav')\n",
" time.sleep(0.08)\n",
" # Load model voice bot from Google Drive\n",
" ipd.display(ipd.Audio('SafeBot.wav', autoplay=True))\n",
" print(\"=\"*60 + \"\\n\")\n",
" # Tambahkan respon 'goodbye' agar bot bisa berhenti\n",
" if response_tag == \"goodbye\":\n",
" break"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "PpFwQ9gWmWtk",
"metadata": {
"id": "PpFwQ9gWmWtk"
},
"source": [
"# 08 Save The Model\n",
"\n",
"Setelah pengujian Chatbot telah disesuaikan dengan kalimat dan jawabannya. Maka, model chatbot bisa disimpan dengan format .h5 atau .pkl (pickle) untuk penggunaan aplikasi AI Chatbot dengan website atau sistem Android. Penyimpanan file model bisa langsung secara transient atau bisa taruh di Google Drive."
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "MxdDHujDmaC0",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 130
},
"id": "MxdDHujDmaC0",
"outputId": "c24afa27-969e-46ca-c0ce-3eeef483513c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model Saved Successfully!\n"
]
}
],
"source": [
"# Simpan model dalam bentuk format file .h5 atau .pkl (pickle)\n",
"model.save('chatbot_model.h5')\n",
"\n",
"print('Model Saved Successfully!')"
]
},
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