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6057CEM-Artificial-Neural-Networks/All Steps.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 80,
"id": "3620533f-384a-4012-a0de-2ac165b3b0f5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import re\n",
"import pandas as pd\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.manifold import TSNE\n",
"from sklearn.model_selection import train_test_split\n",
"from nltk.corpus import stopwords\n",
"from imblearn.over_sampling import SMOTE\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from gensim.models import Word2Vec\n",
"from gensim.test.utils import datapath\n",
"from gensim.models.word2vec import Word2Vec\n",
"from bayes_opt import BayesianOptimization\n",
"from tensorflow.keras.callbacks import EarlyStopping\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Embedding\n",
"from keras.layers import LSTM\n",
"from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
"from tensorflow.keras.layers import Input, Embedding, LSTM, Dense, SpatialDropout1D, Bidirectional, Flatten, Dropout, Conv1D, MaxPooling1D\n",
"from tensorflow.keras.models import Model\n",
"import itertools\n",
"from tensorflow.keras.optimizers import Adam\n",
"import seaborn as sns\n",
"import tensorflow.keras.backend as K\n",
"import sklearn.metrics\n",
"from fastai.text import *"
]
},
{
"cell_type": "markdown",
"id": "90b0a6d9-4a24-4df2-963f-05b56a14d7f7",
"metadata": {
"tags": []
},
"source": [
"Load Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1a0211fd-9e5b-4699-9b82-17add295b379",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def load_data():\n",
" data = pd.read_csv(r\"Tweets.csv\")\n",
" return data"
]
},
{
"cell_type": "markdown",
"id": "56a741e3-b5c1-425a-86c8-db66a60363c6",
"metadata": {},
"source": [
"Pre-Process dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1037f713-a61f-47d6-8705-cee342014f76",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def clean_dataset(data):\n",
" # Dataset processing\n",
" le = LabelEncoder()\n",
" data['airline_sentiment'] = le.fit_transform(data['airline_sentiment'])\n",
" columns_drop = ['tweet_id', 'airline_sentiment_confidence', 'negativereason',\n",
" 'negativereason_confidence', 'airline', 'airline_sentiment_gold',\n",
" 'name', 'negativereason_gold', 'retweet_count', 'tweet_coord',\n",
" 'tweet_created', 'tweet_location', 'user_timezone']\n",
" data = data.drop(columns_drop, axis=1)\n",
" data['text'] = data['text'].apply(lambda x: clean_text(x))\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7a262224-abe9-4232-81f1-7a1540767e77",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def clean_text(text):\n",
" # Clean the text\n",
" text = re.sub(r'http\\S+|#\\w+|@\\w+|[^\\w\\s]+', '', text)\n",
" text = text.lower()\n",
" stop_words = set(stopwords.words('english'))\n",
" words = [word for word in text.split() if word not in stop_words]\n",
" text = ' '.join(words)\n",
" return text"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "030c8ad7-82e6-4659-a6f8-5493c8bba67d",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def plot_sentiment_count(data):\n",
" sentiment_count = data.groupby('airline_sentiment').count()\n",
" sentiment_count.reset_index(inplace=True)\n",
" sentiments = ['Negative', 'Neutral', 'Positive']\n",
" sentiment_count['airline_sentiment'] = sentiment_count['airline_sentiment'].apply(lambda x: sentiments[x])\n",
" \n",
" plt.figure(figsize=(10,5))\n",
" plt.bar(sentiment_count['airline_sentiment'], sentiment_count['text'])\n",
" plt.xlabel('Sentiment')\n",
" plt.ylabel('Count')\n",
" plt.title('Sentiment Count')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "851fab5b-765a-49d7-8523-bcb23afa413c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def tokenize_text(data):\n",
" \"\"\"Tokenize the text and pad the sequences\"\"\"\n",
" tokenizer = Tokenizer(num_words=13113)\n",
" tokenizer.fit_on_texts(data['text'].values)\n",
" X = tokenizer.texts_to_sequences(data['text'].values)\n",
" X = pad_sequences(X, maxlen=100)\n",
" return X, tokenizer"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4ab39680-5617-45a2-97e1-bcd4a701b69b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def create_embedding_matrix(tokenizer, model):\n",
" \"\"\"Create the embedding matrix from the word2vec model\"\"\"\n",
" embedding_dim = model.vector_size\n",
" word_index = tokenizer.word_index\n",
" embedding_matrix = np.zeros((len(word_index) + 1, embedding_dim))\n",
" for word, i in word_index.items():\n",
" if word in model.wv:\n",
" embedding_matrix[i] = model.wv[word]\n",
" return embedding_matrix"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ae77c550-072d-4183-818e-8b308ef0d663",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def word2vec_model(data):\n",
" \"\"\"Train a word2vec model on the text data\"\"\"\n",
" sentences = data['text'].apply(lambda x: x.split()).tolist()\n",
" model = Word2Vec(sentences, vector_size=100, window=5, min_count=2, workers=4)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "c785404a-6124-41df-9d0d-f759337955e9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def create_lstm_model(embedding_matrix, max_length, num_words, embedding_dim, classes, num_hidden_layers=3):\n",
" \"\"\"Create an LSTM model with a specified number of hidden layers\"\"\"\n",
" model = tf.keras.Sequential()\n",
" model.add(tf.keras.layers.Embedding(13113, embedding_dim, input_length=max_length, weights=[embedding_matrix], trainable=False))\n",
" for i in range(num_hidden_layers):\n",
" model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)))\n",
" model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)))\n",
" model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
" model.add(tf.keras.layers.Dropout(0.5))\n",
" model.add(tf.keras.layers.Dense(classes, activation='softmax'))\n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e2e15fd7-dc33-476b-9302-e088f04709c8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tweet_id</th>\n",
" <th>airline_sentiment</th>\n",
" <th>airline_sentiment_confidence</th>\n",
" <th>negativereason</th>\n",
" <th>negativereason_confidence</th>\n",
" <th>airline</th>\n",
" <th>airline_sentiment_gold</th>\n",
" <th>name</th>\n",
" <th>negativereason_gold</th>\n",
" <th>retweet_count</th>\n",
" <th>text</th>\n",
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" <td>1.0000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Virgin America</td>\n",
" <td>NaN</td>\n",
" <td>cairdin</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>@VirginAmerica What @dhepburn said.</td>\n",
" <td>NaN</td>\n",
" <td>2015-02-24 11:35:52 -0800</td>\n",
" <td>NaN</td>\n",
" <td>Eastern Time (US &amp; Canada)</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>570301130888122368</td>\n",
" <td>positive</td>\n",
" <td>0.3486</td>\n",
" <td>NaN</td>\n",
" <td>0.0000</td>\n",
" <td>Virgin America</td>\n",
" <td>NaN</td>\n",
" <td>jnardino</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>@VirginAmerica plus you've added commercials t...</td>\n",
" <td>NaN</td>\n",
" <td>2015-02-24 11:15:59 -0800</td>\n",
" <td>NaN</td>\n",
" <td>Pacific Time (US &amp; Canada)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>570301083672813571</td>\n",
" <td>neutral</td>\n",
" <td>0.6837</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Virgin America</td>\n",
" <td>NaN</td>\n",
" <td>yvonnalynn</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
" <td>NaN</td>\n",
" <td>2015-02-24 11:15:48 -0800</td>\n",
" <td>Lets Play</td>\n",
" <td>Central Time (US &amp; Canada)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>570301031407624196</td>\n",
" <td>negative</td>\n",
" <td>1.0000</td>\n",
" <td>Bad Flight</td>\n",
" <td>0.7033</td>\n",
" <td>Virgin America</td>\n",
" <td>NaN</td>\n",
" <td>jnardino</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>@VirginAmerica it's really aggressive to blast...</td>\n",
" <td>NaN</td>\n",
" <td>2015-02-24 11:15:36 -0800</td>\n",
" <td>NaN</td>\n",
" <td>Pacific Time (US &amp; Canada)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>570300817074462722</td>\n",
" <td>negative</td>\n",
" <td>1.0000</td>\n",
" <td>Can't Tell</td>\n",
" <td>1.0000</td>\n",
" <td>Virgin America</td>\n",
" <td>NaN</td>\n",
" <td>jnardino</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>@VirginAmerica and it's a really big bad thing...</td>\n",
" <td>NaN</td>\n",
" <td>2015-02-24 11:14:45 -0800</td>\n",
" <td>NaN</td>\n",
" <td>Pacific Time (US &amp; Canada)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tweet_id airline_sentiment airline_sentiment_confidence \\\n",
"0 570306133677760513 neutral 1.0000 \n",
"1 570301130888122368 positive 0.3486 \n",
"2 570301083672813571 neutral 0.6837 \n",
"3 570301031407624196 negative 1.0000 \n",
"4 570300817074462722 negative 1.0000 \n",
"\n",
" negativereason negativereason_confidence airline \\\n",
"0 NaN NaN Virgin America \n",
"1 NaN 0.0000 Virgin America \n",
"2 NaN NaN Virgin America \n",
"3 Bad Flight 0.7033 Virgin America \n",
"4 Can't Tell 1.0000 Virgin America \n",
"\n",
" airline_sentiment_gold name negativereason_gold retweet_count \\\n",
"0 NaN cairdin NaN 0 \n",
"1 NaN jnardino NaN 0 \n",
"2 NaN yvonnalynn NaN 0 \n",
"3 NaN jnardino NaN 0 \n",
"4 NaN jnardino NaN 0 \n",
"\n",
" text tweet_coord \\\n",
"0 @VirginAmerica What @dhepburn said. NaN \n",
"1 @VirginAmerica plus you've added commercials t... NaN \n",
"2 @VirginAmerica I didn't today... Must mean I n... NaN \n",
"3 @VirginAmerica it's really aggressive to blast... NaN \n",
"4 @VirginAmerica and it's a really big bad thing... NaN \n",
"\n",
" tweet_created tweet_location user_timezone \n",
"0 2015-02-24 11:35:52 -0800 NaN Eastern Time (US & Canada) \n",
"1 2015-02-24 11:15:59 -0800 NaN Pacific Time (US & Canada) \n",
"2 2015-02-24 11:15:48 -0800 Lets Play Central Time (US & Canada) \n",
"3 2015-02-24 11:15:36 -0800 NaN Pacific Time (US & Canada) \n",
"4 2015-02-24 11:14:45 -0800 NaN Pacific Time (US & Canada) "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = load_data()\n",
"data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6b3e8840-3483-4756-8cdc-4fef811dc047",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"text/plain": [
" airline_sentiment text\n",
"0 1 said\n",
"1 2 plus youve added commercials experience tacky\n",
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"4 0 really big bad thing"
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"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = clean_dataset(data)\n",
"data['text'] = data['text'].apply(lambda x: clean_text(x))\n",
"data.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "398c7525-9c1b-4718-898a-7ffda507ec30",
"metadata": {
"tags": []
},
"outputs": [
{
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",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_sentiment_count(data)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "66ea00c4-e526-4218-b304-9709a5ecadf0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"X, tokenizer = tokenize_text(data)\n",
"model = word2vec_model(data)\n",
"embedding_matrix = create_embedding_matrix(tokenizer, model)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "e4fa5035-bb49-4668-9993-2003b2430df0",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"183/183 [==============================] - 15s 36ms/step - loss: 0.8459 - accuracy: 0.6376 - val_loss: 0.7659 - val_accuracy: 0.6714\n",
"Epoch 2/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.7959 - accuracy: 0.6601 - val_loss: 0.7734 - val_accuracy: 0.6684\n",
"Epoch 3/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.7636 - accuracy: 0.6765 - val_loss: 0.7216 - val_accuracy: 0.6977\n",
"Epoch 4/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.7502 - accuracy: 0.6854 - val_loss: 0.7158 - val_accuracy: 0.6974\n",
"Epoch 5/20\n",
"183/183 [==============================] - 5s 29ms/step - loss: 0.7397 - accuracy: 0.6901 - val_loss: 0.6990 - val_accuracy: 0.7070\n",
"Epoch 6/20\n",
"183/183 [==============================] - 5s 27ms/step - loss: 0.7264 - accuracy: 0.6937 - val_loss: 0.6917 - val_accuracy: 0.7117\n",
"Epoch 7/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.7281 - accuracy: 0.6935 - val_loss: 0.6897 - val_accuracy: 0.7169\n",
"Epoch 8/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.7148 - accuracy: 0.6993 - val_loss: 0.6842 - val_accuracy: 0.7152\n",
"Epoch 9/20\n",
"183/183 [==============================] - 5s 27ms/step - loss: 0.7113 - accuracy: 0.7008 - val_loss: 0.6829 - val_accuracy: 0.7162\n",
"Epoch 10/20\n",
"183/183 [==============================] - 5s 27ms/step - loss: 0.7045 - accuracy: 0.7053 - val_loss: 0.7017 - val_accuracy: 0.7155\n",
"Epoch 11/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.7087 - accuracy: 0.7045 - val_loss: 0.6731 - val_accuracy: 0.7227\n",
"Epoch 12/20\n",
"183/183 [==============================] - 5s 27ms/step - loss: 0.6912 - accuracy: 0.7115 - val_loss: 0.6704 - val_accuracy: 0.7230\n",
"Epoch 13/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.6933 - accuracy: 0.7111 - val_loss: 0.6638 - val_accuracy: 0.7298\n",
"Epoch 14/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.6831 - accuracy: 0.7129 - val_loss: 0.6673 - val_accuracy: 0.7247\n",
"Epoch 15/20\n",
"183/183 [==============================] - 5s 29ms/step - loss: 0.6840 - accuracy: 0.7112 - val_loss: 0.6656 - val_accuracy: 0.7244\n",
"Epoch 16/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.6762 - accuracy: 0.7120 - val_loss: 0.6691 - val_accuracy: 0.7278\n",
"Epoch 17/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.6751 - accuracy: 0.7152 - val_loss: 0.6574 - val_accuracy: 0.7275\n",
"Epoch 18/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.6727 - accuracy: 0.7207 - val_loss: 0.6644 - val_accuracy: 0.7237\n",
"Epoch 19/20\n",
"183/183 [==============================] - 5s 28ms/step - loss: 0.6709 - accuracy: 0.7195 - val_loss: 0.6668 - val_accuracy: 0.7203\n",
"Epoch 20/20\n",
"183/183 [==============================] - 5s 27ms/step - loss: 0.6659 - accuracy: 0.7191 - val_loss: 0.6545 - val_accuracy: 0.7319\n",
"Test accuracy: 0.7318989038467407\n",
"Epoch 1/20\n",
"183/183 [==============================] - 14s 49ms/step - loss: 0.8414 - accuracy: 0.6406 - val_loss: 0.7731 - val_accuracy: 0.6622\n",
"Epoch 2/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.7911 - accuracy: 0.6666 - val_loss: 0.7479 - val_accuracy: 0.6895\n",
"Epoch 3/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.7612 - accuracy: 0.6828 - val_loss: 0.7267 - val_accuracy: 0.6954\n",
"Epoch 4/20\n",
"183/183 [==============================] - 8s 41ms/step - loss: 0.7486 - accuracy: 0.6859 - val_loss: 0.7067 - val_accuracy: 0.7012\n",
"Epoch 5/20\n",
"183/183 [==============================] - 8s 41ms/step - loss: 0.7350 - accuracy: 0.6910 - val_loss: 0.7173 - val_accuracy: 0.6916\n",
"Epoch 6/20\n",
"183/183 [==============================] - 8s 42ms/step - loss: 0.7321 - accuracy: 0.6888 - val_loss: 0.7005 - val_accuracy: 0.7073\n",
"Epoch 7/20\n",
"183/183 [==============================] - 8s 42ms/step - loss: 0.7232 - accuracy: 0.6976 - val_loss: 0.6780 - val_accuracy: 0.7131\n",
"Epoch 8/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.7066 - accuracy: 0.7021 - val_loss: 0.6803 - val_accuracy: 0.7114\n",
"Epoch 9/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.7023 - accuracy: 0.7049 - val_loss: 0.7144 - val_accuracy: 0.7090\n",
"Epoch 10/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.7008 - accuracy: 0.7014 - val_loss: 0.6794 - val_accuracy: 0.7169\n",
"Epoch 11/20\n",
"183/183 [==============================] - 7s 41ms/step - loss: 0.6975 - accuracy: 0.7089 - val_loss: 0.6842 - val_accuracy: 0.7251\n",
"Epoch 12/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.6905 - accuracy: 0.7122 - val_loss: 0.6598 - val_accuracy: 0.7213\n",
"Epoch 13/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.6846 - accuracy: 0.7081 - val_loss: 0.6593 - val_accuracy: 0.7254\n",
"Epoch 14/20\n",
"183/183 [==============================] - 8s 41ms/step - loss: 0.6842 - accuracy: 0.7152 - val_loss: 0.6647 - val_accuracy: 0.7251\n",
"Epoch 15/20\n",
"183/183 [==============================] - 8s 41ms/step - loss: 0.6753 - accuracy: 0.7135 - val_loss: 0.6567 - val_accuracy: 0.7268\n",
"Epoch 16/20\n",
"183/183 [==============================] - 8s 41ms/step - loss: 0.6758 - accuracy: 0.7170 - val_loss: 0.6593 - val_accuracy: 0.7292\n",
"Epoch 17/20\n",
"183/183 [==============================] - 7s 41ms/step - loss: 0.6719 - accuracy: 0.7158 - val_loss: 0.6663 - val_accuracy: 0.7234\n",
"Epoch 18/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.6674 - accuracy: 0.7177 - val_loss: 0.6678 - val_accuracy: 0.7244\n",
"Epoch 19/20\n",
"183/183 [==============================] - 7s 41ms/step - loss: 0.6654 - accuracy: 0.7196 - val_loss: 0.6641 - val_accuracy: 0.7152\n",
"Epoch 20/20\n",
"183/183 [==============================] - 7s 40ms/step - loss: 0.6625 - accuracy: 0.7205 - val_loss: 0.6714 - val_accuracy: 0.7220\n",
"Test accuracy: 0.7219945192337036\n",
"Epoch 1/20\n",
"183/183 [==============================] - 19s 65ms/step - loss: 0.8347 - accuracy: 0.6452 - val_loss: 0.7698 - val_accuracy: 0.6773\n",
"Epoch 2/20\n",
"183/183 [==============================] - 10s 54ms/step - loss: 0.7859 - accuracy: 0.6687 - val_loss: 0.7702 - val_accuracy: 0.6820\n",
"Epoch 3/20\n",
"183/183 [==============================] - 10s 53ms/step - loss: 0.7581 - accuracy: 0.6820 - val_loss: 0.7240 - val_accuracy: 0.7001\n",
"Epoch 4/20\n",
"183/183 [==============================] - 10s 53ms/step - loss: 0.7524 - accuracy: 0.6879 - val_loss: 0.7044 - val_accuracy: 0.7070\n",
"Epoch 5/20\n",
"183/183 [==============================] - 10s 53ms/step - loss: 0.7419 - accuracy: 0.6919 - val_loss: 0.7052 - val_accuracy: 0.7063\n",
"Epoch 6/20\n",
"183/183 [==============================] - 10s 52ms/step - loss: 0.7368 - accuracy: 0.6930 - val_loss: 0.7090 - val_accuracy: 0.7046\n",
"Epoch 7/20\n",
"183/183 [==============================] - 10s 54ms/step - loss: 0.7283 - accuracy: 0.6930 - val_loss: 0.6909 - val_accuracy: 0.7090\n",
"Epoch 8/20\n",
"183/183 [==============================] - 10s 52ms/step - loss: 0.7184 - accuracy: 0.6979 - val_loss: 0.6930 - val_accuracy: 0.7053\n",
"Epoch 9/20\n",
"183/183 [==============================] - 10s 53ms/step - loss: 0.7094 - accuracy: 0.7005 - val_loss: 0.6676 - val_accuracy: 0.7295\n",
"Epoch 10/20\n",
"183/183 [==============================] - 10s 57ms/step - loss: 0.7013 - accuracy: 0.7058 - val_loss: 0.6701 - val_accuracy: 0.7227\n",
"Epoch 11/20\n",
"183/183 [==============================] - 11s 59ms/step - loss: 0.6927 - accuracy: 0.7118 - val_loss: 0.6883 - val_accuracy: 0.7138\n",
"Epoch 12/20\n",
"183/183 [==============================] - 10s 55ms/step - loss: 0.6872 - accuracy: 0.7121 - val_loss: 0.6767 - val_accuracy: 0.7295\n",
"Epoch 13/20\n",
"183/183 [==============================] - 10s 54ms/step - loss: 0.6882 - accuracy: 0.7112 - val_loss: 0.6734 - val_accuracy: 0.7145\n",
"Epoch 14/20\n",
"183/183 [==============================] - 10s 53ms/step - loss: 0.6856 - accuracy: 0.7110 - val_loss: 0.6605 - val_accuracy: 0.7292\n",
"Epoch 15/20\n",
"183/183 [==============================] - 10s 54ms/step - loss: 0.6823 - accuracy: 0.7152 - val_loss: 0.6621 - val_accuracy: 0.7237\n",
"Epoch 16/20\n",
"183/183 [==============================] - 10s 52ms/step - loss: 0.6762 - accuracy: 0.7185 - val_loss: 0.6556 - val_accuracy: 0.7268\n",
"Epoch 17/20\n",
"183/183 [==============================] - 9s 52ms/step - loss: 0.6761 - accuracy: 0.7170 - val_loss: 0.6650 - val_accuracy: 0.7203\n",
"Epoch 18/20\n",
"183/183 [==============================] - 10s 52ms/step - loss: 0.6703 - accuracy: 0.7222 - val_loss: 0.6923 - val_accuracy: 0.7073\n",
"Epoch 19/20\n",
"183/183 [==============================] - 9s 51ms/step - loss: 0.6720 - accuracy: 0.7140 - val_loss: 0.6605 - val_accuracy: 0.7251\n",
"Epoch 20/20\n",
"183/183 [==============================] - 10s 52ms/step - loss: 0.6637 - accuracy: 0.7228 - val_loss: 0.6712 - val_accuracy: 0.7155\n",
"Test accuracy: 0.7155054807662964\n",
"Epoch 1/20\n",
"183/183 [==============================] - 24s 82ms/step - loss: 0.8426 - accuracy: 0.6416 - val_loss: 0.7750 - val_accuracy: 0.6633\n",
"Epoch 2/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.8063 - accuracy: 0.6496 - val_loss: 0.7685 - val_accuracy: 0.6848\n",
"Epoch 3/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.7840 - accuracy: 0.6673 - val_loss: 0.7887 - val_accuracy: 0.6776\n",
"Epoch 4/20\n",
"183/183 [==============================] - 12s 65ms/step - loss: 0.7615 - accuracy: 0.6811 - val_loss: 0.7546 - val_accuracy: 0.6827\n",
"Epoch 5/20\n",
"183/183 [==============================] - 12s 64ms/step - loss: 0.7503 - accuracy: 0.6847 - val_loss: 0.7280 - val_accuracy: 0.6974\n",
"Epoch 6/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.7391 - accuracy: 0.6908 - val_loss: 0.7065 - val_accuracy: 0.7039\n",
"Epoch 7/20\n",
"183/183 [==============================] - 12s 65ms/step - loss: 0.7348 - accuracy: 0.6884 - val_loss: 0.7046 - val_accuracy: 0.7141\n",
"Epoch 8/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.7268 - accuracy: 0.6979 - val_loss: 0.6907 - val_accuracy: 0.7155\n",
"Epoch 9/20\n",
"183/183 [==============================] - 12s 65ms/step - loss: 0.7168 - accuracy: 0.7008 - val_loss: 0.7029 - val_accuracy: 0.7117\n",
"Epoch 10/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.7143 - accuracy: 0.7016 - val_loss: 0.6961 - val_accuracy: 0.7049\n",
"Epoch 11/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.6984 - accuracy: 0.7068 - val_loss: 0.6748 - val_accuracy: 0.7206\n",
"Epoch 12/20\n",
"183/183 [==============================] - 12s 65ms/step - loss: 0.6963 - accuracy: 0.7088 - val_loss: 0.6654 - val_accuracy: 0.7254\n",
"Epoch 13/20\n",
"183/183 [==============================] - 13s 70ms/step - loss: 0.6928 - accuracy: 0.7106 - val_loss: 0.6763 - val_accuracy: 0.7213\n",
"Epoch 14/20\n",
"183/183 [==============================] - 14s 77ms/step - loss: 0.6857 - accuracy: 0.7129 - val_loss: 0.6743 - val_accuracy: 0.7182\n",
"Epoch 15/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.6808 - accuracy: 0.7150 - val_loss: 0.6645 - val_accuracy: 0.7264\n",
"Epoch 16/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.6796 - accuracy: 0.7167 - val_loss: 0.6633 - val_accuracy: 0.7234\n",
"Epoch 17/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.6762 - accuracy: 0.7181 - val_loss: 0.6657 - val_accuracy: 0.7275\n",
"Epoch 18/20\n",
"183/183 [==============================] - 12s 64ms/step - loss: 0.6710 - accuracy: 0.7223 - val_loss: 0.6781 - val_accuracy: 0.7189\n",
"Epoch 19/20\n",
"183/183 [==============================] - 12s 66ms/step - loss: 0.6748 - accuracy: 0.7190 - val_loss: 0.6573 - val_accuracy: 0.7244\n",
"Epoch 20/20\n",
"183/183 [==============================] - 12s 67ms/step - loss: 0.6711 - accuracy: 0.7187 - val_loss: 0.6606 - val_accuracy: 0.7203\n",
"Test accuracy: 0.7202869057655334\n"
]
},
{
"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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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, data['airline_sentiment'].values, test_size=0.2, random_state=42)\n",
"y_train = tf.keras.utils.to_categorical(y_train)\n",
"y_test = tf.keras.utils.to_categorical(y_test)\n",
"\n",
"# Create the LSTM model\n",
"lstm_model1 = create_lstm_model(embedding_matrix, X.shape[1], 5000, 100, 3, num_hidden_layers=1)\n",
"history1 = lstm_model1.fit(X_train, y_train, epochs=20, batch_size=64, validation_data=(X_test, y_test)).history\n",
"score, acc = lstm_model1.evaluate(X_test, y_test, verbose=0)\n",
"print('Test accuracy:', acc)\n",
"\n",
"lstm_model2 = create_lstm_model(embedding_matrix, X.shape[1], 5000, 100, 3, num_hidden_layers=2)\n",
"history2 = lstm_model2.fit(X_train, y_train, epochs=20, batch_size=64, validation_data=(X_test, y_test)).history\n",
"score, acc = lstm_model2.evaluate(X_test, y_test, verbose=0)\n",
"print('Test accuracy:', acc)\n",
"\n",
"lstm_model3 = create_lstm_model(embedding_matrix, X.shape[1], 5000, 100, 3, num_hidden_layers=3)\n",
"history3 = lstm_model3.fit(X_train, y_train, epochs=20, batch_size=64, validation_data=(X_test, y_test)).history\n",
"score, acc = lstm_model3.evaluate(X_test, y_test, verbose=0)\n",
"print('Test accuracy:', acc)\n",
"\n",
"lstm_model4 = create_lstm_model(embedding_matrix, X.shape[1], 5000, 100, 3, num_hidden_layers=4)\n",
"history4 = lstm_model4.fit(X_train, y_train, epochs=20, batch_size=64, validation_data=(X_test, y_test)).history\n",
"score, acc = lstm_model4.evaluate(X_test, y_test, verbose=0)\n",
"print('Test accuracy:', acc)\n",
"\n",
"\n",
"# Plot accuracy\n",
"plt.plot(history1['accuracy'], label='Model 1')\n",
"plt.plot(history2['accuracy'], label='Model 2')\n",
"plt.plot(history3['accuracy'], label='Model 3')\n",
"plt.plot(history4['accuracy'], label='Model 4')\n",
"plt.title('Model accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(loc='upper left')\n",
"plt.show()\n",
"\n",
"# Plot loss\n",
"plt.plot(history1['loss'], label='Model 1')\n",
"plt.plot(history2['loss'], label='Model 2')\n",
"plt.plot(history3['loss'], label='Model 3')\n",
"plt.plot(history4['loss'], label='Model 4')\n",
"plt.title('Model loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "b3089572-b83c-4a85-9be6-d6b8898d3365",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| iter | target | batch_... | dense_... | dropou... | epochs | num_hi... |\n",
"-------------------------------------------------------------------------------------\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8465 - accuracy: 0.6844\n",
"| \u001b[0m1 \u001b[0m | \u001b[0m0.6844 \u001b[0m | \u001b[0m331.0 \u001b[0m | \u001b[0m70.52 \u001b[0m | \u001b[0m0.5126 \u001b[0m | \u001b[0m374.4 \u001b[0m | \u001b[0m4.941 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9827 - accuracy: 0.7531\n",
"| \u001b[95m2 \u001b[0m | \u001b[95m0.7531 \u001b[0m | \u001b[95m90.0 \u001b[0m | \u001b[95m446.9 \u001b[0m | \u001b[95m0.3953 \u001b[0m | \u001b[95m186.3 \u001b[0m | \u001b[95m7.993 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9082 - accuracy: 0.6650\n",
"| \u001b[0m3 \u001b[0m | \u001b[0m0.665 \u001b[0m | \u001b[0m65.54 \u001b[0m | \u001b[0m88.45 \u001b[0m | \u001b[0m0.469 \u001b[0m | \u001b[0m132.5 \u001b[0m | \u001b[0m6.977 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 1.0171 - accuracy: 0.7469\n",
"| \u001b[0m4 \u001b[0m | \u001b[0m0.7469 \u001b[0m | \u001b[0m142.9 \u001b[0m | \u001b[0m442.4 \u001b[0m | \u001b[0m0.2963 \u001b[0m | \u001b[0m236.7 \u001b[0m | \u001b[0m7.511 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9027 - accuracy: 0.7182\n",
"| \u001b[0m5 \u001b[0m | \u001b[0m0.7182 \u001b[0m | \u001b[0m276.6 \u001b[0m | \u001b[0m334.1 \u001b[0m | \u001b[0m0.3214 \u001b[0m | \u001b[0m22.81 \u001b[0m | \u001b[0m7.63 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8788 - accuracy: 0.7401\n",
"| \u001b[0m6 \u001b[0m | \u001b[0m0.7401 \u001b[0m | \u001b[0m103.2 \u001b[0m | \u001b[0m450.1 \u001b[0m | \u001b[0m0.4685 \u001b[0m | \u001b[0m179.8 \u001b[0m | \u001b[0m6.365 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9560 - accuracy: 0.7319\n",
"| \u001b[0m7 \u001b[0m | \u001b[0m0.7319 \u001b[0m | \u001b[0m58.01 \u001b[0m | \u001b[0m432.9 \u001b[0m | \u001b[0m0.0 \u001b[0m | \u001b[0m246.6 \u001b[0m | \u001b[0m10.0 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8383 - accuracy: 0.7452\n",
"| \u001b[0m8 \u001b[0m | \u001b[0m0.7452 \u001b[0m | \u001b[0m61.4 \u001b[0m | \u001b[0m439.5 \u001b[0m | \u001b[0m0.3263 \u001b[0m | \u001b[0m248.9 \u001b[0m | \u001b[0m7.983 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7070 - accuracy: 0.7418\n",
"| \u001b[0m9 \u001b[0m | \u001b[0m0.7418 \u001b[0m | \u001b[0m97.87 \u001b[0m | \u001b[0m461.5 \u001b[0m | \u001b[0m0.8502 \u001b[0m | \u001b[0m244.7 \u001b[0m | \u001b[0m1.294 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.6938 - accuracy: 0.7777\n",
"| \u001b[95m10 \u001b[0m | \u001b[95m0.7777 \u001b[0m | \u001b[95m58.78 \u001b[0m | \u001b[95m469.9 \u001b[0m | \u001b[95m0.7318 \u001b[0m | \u001b[95m207.8 \u001b[0m | \u001b[95m1.334 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 1.0295 - accuracy: 0.7589\n",
"| \u001b[0m11 \u001b[0m | \u001b[0m0.7589 \u001b[0m | \u001b[0m144.3 \u001b[0m | \u001b[0m438.6 \u001b[0m | \u001b[0m0.1679 \u001b[0m | \u001b[0m235.4 \u001b[0m | \u001b[0m5.491 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9300 - accuracy: 0.6899\n",
"| \u001b[0m12 \u001b[0m | \u001b[0m0.6899 \u001b[0m | \u001b[0m62.45 \u001b[0m | \u001b[0m473.7 \u001b[0m | \u001b[0m0.5447 \u001b[0m | \u001b[0m236.6 \u001b[0m | \u001b[0m6.367 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 1.0207 - accuracy: 0.6452\n",
"| \u001b[0m13 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m68.39 \u001b[0m | \u001b[0m466.9 \u001b[0m | \u001b[0m0.7827 \u001b[0m | \u001b[0m196.2 \u001b[0m | \u001b[0m9.276 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 1.4100 - accuracy: 0.7661\n",
"| \u001b[0m14 \u001b[0m | \u001b[0m0.7661 \u001b[0m | \u001b[0m50.06 \u001b[0m | \u001b[0m475.6 \u001b[0m | \u001b[0m0.1793 \u001b[0m | \u001b[0m213.1 \u001b[0m | \u001b[0m5.946 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8964 - accuracy: 0.6452\n",
"| \u001b[0m15 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m51.17 \u001b[0m | \u001b[0m479.8 \u001b[0m | \u001b[0m0.8846 \u001b[0m | \u001b[0m203.4 \u001b[0m | \u001b[0m8.934 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9385 - accuracy: 0.6452\n",
"| \u001b[0m16 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m95.22 \u001b[0m | \u001b[0m451.3 \u001b[0m | \u001b[0m0.8563 \u001b[0m | \u001b[0m191.3 \u001b[0m | \u001b[0m5.242 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8063 - accuracy: 0.7240\n",
"| \u001b[0m17 \u001b[0m | \u001b[0m0.724 \u001b[0m | \u001b[0m100.6 \u001b[0m | \u001b[0m459.0 \u001b[0m | \u001b[0m0.588 \u001b[0m | \u001b[0m245.6 \u001b[0m | \u001b[0m4.665 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9986 - accuracy: 0.7497\n",
"| \u001b[0m18 \u001b[0m | \u001b[0m0.7497 \u001b[0m | \u001b[0m144.9 \u001b[0m | \u001b[0m440.3 \u001b[0m | \u001b[0m0.09593 \u001b[0m | \u001b[0m227.7 \u001b[0m | \u001b[0m8.936 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8182 - accuracy: 0.6653\n",
"| \u001b[0m19 \u001b[0m | \u001b[0m0.6653 \u001b[0m | \u001b[0m58.59 \u001b[0m | \u001b[0m476.7 \u001b[0m | \u001b[0m0.7071 \u001b[0m | \u001b[0m214.2 \u001b[0m | \u001b[0m6.293 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7957 - accuracy: 0.7186\n",
"| \u001b[0m20 \u001b[0m | \u001b[0m0.7186 \u001b[0m | \u001b[0m60.14 \u001b[0m | \u001b[0m465.0 \u001b[0m | \u001b[0m0.638 \u001b[0m | \u001b[0m202.5 \u001b[0m | \u001b[0m4.637 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7705 - accuracy: 0.6909\n",
"| \u001b[0m21 \u001b[0m | \u001b[0m0.6909 \u001b[0m | \u001b[0m151.1 \u001b[0m | \u001b[0m439.4 \u001b[0m | \u001b[0m0.7028 \u001b[0m | \u001b[0m230.8 \u001b[0m | \u001b[0m2.456 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 1.1057 - accuracy: 0.7503\n",
"| \u001b[0m22 \u001b[0m | \u001b[0m0.7503 \u001b[0m | \u001b[0m108.1 \u001b[0m | \u001b[0m453.2 \u001b[0m | \u001b[0m0.1109 \u001b[0m | \u001b[0m173.4 \u001b[0m | \u001b[0m2.011 \u001b[0m |\n",
"92/92 [==============================] - 1s 9ms/step - loss: 0.8449 - accuracy: 0.7370\n",
"| \u001b[0m23 \u001b[0m | \u001b[0m0.737 \u001b[0m | \u001b[0m141.0 \u001b[0m | \u001b[0m441.0 \u001b[0m | \u001b[0m0.07501 \u001b[0m | \u001b[0m245.7 \u001b[0m | \u001b[0m9.392 \u001b[0m |\n",
"92/92 [==============================] - 1s 9ms/step - loss: 0.8958 - accuracy: 0.6452\n",
"| \u001b[0m24 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m139.0 \u001b[0m | \u001b[0m439.3 \u001b[0m | \u001b[0m0.9492 \u001b[0m | \u001b[0m240.2 \u001b[0m | \u001b[0m8.957 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8977 - accuracy: 0.7538\n",
"| \u001b[0m25 \u001b[0m | \u001b[0m0.7538 \u001b[0m | \u001b[0m96.73 \u001b[0m | \u001b[0m462.0 \u001b[0m | \u001b[0m0.3153 \u001b[0m | \u001b[0m241.4 \u001b[0m | \u001b[0m5.258 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9809 - accuracy: 0.7527\n",
"| \u001b[0m26 \u001b[0m | \u001b[0m0.7527 \u001b[0m | \u001b[0m100.6 \u001b[0m | \u001b[0m463.1 \u001b[0m | \u001b[0m0.1231 \u001b[0m | \u001b[0m240.6 \u001b[0m | \u001b[0m5.222 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9296 - accuracy: 0.7428\n",
"| \u001b[0m27 \u001b[0m | \u001b[0m0.7428 \u001b[0m | \u001b[0m61.65 \u001b[0m | \u001b[0m440.8 \u001b[0m | \u001b[0m0.4587 \u001b[0m | \u001b[0m253.1 \u001b[0m | \u001b[0m4.574 \u001b[0m |\n",
"92/92 [==============================] - 1s 9ms/step - loss: 1.0653 - accuracy: 0.7473\n",
"| \u001b[0m28 \u001b[0m | \u001b[0m0.7473 \u001b[0m | \u001b[0m60.11 \u001b[0m | \u001b[0m432.8 \u001b[0m | \u001b[0m0.2088 \u001b[0m | \u001b[0m249.7 \u001b[0m | \u001b[0m4.311 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9019 - accuracy: 0.7602\n",
"| \u001b[0m29 \u001b[0m | \u001b[0m0.7602 \u001b[0m | \u001b[0m55.21 \u001b[0m | \u001b[0m467.5 \u001b[0m | \u001b[0m0.3901 \u001b[0m | \u001b[0m211.0 \u001b[0m | \u001b[0m1.229 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 1.0898 - accuracy: 0.7425\n",
"| \u001b[0m30 \u001b[0m | \u001b[0m0.7425 \u001b[0m | \u001b[0m49.43 \u001b[0m | \u001b[0m467.5 \u001b[0m | \u001b[0m0.2021 \u001b[0m | \u001b[0m208.3 \u001b[0m | \u001b[0m1.094 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8835 - accuracy: 0.6950\n",
"| \u001b[0m31 \u001b[0m | \u001b[0m0.695 \u001b[0m | \u001b[0m96.21 \u001b[0m | \u001b[0m462.4 \u001b[0m | \u001b[0m0.6178 \u001b[0m | \u001b[0m238.0 \u001b[0m | \u001b[0m5.618 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8579 - accuracy: 0.7565\n",
"| \u001b[0m32 \u001b[0m | \u001b[0m0.7565 \u001b[0m | \u001b[0m318.3 \u001b[0m | \u001b[0m146.8 \u001b[0m | \u001b[0m0.1725 \u001b[0m | \u001b[0m125.8 \u001b[0m | \u001b[0m1.086 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7965 - accuracy: 0.6803\n",
"| \u001b[0m33 \u001b[0m | \u001b[0m0.6803 \u001b[0m | \u001b[0m346.3 \u001b[0m | \u001b[0m207.8 \u001b[0m | \u001b[0m0.503 \u001b[0m | \u001b[0m111.1 \u001b[0m | \u001b[0m5.295 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9516 - accuracy: 0.6452\n",
"| \u001b[0m34 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m484.9 \u001b[0m | \u001b[0m497.2 \u001b[0m | \u001b[0m0.8534 \u001b[0m | \u001b[0m61.61 \u001b[0m | \u001b[0m6.523 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.6656 - accuracy: 0.7664\n",
"| \u001b[0m35 \u001b[0m | \u001b[0m0.7664 \u001b[0m | \u001b[0m49.02 \u001b[0m | \u001b[0m479.4 \u001b[0m | \u001b[0m0.9553 \u001b[0m | \u001b[0m217.1 \u001b[0m | \u001b[0m1.705 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8191 - accuracy: 0.6585\n",
"| \u001b[0m36 \u001b[0m | \u001b[0m0.6585 \u001b[0m | \u001b[0m363.1 \u001b[0m | \u001b[0m498.5 \u001b[0m | \u001b[0m0.703 \u001b[0m | \u001b[0m280.9 \u001b[0m | \u001b[0m5.473 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8975 - accuracy: 0.6452\n",
"| \u001b[0m37 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m54.49 \u001b[0m | \u001b[0m439.5 \u001b[0m | \u001b[0m0.893 \u001b[0m | \u001b[0m248.1 \u001b[0m | \u001b[0m9.32 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 1.1281 - accuracy: 0.7380\n",
"| \u001b[0m38 \u001b[0m | \u001b[0m0.738 \u001b[0m | \u001b[0m103.6 \u001b[0m | \u001b[0m469.4 \u001b[0m | \u001b[0m0.2083 \u001b[0m | \u001b[0m244.3 \u001b[0m | \u001b[0m8.441 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 1.0814 - accuracy: 0.7736\n",
"| \u001b[0m39 \u001b[0m | \u001b[0m0.7736 \u001b[0m | \u001b[0m110.9 \u001b[0m | \u001b[0m452.0 \u001b[0m | \u001b[0m0.05126 \u001b[0m | \u001b[0m167.5 \u001b[0m | \u001b[0m1.676 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8738 - accuracy: 0.6851\n",
"| \u001b[0m40 \u001b[0m | \u001b[0m0.6851 \u001b[0m | \u001b[0m97.52 \u001b[0m | \u001b[0m462.4 \u001b[0m | \u001b[0m0.5965 \u001b[0m | \u001b[0m248.2 \u001b[0m | \u001b[0m6.631 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7528 - accuracy: 0.6936\n",
"| \u001b[0m41 \u001b[0m | \u001b[0m0.6936 \u001b[0m | \u001b[0m142.3 \u001b[0m | \u001b[0m435.0 \u001b[0m | \u001b[0m0.7623 \u001b[0m | \u001b[0m233.6 \u001b[0m | \u001b[0m3.005 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9263 - accuracy: 0.7671\n",
"| \u001b[0m42 \u001b[0m | \u001b[0m0.7671 \u001b[0m | \u001b[0m107.5 \u001b[0m | \u001b[0m449.8 \u001b[0m | \u001b[0m0.4772 \u001b[0m | \u001b[0m169.3 \u001b[0m | \u001b[0m3.822 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7857 - accuracy: 0.7107\n",
"| \u001b[0m43 \u001b[0m | \u001b[0m0.7107 \u001b[0m | \u001b[0m110.9 \u001b[0m | \u001b[0m452.8 \u001b[0m | \u001b[0m0.6368 \u001b[0m | \u001b[0m171.3 \u001b[0m | \u001b[0m5.53 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8958 - accuracy: 0.6452\n",
"| \u001b[0m44 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m67.19 \u001b[0m | \u001b[0m437.9 \u001b[0m | \u001b[0m0.9627 \u001b[0m | \u001b[0m248.6 \u001b[0m | \u001b[0m6.834 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8264 - accuracy: 0.7643\n",
"| \u001b[0m45 \u001b[0m | \u001b[0m0.7643 \u001b[0m | \u001b[0m115.1 \u001b[0m | \u001b[0m444.8 \u001b[0m | \u001b[0m0.4371 \u001b[0m | \u001b[0m167.9 \u001b[0m | \u001b[0m2.035 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8125 - accuracy: 0.6803\n",
"| \u001b[0m46 \u001b[0m | \u001b[0m0.6803 \u001b[0m | \u001b[0m44.98 \u001b[0m | \u001b[0m485.5 \u001b[0m | \u001b[0m0.6675 \u001b[0m | \u001b[0m215.3 \u001b[0m | \u001b[0m6.961 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7317 - accuracy: 0.6861\n",
"| \u001b[0m47 \u001b[0m | \u001b[0m0.6861 \u001b[0m | \u001b[0m56.08 \u001b[0m | \u001b[0m460.8 \u001b[0m | \u001b[0m0.9106 \u001b[0m | \u001b[0m210.8 \u001b[0m | \u001b[0m2.203 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7621 - accuracy: 0.6711\n",
"| \u001b[0m48 \u001b[0m | \u001b[0m0.6711 \u001b[0m | \u001b[0m56.03 \u001b[0m | \u001b[0m436.1 \u001b[0m | \u001b[0m0.7658 \u001b[0m | \u001b[0m255.1 \u001b[0m | \u001b[0m4.24 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8953 - accuracy: 0.6452\n",
"| \u001b[0m49 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m142.2 \u001b[0m | \u001b[0m447.1 \u001b[0m | \u001b[0m0.9701 \u001b[0m | \u001b[0m237.1 \u001b[0m | \u001b[0m9.154 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 1.0389 - accuracy: 0.7688\n",
"| \u001b[0m50 \u001b[0m | \u001b[0m0.7688 \u001b[0m | \u001b[0m102.3 \u001b[0m | \u001b[0m454.9 \u001b[0m | \u001b[0m0.02386 \u001b[0m | \u001b[0m238.0 \u001b[0m | \u001b[0m1.635 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 1.0865 - accuracy: 0.7585\n",
"| \u001b[0m51 \u001b[0m | \u001b[0m0.7585 \u001b[0m | \u001b[0m49.79 \u001b[0m | \u001b[0m465.3 \u001b[0m | \u001b[0m0.08019 \u001b[0m | \u001b[0m212.1 \u001b[0m | \u001b[0m1.83 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8925 - accuracy: 0.6291\n",
"| \u001b[0m52 \u001b[0m | \u001b[0m0.6291 \u001b[0m | \u001b[0m106.9 \u001b[0m | \u001b[0m450.4 \u001b[0m | \u001b[0m0.6774 \u001b[0m | \u001b[0m168.2 \u001b[0m | \u001b[0m8.06 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8970 - accuracy: 0.6452\n",
"| \u001b[0m53 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m208.5 \u001b[0m | \u001b[0m56.0 \u001b[0m | \u001b[0m0.8066 \u001b[0m | \u001b[0m138.9 \u001b[0m | \u001b[0m5.779 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9690 - accuracy: 0.7630\n",
"| \u001b[0m54 \u001b[0m | \u001b[0m0.763 \u001b[0m | \u001b[0m187.0 \u001b[0m | \u001b[0m382.1 \u001b[0m | \u001b[0m0.1332 \u001b[0m | \u001b[0m129.5 \u001b[0m | \u001b[0m5.682 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.6627 - accuracy: 0.7630\n",
"| \u001b[0m55 \u001b[0m | \u001b[0m0.763 \u001b[0m | \u001b[0m47.46 \u001b[0m | \u001b[0m79.33 \u001b[0m | \u001b[0m0.8413 \u001b[0m | \u001b[0m356.7 \u001b[0m | \u001b[0m1.37 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8746 - accuracy: 0.7336\n",
"| \u001b[0m56 \u001b[0m | \u001b[0m0.7336 \u001b[0m | \u001b[0m399.1 \u001b[0m | \u001b[0m502.2 \u001b[0m | \u001b[0m0.5134 \u001b[0m | \u001b[0m338.4 \u001b[0m | \u001b[0m5.838 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7860 - accuracy: 0.6755\n",
"| \u001b[0m57 \u001b[0m | \u001b[0m0.6755 \u001b[0m | \u001b[0m370.0 \u001b[0m | \u001b[0m399.3 \u001b[0m | \u001b[0m0.6431 \u001b[0m | \u001b[0m368.5 \u001b[0m | \u001b[0m5.625 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7483 - accuracy: 0.6462\n",
"| \u001b[0m58 \u001b[0m | \u001b[0m0.6462 \u001b[0m | \u001b[0m362.9 \u001b[0m | \u001b[0m151.2 \u001b[0m | \u001b[0m0.6936 \u001b[0m | \u001b[0m18.36 \u001b[0m | \u001b[0m4.71 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8195 - accuracy: 0.7165\n",
"| \u001b[0m59 \u001b[0m | \u001b[0m0.7165 \u001b[0m | \u001b[0m188.6 \u001b[0m | \u001b[0m384.6 \u001b[0m | \u001b[0m0.4572 \u001b[0m | \u001b[0m132.1 \u001b[0m | \u001b[0m8.041 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9757 - accuracy: 0.7309\n",
"| \u001b[0m60 \u001b[0m | \u001b[0m0.7309 \u001b[0m | \u001b[0m44.14 \u001b[0m | \u001b[0m469.5 \u001b[0m | \u001b[0m0.4708 \u001b[0m | \u001b[0m213.4 \u001b[0m | \u001b[0m6.79 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7450 - accuracy: 0.6974\n",
"| \u001b[0m61 \u001b[0m | \u001b[0m0.6974 \u001b[0m | \u001b[0m101.7 \u001b[0m | \u001b[0m452.2 \u001b[0m | \u001b[0m0.7263 \u001b[0m | \u001b[0m178.7 \u001b[0m | \u001b[0m3.331 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9137 - accuracy: 0.7534\n",
"| \u001b[0m62 \u001b[0m | \u001b[0m0.7534 \u001b[0m | \u001b[0m164.6 \u001b[0m | \u001b[0m164.5 \u001b[0m | \u001b[0m0.2472 \u001b[0m | \u001b[0m177.9 \u001b[0m | \u001b[0m3.833 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8135 - accuracy: 0.6889\n",
"| \u001b[0m63 \u001b[0m | \u001b[0m0.6889 \u001b[0m | \u001b[0m238.0 \u001b[0m | \u001b[0m202.5 \u001b[0m | \u001b[0m0.4704 \u001b[0m | \u001b[0m160.1 \u001b[0m | \u001b[0m9.361 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9774 - accuracy: 0.7596\n",
"| \u001b[0m64 \u001b[0m | \u001b[0m0.7596 \u001b[0m | \u001b[0m153.3 \u001b[0m | \u001b[0m386.0 \u001b[0m | \u001b[0m0.1907 \u001b[0m | \u001b[0m234.6 \u001b[0m | \u001b[0m5.652 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7135 - accuracy: 0.7626\n",
"| \u001b[0m65 \u001b[0m | \u001b[0m0.7626 \u001b[0m | \u001b[0m448.2 \u001b[0m | \u001b[0m279.3 \u001b[0m | \u001b[0m0.6908 \u001b[0m | \u001b[0m74.61 \u001b[0m | \u001b[0m1.194 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9089 - accuracy: 0.7302\n",
"| \u001b[0m66 \u001b[0m | \u001b[0m0.7302 \u001b[0m | \u001b[0m353.6 \u001b[0m | \u001b[0m393.9 \u001b[0m | \u001b[0m0.3795 \u001b[0m | \u001b[0m286.2 \u001b[0m | \u001b[0m5.221 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7586 - accuracy: 0.7712\n",
"| \u001b[0m67 \u001b[0m | \u001b[0m0.7712 \u001b[0m | \u001b[0m229.5 \u001b[0m | \u001b[0m158.4 \u001b[0m | \u001b[0m0.1226 \u001b[0m | \u001b[0m142.3 \u001b[0m | \u001b[0m3.136 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8782 - accuracy: 0.7555\n",
"| \u001b[0m68 \u001b[0m | \u001b[0m0.7555 \u001b[0m | \u001b[0m228.7 \u001b[0m | \u001b[0m238.1 \u001b[0m | \u001b[0m0.2831 \u001b[0m | \u001b[0m248.0 \u001b[0m | \u001b[0m4.425 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.6819 - accuracy: 0.7746\n",
"| \u001b[0m69 \u001b[0m | \u001b[0m0.7746 \u001b[0m | \u001b[0m249.6 \u001b[0m | \u001b[0m350.0 \u001b[0m | \u001b[0m0.784 \u001b[0m | \u001b[0m208.2 \u001b[0m | \u001b[0m1.449 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9354 - accuracy: 0.6452\n",
"| \u001b[0m70 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m268.2 \u001b[0m | \u001b[0m109.8 \u001b[0m | \u001b[0m0.7679 \u001b[0m | \u001b[0m35.09 \u001b[0m | \u001b[0m6.157 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9715 - accuracy: 0.7281\n",
"| \u001b[0m71 \u001b[0m | \u001b[0m0.7281 \u001b[0m | \u001b[0m413.1 \u001b[0m | \u001b[0m507.3 \u001b[0m | \u001b[0m0.148 \u001b[0m | \u001b[0m215.5 \u001b[0m | \u001b[0m7.22 \u001b[0m |\n",
"92/92 [==============================] - 1s 9ms/step - loss: 0.7851 - accuracy: 0.7343\n",
"| \u001b[0m72 \u001b[0m | \u001b[0m0.7343 \u001b[0m | \u001b[0m239.0 \u001b[0m | \u001b[0m421.8 \u001b[0m | \u001b[0m0.4224 \u001b[0m | \u001b[0m374.2 \u001b[0m | \u001b[0m8.511 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9883 - accuracy: 0.7582\n",
"| \u001b[0m73 \u001b[0m | \u001b[0m0.7582 \u001b[0m | \u001b[0m362.8 \u001b[0m | \u001b[0m509.8 \u001b[0m | \u001b[0m0.08062 \u001b[0m | \u001b[0m180.3 \u001b[0m | \u001b[0m6.657 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8935 - accuracy: 0.7367\n",
"| \u001b[0m74 \u001b[0m | \u001b[0m0.7367 \u001b[0m | \u001b[0m415.1 \u001b[0m | \u001b[0m327.4 \u001b[0m | \u001b[0m0.4135 \u001b[0m | \u001b[0m112.7 \u001b[0m | \u001b[0m5.778 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8954 - accuracy: 0.6452\n",
"| \u001b[0m75 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m123.1 \u001b[0m | \u001b[0m148.3 \u001b[0m | \u001b[0m0.9667 \u001b[0m | \u001b[0m24.76 \u001b[0m | \u001b[0m8.079 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8149 - accuracy: 0.7613\n",
"| \u001b[0m76 \u001b[0m | \u001b[0m0.7613 \u001b[0m | \u001b[0m440.0 \u001b[0m | \u001b[0m142.2 \u001b[0m | \u001b[0m0.2703 \u001b[0m | \u001b[0m345.3 \u001b[0m | \u001b[0m2.163 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8466 - accuracy: 0.7599\n",
"| \u001b[0m77 \u001b[0m | \u001b[0m0.7599 \u001b[0m | \u001b[0m274.2 \u001b[0m | \u001b[0m485.7 \u001b[0m | \u001b[0m0.01819 \u001b[0m | \u001b[0m322.4 \u001b[0m | \u001b[0m2.207 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7945 - accuracy: 0.7722\n",
"| \u001b[0m78 \u001b[0m | \u001b[0m0.7722 \u001b[0m | \u001b[0m357.8 \u001b[0m | \u001b[0m358.0 \u001b[0m | \u001b[0m0.156 \u001b[0m | \u001b[0m227.1 \u001b[0m | \u001b[0m1.025 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9718 - accuracy: 0.7493\n",
"| \u001b[0m79 \u001b[0m | \u001b[0m0.7493 \u001b[0m | \u001b[0m98.62 \u001b[0m | \u001b[0m443.4 \u001b[0m | \u001b[0m0.1832 \u001b[0m | \u001b[0m324.6 \u001b[0m | \u001b[0m2.604 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8499 - accuracy: 0.7705\n",
"| \u001b[0m80 \u001b[0m | \u001b[0m0.7705 \u001b[0m | \u001b[0m269.4 \u001b[0m | \u001b[0m339.9 \u001b[0m | \u001b[0m0.2069 \u001b[0m | \u001b[0m375.3 \u001b[0m | \u001b[0m3.934 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9856 - accuracy: 0.7141\n",
"| \u001b[0m81 \u001b[0m | \u001b[0m0.7141 \u001b[0m | \u001b[0m112.0 \u001b[0m | \u001b[0m134.8 \u001b[0m | \u001b[0m0.2966 \u001b[0m | \u001b[0m326.7 \u001b[0m | \u001b[0m6.526 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8958 - accuracy: 0.6452\n",
"| \u001b[0m82 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m65.61 \u001b[0m | \u001b[0m72.35 \u001b[0m | \u001b[0m0.777 \u001b[0m | \u001b[0m130.1 \u001b[0m | \u001b[0m8.418 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.7559 - accuracy: 0.7087\n",
"| \u001b[0m83 \u001b[0m | \u001b[0m0.7087 \u001b[0m | \u001b[0m34.06 \u001b[0m | \u001b[0m366.5 \u001b[0m | \u001b[0m0.753 \u001b[0m | \u001b[0m394.7 \u001b[0m | \u001b[0m3.888 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7401 - accuracy: 0.7729\n",
"| \u001b[0m84 \u001b[0m | \u001b[0m0.7729 \u001b[0m | \u001b[0m163.7 \u001b[0m | \u001b[0m292.4 \u001b[0m | \u001b[0m0.6907 \u001b[0m | \u001b[0m95.2 \u001b[0m | \u001b[0m1.875 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7367 - accuracy: 0.6773\n",
"| \u001b[0m85 \u001b[0m | \u001b[0m0.6773 \u001b[0m | \u001b[0m227.4 \u001b[0m | \u001b[0m358.7 \u001b[0m | \u001b[0m0.8725 \u001b[0m | \u001b[0m94.66 \u001b[0m | \u001b[0m2.645 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8561 - accuracy: 0.6827\n",
"| \u001b[0m86 \u001b[0m | \u001b[0m0.6827 \u001b[0m | \u001b[0m475.6 \u001b[0m | \u001b[0m64.68 \u001b[0m | \u001b[0m0.3332 \u001b[0m | \u001b[0m90.03 \u001b[0m | \u001b[0m4.493 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8544 - accuracy: 0.5823\n",
"| \u001b[0m87 \u001b[0m | \u001b[0m0.5823 \u001b[0m | \u001b[0m129.1 \u001b[0m | \u001b[0m372.7 \u001b[0m | \u001b[0m0.7801 \u001b[0m | \u001b[0m174.6 \u001b[0m | \u001b[0m5.709 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7324 - accuracy: 0.7633\n",
"| \u001b[0m88 \u001b[0m | \u001b[0m0.7633 \u001b[0m | \u001b[0m71.88 \u001b[0m | \u001b[0m163.4 \u001b[0m | \u001b[0m0.7475 \u001b[0m | \u001b[0m272.7 \u001b[0m | \u001b[0m1.669 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7590 - accuracy: 0.7684\n",
"| \u001b[0m89 \u001b[0m | \u001b[0m0.7684 \u001b[0m | \u001b[0m503.2 \u001b[0m | \u001b[0m213.7 \u001b[0m | \u001b[0m0.07448 \u001b[0m | \u001b[0m262.9 \u001b[0m | \u001b[0m2.023 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8454 - accuracy: 0.6452\n",
"| \u001b[0m90 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m156.3 \u001b[0m | \u001b[0m260.6 \u001b[0m | \u001b[0m0.818 \u001b[0m | \u001b[0m12.66 \u001b[0m | \u001b[0m3.102 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8884 - accuracy: 0.7121\n",
"| \u001b[0m91 \u001b[0m | \u001b[0m0.7121 \u001b[0m | \u001b[0m52.86 \u001b[0m | \u001b[0m87.24 \u001b[0m | \u001b[0m0.4061 \u001b[0m | \u001b[0m202.9 \u001b[0m | \u001b[0m6.666 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.9676 - accuracy: 0.7630\n",
"| \u001b[0m92 \u001b[0m | \u001b[0m0.763 \u001b[0m | \u001b[0m97.82 \u001b[0m | \u001b[0m338.5 \u001b[0m | \u001b[0m0.3057 \u001b[0m | \u001b[0m372.3 \u001b[0m | \u001b[0m2.859 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8288 - accuracy: 0.7756\n",
"| \u001b[0m93 \u001b[0m | \u001b[0m0.7756 \u001b[0m | \u001b[0m419.9 \u001b[0m | \u001b[0m487.8 \u001b[0m | \u001b[0m0.01786 \u001b[0m | \u001b[0m102.2 \u001b[0m | \u001b[0m2.734 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9497 - accuracy: 0.7606\n",
"| \u001b[0m94 \u001b[0m | \u001b[0m0.7606 \u001b[0m | \u001b[0m209.2 \u001b[0m | \u001b[0m303.6 \u001b[0m | \u001b[0m0.01924 \u001b[0m | \u001b[0m385.0 \u001b[0m | \u001b[0m9.143 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.6898 - accuracy: 0.7339\n",
"| \u001b[0m95 \u001b[0m | \u001b[0m0.7339 \u001b[0m | \u001b[0m77.22 \u001b[0m | \u001b[0m74.69 \u001b[0m | \u001b[0m0.7958 \u001b[0m | \u001b[0m257.4 \u001b[0m | \u001b[0m1.626 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8500 - accuracy: 0.6773\n",
"| \u001b[0m96 \u001b[0m | \u001b[0m0.6773 \u001b[0m | \u001b[0m472.0 \u001b[0m | \u001b[0m81.66 \u001b[0m | \u001b[0m0.4519 \u001b[0m | \u001b[0m360.0 \u001b[0m | \u001b[0m6.709 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8469 - accuracy: 0.6776\n",
"| \u001b[0m97 \u001b[0m | \u001b[0m0.6776 \u001b[0m | \u001b[0m220.9 \u001b[0m | \u001b[0m48.63 \u001b[0m | \u001b[0m0.6451 \u001b[0m | \u001b[0m199.3 \u001b[0m | \u001b[0m3.171 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.9514 - accuracy: 0.7620\n",
"| \u001b[0m98 \u001b[0m | \u001b[0m0.762 \u001b[0m | \u001b[0m190.3 \u001b[0m | \u001b[0m100.4 \u001b[0m | \u001b[0m0.03956 \u001b[0m | \u001b[0m331.8 \u001b[0m | \u001b[0m3.842 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7783 - accuracy: 0.7022\n",
"| \u001b[0m99 \u001b[0m | \u001b[0m0.7022 \u001b[0m | \u001b[0m206.6 \u001b[0m | \u001b[0m423.1 \u001b[0m | \u001b[0m0.7579 \u001b[0m | \u001b[0m349.3 \u001b[0m | \u001b[0m2.218 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8718 - accuracy: 0.7739\n",
"| \u001b[0m100 \u001b[0m | \u001b[0m0.7739 \u001b[0m | \u001b[0m248.4 \u001b[0m | \u001b[0m342.5 \u001b[0m | \u001b[0m0.2937 \u001b[0m | \u001b[0m332.9 \u001b[0m | \u001b[0m4.584 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8453 - accuracy: 0.7650\n",
"| \u001b[0m101 \u001b[0m | \u001b[0m0.765 \u001b[0m | \u001b[0m236.6 \u001b[0m | \u001b[0m43.63 \u001b[0m | \u001b[0m0.05038 \u001b[0m | \u001b[0m21.21 \u001b[0m | \u001b[0m4.396 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8971 - accuracy: 0.6452\n",
"| \u001b[0m102 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m411.2 \u001b[0m | \u001b[0m210.7 \u001b[0m | \u001b[0m0.9247 \u001b[0m | \u001b[0m342.2 \u001b[0m | \u001b[0m3.514 \u001b[0m |\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7147 - accuracy: 0.6916\n",
"| \u001b[0m103 \u001b[0m | \u001b[0m0.6916 \u001b[0m | \u001b[0m505.5 \u001b[0m | \u001b[0m95.75 \u001b[0m | \u001b[0m0.9056 \u001b[0m | \u001b[0m86.15 \u001b[0m | \u001b[0m1.876 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8424 - accuracy: 0.6742\n",
"| \u001b[0m104 \u001b[0m | \u001b[0m0.6742 \u001b[0m | \u001b[0m192.2 \u001b[0m | \u001b[0m309.3 \u001b[0m | \u001b[0m0.5172 \u001b[0m | \u001b[0m15.75 \u001b[0m | \u001b[0m7.696 \u001b[0m |\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8234 - accuracy: 0.7719\n",
"| \u001b[0m105 \u001b[0m | \u001b[0m0.7719 \u001b[0m | \u001b[0m398.1 \u001b[0m | \u001b[0m94.3 \u001b[0m | \u001b[0m0.1851 \u001b[0m | \u001b[0m12.05 \u001b[0m | \u001b[0m1.841 \u001b[0m |\n",
"=====================================================================================\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.7717 - accuracy: 0.7643\n"
]
},
{
"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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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Hyperparameters: {'batch_size': 58.78430557146187, 'dense_units': 469.94105554913057, 'dropout_rate': 0.731846724190643, 'epochs': 207.76128908807675, 'num_hidden_layers': 1.3335894768318135}\n",
"Optimal Accuracy: 0.7643442749977112\n",
"92/92 [==============================] - 1s 7ms/step\n",
"[[1630 198 61]\n",
" [ 208 318 54]\n",
" [ 99 70 290]]\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def create_lstm_model(embedding_matrix, max_len, max_features, embed_size, lstm_units, num_hidden_layers, dropout_rate, dense_units):\n",
" \"\"\"Optimizing LSTM Models Using Bayesian Optimization\"\"\"\n",
" inp = Input(shape=(max_len,))\n",
" x = Embedding(max_features, embed_size, weights=[embedding_matrix])(inp)\n",
" x = SpatialDropout1D(dropout_rate)(x)\n",
" x = Bidirectional(LSTM(lstm_units, return_sequences=True))(x)\n",
" x = Flatten()(x)\n",
" for i in range(num_hidden_layers):\n",
" x = Dense(dense_units, activation='relu')(x)\n",
" x = Dropout(dropout_rate)(x)\n",
" outp = Dense(3, activation='softmax')(x)\n",
" model = Model(inputs=inp, outputs=outp)\n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
" return model\n",
"\n",
"\n",
"def lstm_model_fn(num_hidden_layers, dropout_rate, dense_units, batch_size, epochs, X_train, y_train, X_val, y_val):\n",
" model = create_lstm_model(embedding_matrix, X.shape[1], 13113, 100, 3, num_hidden_layers=int(num_hidden_layers), dropout_rate=dropout_rate, dense_units=dense_units)\n",
" early_stopping = EarlyStopping(monitor='val_loss', patience=5)\n",
" model.fit(X_train, y_train, batch_size=int(batch_size), epochs=int(epochs), validation_data=(X_val, y_val), verbose=0, callbacks=[early_stopping])\n",
" loss, acc = model.evaluate(X_val, y_val)\n",
" return acc\n",
"\n",
"def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
" if normalize:\n",
" cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
" plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" plt.title(title)\n",
" plt.colorbar()\n",
" tick_marks = np.arange(len(classes))\n",
" plt.xticks(tick_marks, classes, rotation=45)\n",
" plt.yticks(tick_marks, classes)\n",
" thresh = cm.max() / 2.\n",
" for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
" plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
" plt.tight_layout()\n",
" plt.ylabel('True label')\n",
" plt.xlabel('Predicted label')\n",
"\n",
"def optimize_lstm_model(X_train, y_train, X_val, y_val, batch_size, epochs):\n",
" \"\"\"Perform Bayesian optimization of the LSTM model\"\"\"\n",
" X_train = tf.convert_to_tensor(X_train)\n",
" y_train = tf.convert_to_tensor(y_train)\n",
" X_val = tf.convert_to_tensor(X_val)\n",
" y_val = tf.convert_to_tensor(y_val)\n",
" bo = BayesianOptimization(lambda num_hidden_layers, dropout_rate, dense_units, batch_size, epochs: \n",
" lstm_model_fn(num_hidden_layers, dropout_rate, dense_units, batch_size, epochs, X_train, y_train, X_val, y_val),\n",
" {'num_hidden_layers': (1, 10),\n",
" 'dropout_rate': (0.0, 1),\n",
" 'dense_units': (32, 512),\n",
" 'batch_size': (32, 512),\n",
" 'epochs': (10, 400)})\n",
" bo.maximize(n_iter=100)\n",
" best_params = bo.max['params']\n",
" num_hidden_layers = int(best_params['num_hidden_layers'])\n",
" dropout_rate = round(best_params['dropout_rate'], 1)\n",
" dense_units = int(best_params['dense_units'])\n",
" batch_size = int(best_params['batch_size'])\n",
" epochs = int(best_params['epochs'])\n",
" optimized_lstm_model = create_lstm_model(embedding_matrix, X.shape[1], 13113, 100, 3, num_hidden_layers=num_hidden_layers, dropout_rate=dropout_rate, dense_units=dense_units)\n",
" early_stopping = EarlyStopping(monitor='val_loss', patience=5)\n",
" history = optimized_lstm_model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val), verbose=0, callbacks=[early_stopping])\n",
" loss, acc = optimized_lstm_model.evaluate(X_val, y_val)\n",
" return optimized_lstm_model, history, acc, best_params\n",
"\n",
"\n",
"X_train, X_val, y_train, y_val = train_test_split(X, data['airline_sentiment'].values, test_size=0.2, random_state=42)\n",
"y_train = tf.keras.utils.to_categorical(y_train)\n",
"y_val = tf.keras.utils.to_categorical(y_val)\n",
"optimized_lstm_model, history, acc, best_params = optimize_lstm_model(X_train, y_train, X_val, y_val, batch_size=64, epochs=20)\n",
"\n",
"plt.plot(history.history['accuracy'], label='Optimized Model')\n",
"plt.title('Model accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(loc='upper left')\n",
"plt.show()\n",
"\n",
"plt.plot(history.history['loss'], label='Optimized Model')\n",
"plt.title('Model loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(loc='upper left')\n",
"plt.show()\n",
"\n",
"print(\"Optimal Hyperparameters:\", best_params)\n",
"print(\"Optimal Accuracy:\", acc)\n",
"\n",
"y_val_pred = optimized_lstm_model.predict(X_val)\n",
"y_val_pred = np.argmax(y_val_pred, axis=1)\n",
"y_val_true = np.argmax(y_val, axis=1)\n",
"\n",
"conf_matrix = confusion_matrix(y_val_true, y_val_pred)\n",
"print(conf_matrix)\n",
"\n",
"plt.figure()\n",
"plot_confusion_matrix(conf_matrix, classes=['Negative', 'Neutral', 'Positive'], title='Confusion matrix')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7c7496fe-0111-4e00-bdde-0cd022a4f6f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"92/92 [==============================] - 1s 8ms/step - loss: 0.8271 - accuracy: 0.6364 - val_loss: 0.7698 - val_accuracy: 0.6735\n",
"Epoch 2/5\n",
"92/92 [==============================] - 0s 4ms/step - loss: 0.7666 - accuracy: 0.6729 - val_loss: 0.7528 - val_accuracy: 0.6895\n",
"Epoch 3/5\n",
"92/92 [==============================] - 0s 4ms/step - loss: 0.7553 - accuracy: 0.6771 - val_loss: 0.7360 - val_accuracy: 0.6892\n",
"Epoch 4/5\n",
"92/92 [==============================] - 0s 4ms/step - loss: 0.7319 - accuracy: 0.6883 - val_loss: 0.7228 - val_accuracy: 0.7008\n",
"Epoch 5/5\n",
"92/92 [==============================] - 0s 4ms/step - loss: 0.7279 - accuracy: 0.6904 - val_loss: 0.7191 - val_accuracy: 0.6991\n",
"92/92 [==============================] - 0s 2ms/step - loss: 0.7191 - accuracy: 0.6991\n",
"Test Loss: 0.7190760374069214\n",
"Test Accuracy: 0.6991119980812073\n",
"Epoch 1/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.8389 - accuracy: 0.6342 - val_loss: 0.7820 - val_accuracy: 0.6633\n",
"Epoch 2/5\n",
"92/92 [==============================] - 0s 5ms/step - loss: 0.7854 - accuracy: 0.6631 - val_loss: 0.7637 - val_accuracy: 0.6622\n",
"Epoch 3/5\n",
"92/92 [==============================] - 0s 5ms/step - loss: 0.7593 - accuracy: 0.6828 - val_loss: 0.7345 - val_accuracy: 0.6967\n",
"Epoch 4/5\n",
"92/92 [==============================] - 0s 5ms/step - loss: 0.7335 - accuracy: 0.6930 - val_loss: 0.7152 - val_accuracy: 0.6981\n",
"Epoch 5/5\n",
"92/92 [==============================] - 0s 5ms/step - loss: 0.7281 - accuracy: 0.6926 - val_loss: 0.7110 - val_accuracy: 0.7036\n",
"92/92 [==============================] - 0s 2ms/step - loss: 0.7110 - accuracy: 0.7036\n",
"Test Loss: 0.7109825015068054\n",
"Test Accuracy: 0.7035518884658813\n",
"Epoch 1/5\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8470 - accuracy: 0.6277 - val_loss: 0.7981 - val_accuracy: 0.6544\n",
"Epoch 2/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.8070 - accuracy: 0.6454 - val_loss: 0.7779 - val_accuracy: 0.6574\n",
"Epoch 3/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.7916 - accuracy: 0.6562 - val_loss: 0.7552 - val_accuracy: 0.6745\n",
"Epoch 4/5\n",
"92/92 [==============================] - 0s 5ms/step - loss: 0.7759 - accuracy: 0.6639 - val_loss: 0.7565 - val_accuracy: 0.6660\n",
"Epoch 5/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.7640 - accuracy: 0.6670 - val_loss: 0.7576 - val_accuracy: 0.6725\n",
"92/92 [==============================] - 0s 3ms/step - loss: 0.7576 - accuracy: 0.6725\n",
"Test Loss: 0.7576203942298889\n",
"Test Accuracy: 0.6724726557731628\n",
"Epoch 1/5\n",
"92/92 [==============================] - 1s 7ms/step - loss: 0.8520 - accuracy: 0.6255 - val_loss: 0.8006 - val_accuracy: 0.6571\n",
"Epoch 2/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.8077 - accuracy: 0.6429 - val_loss: 0.8421 - val_accuracy: 0.6308\n",
"Epoch 3/5\n",
"92/92 [==============================] - 0s 5ms/step - loss: 0.7973 - accuracy: 0.6446 - val_loss: 0.7619 - val_accuracy: 0.6687\n",
"Epoch 4/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.7833 - accuracy: 0.6580 - val_loss: 0.7602 - val_accuracy: 0.6755\n",
"Epoch 5/5\n",
"92/92 [==============================] - 1s 6ms/step - loss: 0.7721 - accuracy: 0.6632 - val_loss: 0.7481 - val_accuracy: 0.6817\n",
"92/92 [==============================] - 0s 3ms/step - loss: 0.7481 - accuracy: 0.6817\n",
"Test Loss: 0.7481333613395691\n",
"Test Accuracy: 0.681693971157074\n"
]
},
{
"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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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def build_cnn_model(num_conv_layers, embedding_matrix, num_classes, max_sequence_length, embedding_dim, word_index):\n",
" \"\"\"Build a Convolutional Neural Network (CNN) model with a specified number of convolutional layers\"\"\"\n",
" model = Sequential()\n",
" model.add(Embedding(len(word_index) + 1,\n",
" embedding_dim,\n",
" weights=[embedding_matrix],\n",
" input_length=max_sequence_length,\n",
" trainable=False))\n",
" \n",
" for i in range(num_conv_layers-1):\n",
" model.add(Conv1D(128, 7, activation='relu', strides=1))\n",
" if max_sequence_length >= 2:\n",
" model.add(MaxPooling1D(2))\n",
" max_sequence_length = max_sequence_length // 2\n",
" else:\n",
" break\n",
" model.add(Conv1D(128, 7, activation='relu', strides=1))\n",
" model.add(Flatten())\n",
" model.add(Dense(128, activation='relu'))\n",
" model.add(Dense(num_classes, activation='softmax'))\n",
" model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
" return model\n",
"\n",
"\n",
"def train_cnn_model(model, X_train, y_train, X_test, y_test):\n",
" \"\"\"Train the CNN model on the training data\"\"\"\n",
" history = model.fit(X_train, y_train, epochs=5, batch_size=128, validation_data=(X_test, y_test))\n",
" test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
" print('Test Loss: {}'.format(test_loss))\n",
" print('Test Accuracy: {}'.format(test_accuracy))\n",
" return history\n",
"\n",
"def plot_cnn_model_results(history_list):\n",
" for i, history in enumerate(history_list):\n",
" plt.plot(history.history['accuracy'], label='Conv Layers: {}'.format(num_conv_layers[i]))\n",
" plt.xlabel('Epochs')\n",
" plt.ylabel('Accuracy')\n",
" plt.legend()\n",
" plt.show()\n",
"\n",
" for i, history in enumerate(history_list):\n",
" plt.plot(history.history['loss'], label='Conv Layers: {}'.format(num_conv_layers[i]))\n",
" plt.xlabel('Epochs')\n",
" plt.ylabel('Loss')\n",
" plt.legend()\n",
" plt.show()\n",
"\n",
"data = load_data()\n",
"data = clean_dataset(data)\n",
"data['text'] = data['text'].apply(lambda x: clean_text(x))\n",
"X, tokenizer = tokenize_text(data)\n",
"model = word2vec_model(data)\n",
"embedding_matrix = create_embedding_matrix(tokenizer, model)\n",
"\n",
"y = pd.get_dummies(data['airline_sentiment']).values\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"num_conv_layers = [1, 2, 3, 4]\n",
"num_classes = 3\n",
"max_sequence_length = 100\n",
"embedding_dim = 100\n",
"X, tokenizer = tokenize_text(data)\n",
"word_index = tokenizer.word_index\n",
"history_list = []\n",
"\n",
"for i in num_conv_layers:\n",
" model = build_cnn_model(i, embedding_matrix, num_classes, max_sequence_length, embedding_dim, word_index)\n",
" history = train_cnn_model(model, X_train, y_train, X_test, y_test)\n",
" history_list.append(history)\n",
"\n",
"plot_cnn_model_results(history_list)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "f2bdb3eb-9fe0-4257-b7fe-67761b7decf7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| iter | target | batch_... | dropou... | epochs | learni... | num_la... |\n",
"-------------------------------------------------------------------------------------\n",
"| \u001b[0m1 \u001b[0m | \u001b[0m0.6936 \u001b[0m | \u001b[0m36.48 \u001b[0m | \u001b[0m0.2743 \u001b[0m | \u001b[0m209.6 \u001b[0m | \u001b[0m0.02107 \u001b[0m | \u001b[0m1.565 \u001b[0m |\n",
"| \u001b[0m2 \u001b[0m | \u001b[0m0.6489 \u001b[0m | \u001b[0m47.23 \u001b[0m | \u001b[0m0.4975 \u001b[0m | \u001b[0m9.369 \u001b[0m | \u001b[0m0.09674 \u001b[0m | \u001b[0m7.404 \u001b[0m |\n",
"| \u001b[95m3 \u001b[0m | \u001b[95m0.6977 \u001b[0m | \u001b[95m94.44 \u001b[0m | \u001b[95m0.2544 \u001b[0m | \u001b[95m250.0 \u001b[0m | \u001b[95m0.06977 \u001b[0m | \u001b[95m8.601 \u001b[0m |\n",
"| \u001b[95m4 \u001b[0m | \u001b[95m0.7018 \u001b[0m | \u001b[95m87.17 \u001b[0m | \u001b[95m0.1407 \u001b[0m | \u001b[95m251.8 \u001b[0m | \u001b[95m0.09803 \u001b[0m | \u001b[95m9.996 \u001b[0m |\n",
"| \u001b[0m5 \u001b[0m | \u001b[0m0.6824 \u001b[0m | \u001b[0m34.87 \u001b[0m | \u001b[0m0.4939 \u001b[0m | \u001b[0m104.7 \u001b[0m | \u001b[0m0.08569 \u001b[0m | \u001b[0m7.277 \u001b[0m |\n",
"| \u001b[0m6 \u001b[0m | \u001b[0m0.6424 \u001b[0m | \u001b[0m38.92 \u001b[0m | \u001b[0m0.1 \u001b[0m | \u001b[0m297.1 \u001b[0m | \u001b[0m0.1 \u001b[0m | \u001b[0m10.0 \u001b[0m |\n",
"| \u001b[0m7 \u001b[0m | \u001b[0m0.638 \u001b[0m | \u001b[0m75.98 \u001b[0m | \u001b[0m0.1 \u001b[0m | \u001b[0m217.8 \u001b[0m | \u001b[0m0.1 \u001b[0m | \u001b[0m10.0 \u001b[0m |\n",
"| \u001b[0m8 \u001b[0m | \u001b[0m0.6878 \u001b[0m | \u001b[0m94.0 \u001b[0m | \u001b[0m0.1023 \u001b[0m | \u001b[0m250.5 \u001b[0m | \u001b[0m0.08952 \u001b[0m | \u001b[0m7.936 \u001b[0m |\n",
"| \u001b[0m9 \u001b[0m | \u001b[0m0.6998 \u001b[0m | \u001b[0m87.82 \u001b[0m | \u001b[0m0.2634 \u001b[0m | \u001b[0m248.0 \u001b[0m | \u001b[0m0.06755 \u001b[0m | \u001b[0m8.73 \u001b[0m |\n",
"| \u001b[95m10 \u001b[0m | \u001b[95m0.7046 \u001b[0m | \u001b[95m94.9 \u001b[0m | \u001b[95m0.1225 \u001b[0m | \u001b[95m244.5 \u001b[0m | \u001b[95m0.05493 \u001b[0m | \u001b[95m9.987 \u001b[0m |\n",
"| \u001b[0m11 \u001b[0m | \u001b[0m0.6455 \u001b[0m | \u001b[0m102.8 \u001b[0m | \u001b[0m0.4441 \u001b[0m | \u001b[0m247.4 \u001b[0m | \u001b[0m0.04848 \u001b[0m | \u001b[0m7.944 \u001b[0m |\n",
"| \u001b[0m12 \u001b[0m | \u001b[0m0.6827 \u001b[0m | \u001b[0m91.3 \u001b[0m | \u001b[0m0.1045 \u001b[0m | \u001b[0m244.5 \u001b[0m | \u001b[0m0.07148 \u001b[0m | \u001b[0m8.066 \u001b[0m |\n",
"| \u001b[0m13 \u001b[0m | \u001b[0m0.6452 \u001b[0m | \u001b[0m83.57 \u001b[0m | \u001b[0m0.3033 \u001b[0m | \u001b[0m250.9 \u001b[0m | \u001b[0m0.05518 \u001b[0m | \u001b[0m6.518 \u001b[0m |\n",
"| \u001b[0m14 \u001b[0m | \u001b[0m0.6404 \u001b[0m | \u001b[0m96.69 \u001b[0m | \u001b[0m0.2464 \u001b[0m | \u001b[0m241.2 \u001b[0m | \u001b[0m0.06245 \u001b[0m | \u001b[0m9.924 \u001b[0m |\n",
"| \u001b[0m15 \u001b[0m | \u001b[0m0.6919 \u001b[0m | \u001b[0m89.16 \u001b[0m | \u001b[0m0.1396 \u001b[0m | \u001b[0m251.4 \u001b[0m | \u001b[0m0.08644 \u001b[0m | \u001b[0m8.421 \u001b[0m |\n",
"| \u001b[0m16 \u001b[0m | \u001b[0m0.695 \u001b[0m | \u001b[0m88.64 \u001b[0m | \u001b[0m0.1101 \u001b[0m | \u001b[0m255.3 \u001b[0m | \u001b[0m0.05139 \u001b[0m | \u001b[0m8.49 \u001b[0m |\n",
"| \u001b[0m17 \u001b[0m | \u001b[0m0.6544 \u001b[0m | \u001b[0m93.9 \u001b[0m | \u001b[0m0.3928 \u001b[0m | \u001b[0m246.1 \u001b[0m | \u001b[0m0.02148 \u001b[0m | \u001b[0m5.481 \u001b[0m |\n",
"| \u001b[0m18 \u001b[0m | \u001b[0m0.6998 \u001b[0m | \u001b[0m89.29 \u001b[0m | \u001b[0m0.1904 \u001b[0m | \u001b[0m248.8 \u001b[0m | \u001b[0m0.03374 \u001b[0m | \u001b[0m9.884 \u001b[0m |\n",
"| \u001b[0m19 \u001b[0m | \u001b[0m0.6503 \u001b[0m | \u001b[0m92.46 \u001b[0m | \u001b[0m0.3666 \u001b[0m | \u001b[0m246.6 \u001b[0m | \u001b[0m0.008682 \u001b[0m | \u001b[0m9.642 \u001b[0m |\n",
"| \u001b[0m20 \u001b[0m | \u001b[0m0.6974 \u001b[0m | \u001b[0m89.25 \u001b[0m | \u001b[0m0.2156 \u001b[0m | \u001b[0m247.7 \u001b[0m | \u001b[0m0.0328 \u001b[0m | \u001b[0m7.589 \u001b[0m |\n",
"| \u001b[0m21 \u001b[0m | \u001b[0m0.7039 \u001b[0m | \u001b[0m85.45 \u001b[0m | \u001b[0m0.1237 \u001b[0m | \u001b[0m254.5 \u001b[0m | \u001b[0m0.09225 \u001b[0m | \u001b[0m8.904 \u001b[0m |\n",
"| \u001b[0m22 \u001b[0m | \u001b[0m0.652 \u001b[0m | \u001b[0m84.69 \u001b[0m | \u001b[0m0.4321 \u001b[0m | \u001b[0m256.0 \u001b[0m | \u001b[0m0.08278 \u001b[0m | \u001b[0m9.693 \u001b[0m |\n",
"| \u001b[0m23 \u001b[0m | \u001b[0m0.6383 \u001b[0m | \u001b[0m87.26 \u001b[0m | \u001b[0m0.3227 \u001b[0m | \u001b[0m253.9 \u001b[0m | \u001b[0m0.02428 \u001b[0m | \u001b[0m9.921 \u001b[0m |\n",
"| \u001b[0m24 \u001b[0m | \u001b[0m0.6503 \u001b[0m | \u001b[0m95.25 \u001b[0m | \u001b[0m0.3167 \u001b[0m | \u001b[0m244.1 \u001b[0m | \u001b[0m0.06384 \u001b[0m | \u001b[0m8.638 \u001b[0m |\n",
"| \u001b[0m25 \u001b[0m | \u001b[0m0.6462 \u001b[0m | \u001b[0m87.24 \u001b[0m | \u001b[0m0.4593 \u001b[0m | \u001b[0m247.6 \u001b[0m | \u001b[0m0.05232 \u001b[0m | \u001b[0m7.703 \u001b[0m |\n",
"| \u001b[0m26 \u001b[0m | \u001b[0m0.693 \u001b[0m | \u001b[0m87.16 \u001b[0m | \u001b[0m0.2173 \u001b[0m | \u001b[0m248.3 \u001b[0m | \u001b[0m0.09428 \u001b[0m | \u001b[0m8.542 \u001b[0m |\n",
"| \u001b[0m27 \u001b[0m | \u001b[0m0.6899 \u001b[0m | \u001b[0m87.12 \u001b[0m | \u001b[0m0.3605 \u001b[0m | \u001b[0m251.8 \u001b[0m | \u001b[0m0.0428 \u001b[0m | \u001b[0m9.857 \u001b[0m |\n",
"| \u001b[0m28 \u001b[0m | \u001b[0m0.6547 \u001b[0m | \u001b[0m89.17 \u001b[0m | \u001b[0m0.4966 \u001b[0m | \u001b[0m248.2 \u001b[0m | \u001b[0m0.08755 \u001b[0m | \u001b[0m7.503 \u001b[0m |\n",
"| \u001b[0m29 \u001b[0m | \u001b[0m0.6902 \u001b[0m | \u001b[0m87.25 \u001b[0m | \u001b[0m0.1939 \u001b[0m | \u001b[0m247.2 \u001b[0m | \u001b[0m0.02345 \u001b[0m | \u001b[0m9.531 \u001b[0m |\n",
"| \u001b[0m30 \u001b[0m | \u001b[0m0.6981 \u001b[0m | \u001b[0m94.47 \u001b[0m | \u001b[0m0.1852 \u001b[0m | \u001b[0m251.0 \u001b[0m | \u001b[0m0.03884 \u001b[0m | \u001b[0m9.752 \u001b[0m |\n",
"| \u001b[0m31 \u001b[0m | \u001b[0m0.6995 \u001b[0m | \u001b[0m87.73 \u001b[0m | \u001b[0m0.1633 \u001b[0m | \u001b[0m248.5 \u001b[0m | \u001b[0m0.03568 \u001b[0m | \u001b[0m8.992 \u001b[0m |\n",
"| \u001b[0m32 \u001b[0m | \u001b[0m0.6523 \u001b[0m | \u001b[0m87.82 \u001b[0m | \u001b[0m0.3855 \u001b[0m | \u001b[0m248.7 \u001b[0m | \u001b[0m0.03588 \u001b[0m | \u001b[0m9.471 \u001b[0m |\n",
"| \u001b[0m33 \u001b[0m | \u001b[0m0.6383 \u001b[0m | \u001b[0m94.61 \u001b[0m | \u001b[0m0.3067 \u001b[0m | \u001b[0m251.5 \u001b[0m | \u001b[0m0.06 \u001b[0m | \u001b[0m9.524 \u001b[0m |\n",
"| \u001b[0m34 \u001b[0m | \u001b[0m0.6936 \u001b[0m | \u001b[0m93.73 \u001b[0m | \u001b[0m0.3441 \u001b[0m | \u001b[0m250.2 \u001b[0m | \u001b[0m0.09534 \u001b[0m | \u001b[0m8.398 \u001b[0m |\n",
"| \u001b[0m35 \u001b[0m | \u001b[0m0.6383 \u001b[0m | \u001b[0m86.97 \u001b[0m | \u001b[0m0.283 \u001b[0m | \u001b[0m247.7 \u001b[0m | \u001b[0m0.08164 \u001b[0m | \u001b[0m9.417 \u001b[0m |\n",
"| \u001b[0m36 \u001b[0m | \u001b[0m0.7018 \u001b[0m | \u001b[0m36.8 \u001b[0m | \u001b[0m0.1156 \u001b[0m | \u001b[0m209.2 \u001b[0m | \u001b[0m0.09417 \u001b[0m | \u001b[0m2.055 \u001b[0m |\n",
"| \u001b[0m37 \u001b[0m | \u001b[0m0.6964 \u001b[0m | \u001b[0m89.07 \u001b[0m | \u001b[0m0.2478 \u001b[0m | \u001b[0m250.5 \u001b[0m | \u001b[0m0.07444 \u001b[0m | \u001b[0m8.476 \u001b[0m |\n",
"| \u001b[0m38 \u001b[0m | \u001b[0m0.6523 \u001b[0m | \u001b[0m89.67 \u001b[0m | \u001b[0m0.3255 \u001b[0m | \u001b[0m178.5 \u001b[0m | \u001b[0m0.08929 \u001b[0m | \u001b[0m6.118 \u001b[0m |\n",
"| \u001b[0m39 \u001b[0m | \u001b[0m0.6923 \u001b[0m | \u001b[0m88.1 \u001b[0m | \u001b[0m0.3987 \u001b[0m | \u001b[0m255.0 \u001b[0m | \u001b[0m0.09509 \u001b[0m | \u001b[0m9.5 \u001b[0m |\n",
"| \u001b[0m40 \u001b[0m | \u001b[0m0.6964 \u001b[0m | \u001b[0m36.41 \u001b[0m | \u001b[0m0.455 \u001b[0m | \u001b[0m209.5 \u001b[0m | \u001b[0m0.08216 \u001b[0m | \u001b[0m1.007 \u001b[0m |\n",
"| \u001b[0m41 \u001b[0m | \u001b[0m0.6943 \u001b[0m | \u001b[0m88.02 \u001b[0m | \u001b[0m0.1017 \u001b[0m | \u001b[0m251.1 \u001b[0m | \u001b[0m0.06464 \u001b[0m | \u001b[0m8.507 \u001b[0m |\n",
"| \u001b[0m42 \u001b[0m | \u001b[0m0.7046 \u001b[0m | \u001b[0m36.73 \u001b[0m | \u001b[0m0.3379 \u001b[0m | \u001b[0m208.8 \u001b[0m | \u001b[0m0.0007407\u001b[0m | \u001b[0m2.44 \u001b[0m |\n",
"| \u001b[0m43 \u001b[0m | \u001b[0m0.6974 \u001b[0m | \u001b[0m88.63 \u001b[0m | \u001b[0m0.2341 \u001b[0m | \u001b[0m396.7 \u001b[0m | \u001b[0m0.09807 \u001b[0m | \u001b[0m7.998 \u001b[0m |\n",
"| \u001b[0m44 \u001b[0m | \u001b[0m0.6872 \u001b[0m | \u001b[0m87.51 \u001b[0m | \u001b[0m0.1173 \u001b[0m | \u001b[0m251.1 \u001b[0m | \u001b[0m0.09924 \u001b[0m | \u001b[0m8.437 \u001b[0m |\n",
"| \u001b[0m45 \u001b[0m | \u001b[0m0.7039 \u001b[0m | \u001b[0m18.1 \u001b[0m | \u001b[0m0.3759 \u001b[0m | \u001b[0m323.3 \u001b[0m | \u001b[0m0.04517 \u001b[0m | \u001b[0m2.033 \u001b[0m |\n",
"| \u001b[0m46 \u001b[0m | \u001b[0m0.6892 \u001b[0m | \u001b[0m125.1 \u001b[0m | \u001b[0m0.3336 \u001b[0m | \u001b[0m168.0 \u001b[0m | \u001b[0m0.04671 \u001b[0m | \u001b[0m8.403 \u001b[0m |\n",
"| \u001b[0m47 \u001b[0m | \u001b[0m0.6762 \u001b[0m | \u001b[0m25.01 \u001b[0m | \u001b[0m0.3853 \u001b[0m | \u001b[0m117.8 \u001b[0m | \u001b[0m0.0825 \u001b[0m | \u001b[0m7.726 \u001b[0m |\n",
"| \u001b[0m48 \u001b[0m | \u001b[0m0.6537 \u001b[0m | \u001b[0m88.86 \u001b[0m | \u001b[0m0.3772 \u001b[0m | \u001b[0m251.2 \u001b[0m | \u001b[0m0.07836 \u001b[0m | \u001b[0m8.031 \u001b[0m |\n",
"| \u001b[0m49 \u001b[0m | \u001b[0m0.7042 \u001b[0m | \u001b[0m37.36 \u001b[0m | \u001b[0m0.2163 \u001b[0m | \u001b[0m208.9 \u001b[0m | \u001b[0m0.06489 \u001b[0m | \u001b[0m2.111 \u001b[0m |\n",
"| \u001b[0m50 \u001b[0m | \u001b[0m0.652 \u001b[0m | \u001b[0m88.99 \u001b[0m | \u001b[0m0.112 \u001b[0m | \u001b[0m248.7 \u001b[0m | \u001b[0m0.09799 \u001b[0m | \u001b[0m9.357 \u001b[0m |\n",
"| \u001b[0m51 \u001b[0m | \u001b[0m0.6902 \u001b[0m | \u001b[0m31.17 \u001b[0m | \u001b[0m0.1795 \u001b[0m | \u001b[0m99.6 \u001b[0m | \u001b[0m0.06258 \u001b[0m | \u001b[0m7.142 \u001b[0m |\n",
"| \u001b[0m52 \u001b[0m | \u001b[0m0.6571 \u001b[0m | \u001b[0m80.52 \u001b[0m | \u001b[0m0.1736 \u001b[0m | \u001b[0m383.1 \u001b[0m | \u001b[0m0.006422 \u001b[0m | \u001b[0m5.812 \u001b[0m |\n",
"| \u001b[0m53 \u001b[0m | \u001b[0m0.7012 \u001b[0m | \u001b[0m89.49 \u001b[0m | \u001b[0m0.1413 \u001b[0m | \u001b[0m249.7 \u001b[0m | \u001b[0m0.06067 \u001b[0m | \u001b[0m9.612 \u001b[0m |\n",
"| \u001b[0m54 \u001b[0m | \u001b[0m0.6571 \u001b[0m | \u001b[0m106.3 \u001b[0m | \u001b[0m0.1592 \u001b[0m | \u001b[0m294.3 \u001b[0m | \u001b[0m0.01921 \u001b[0m | \u001b[0m6.119 \u001b[0m |\n",
"| \u001b[0m55 \u001b[0m | \u001b[0m0.6506 \u001b[0m | \u001b[0m63.23 \u001b[0m | \u001b[0m0.4745 \u001b[0m | \u001b[0m125.9 \u001b[0m | \u001b[0m0.09986 \u001b[0m | \u001b[0m6.264 \u001b[0m |\n",
"| \u001b[0m56 \u001b[0m | \u001b[0m0.6967 \u001b[0m | \u001b[0m60.52 \u001b[0m | \u001b[0m0.2882 \u001b[0m | \u001b[0m216.0 \u001b[0m | \u001b[0m0.02677 \u001b[0m | \u001b[0m5.648 \u001b[0m |\n",
"| \u001b[0m57 \u001b[0m | \u001b[0m0.6479 \u001b[0m | \u001b[0m39.4 \u001b[0m | \u001b[0m0.382 \u001b[0m | \u001b[0m6.732 \u001b[0m | \u001b[0m0.07786 \u001b[0m | \u001b[0m7.888 \u001b[0m |\n",
"| \u001b[0m58 \u001b[0m | \u001b[0m0.7005 \u001b[0m | \u001b[0m71.33 \u001b[0m | \u001b[0m0.2781 \u001b[0m | \u001b[0m265.6 \u001b[0m | \u001b[0m0.09997 \u001b[0m | \u001b[0m7.485 \u001b[0m |\n",
"| \u001b[0m59 \u001b[0m | \u001b[0m0.6977 \u001b[0m | \u001b[0m45.11 \u001b[0m | \u001b[0m0.1637 \u001b[0m | \u001b[0m288.2 \u001b[0m | \u001b[0m0.02485 \u001b[0m | \u001b[0m7.181 \u001b[0m |\n",
"| \u001b[95m60 \u001b[0m | \u001b[95m0.7097 \u001b[0m | \u001b[95m80.61 \u001b[0m | \u001b[95m0.172 \u001b[0m | \u001b[95m245.6 \u001b[0m | \u001b[95m0.04662 \u001b[0m | \u001b[95m3.456 \u001b[0m |\n",
"| \u001b[0m61 \u001b[0m | \u001b[0m0.6554 \u001b[0m | \u001b[0m88.71 \u001b[0m | \u001b[0m0.3804 \u001b[0m | \u001b[0m396.8 \u001b[0m | \u001b[0m0.06647 \u001b[0m | \u001b[0m7.531 \u001b[0m |\n",
"| \u001b[0m62 \u001b[0m | \u001b[0m0.6947 \u001b[0m | \u001b[0m92.72 \u001b[0m | \u001b[0m0.2612 \u001b[0m | \u001b[0m12.78 \u001b[0m | \u001b[0m0.02669 \u001b[0m | \u001b[0m2.967 \u001b[0m |\n",
"| \u001b[0m63 \u001b[0m | \u001b[0m0.695 \u001b[0m | \u001b[0m121.1 \u001b[0m | \u001b[0m0.4407 \u001b[0m | \u001b[0m107.7 \u001b[0m | \u001b[0m0.001808 \u001b[0m | \u001b[0m2.28 \u001b[0m |\n",
"| \u001b[0m64 \u001b[0m | \u001b[0m0.7087 \u001b[0m | \u001b[0m80.98 \u001b[0m | \u001b[0m0.146 \u001b[0m | \u001b[0m343.6 \u001b[0m | \u001b[0m0.06931 \u001b[0m | \u001b[0m3.363 \u001b[0m |\n",
"| \u001b[0m65 \u001b[0m | \u001b[0m0.7039 \u001b[0m | \u001b[0m87.27 \u001b[0m | \u001b[0m0.1223 \u001b[0m | \u001b[0m250.6 \u001b[0m | \u001b[0m0.006868 \u001b[0m | \u001b[0m8.841 \u001b[0m |\n",
"| \u001b[0m66 \u001b[0m | \u001b[0m0.7008 \u001b[0m | \u001b[0m82.67 \u001b[0m | \u001b[0m0.1049 \u001b[0m | \u001b[0m102.4 \u001b[0m | \u001b[0m0.004092 \u001b[0m | \u001b[0m7.938 \u001b[0m |\n",
"| \u001b[0m67 \u001b[0m | \u001b[0m0.6943 \u001b[0m | \u001b[0m85.53 \u001b[0m | \u001b[0m0.2972 \u001b[0m | \u001b[0m254.9 \u001b[0m | \u001b[0m0.07472 \u001b[0m | \u001b[0m8.815 \u001b[0m |\n",
"| \u001b[0m68 \u001b[0m | \u001b[0m0.6899 \u001b[0m | \u001b[0m93.77 \u001b[0m | \u001b[0m0.3258 \u001b[0m | \u001b[0m250.7 \u001b[0m | \u001b[0m0.06886 \u001b[0m | \u001b[0m8.914 \u001b[0m |\n",
"| \u001b[0m69 \u001b[0m | \u001b[0m0.6926 \u001b[0m | \u001b[0m31.16 \u001b[0m | \u001b[0m0.4106 \u001b[0m | \u001b[0m301.3 \u001b[0m | \u001b[0m0.09515 \u001b[0m | \u001b[0m7.186 \u001b[0m |\n",
"| \u001b[0m70 \u001b[0m | \u001b[0m0.6544 \u001b[0m | \u001b[0m66.07 \u001b[0m | \u001b[0m0.3163 \u001b[0m | \u001b[0m103.0 \u001b[0m | \u001b[0m0.02833 \u001b[0m | \u001b[0m4.123 \u001b[0m |\n",
"| \u001b[0m71 \u001b[0m | \u001b[0m0.6533 \u001b[0m | \u001b[0m125.4 \u001b[0m | \u001b[0m0.1518 \u001b[0m | \u001b[0m41.38 \u001b[0m | \u001b[0m0.02661 \u001b[0m | \u001b[0m9.759 \u001b[0m |\n",
"| \u001b[0m72 \u001b[0m | \u001b[0m0.6469 \u001b[0m | \u001b[0m18.27 \u001b[0m | \u001b[0m0.4598 \u001b[0m | \u001b[0m340.5 \u001b[0m | \u001b[0m0.07045 \u001b[0m | \u001b[0m9.223 \u001b[0m |\n",
"| \u001b[0m73 \u001b[0m | \u001b[0m0.6984 \u001b[0m | \u001b[0m81.66 \u001b[0m | \u001b[0m0.4267 \u001b[0m | \u001b[0m226.1 \u001b[0m | \u001b[0m0.09965 \u001b[0m | \u001b[0m5.26 \u001b[0m |\n",
"| \u001b[0m74 \u001b[0m | \u001b[0m0.6434 \u001b[0m | \u001b[0m87.23 \u001b[0m | \u001b[0m0.1668 \u001b[0m | \u001b[0m251.3 \u001b[0m | \u001b[0m0.04407 \u001b[0m | \u001b[0m8.646 \u001b[0m |\n",
"| \u001b[0m75 \u001b[0m | \u001b[0m0.6933 \u001b[0m | \u001b[0m121.0 \u001b[0m | \u001b[0m0.2405 \u001b[0m | \u001b[0m287.2 \u001b[0m | \u001b[0m0.03584 \u001b[0m | \u001b[0m8.237 \u001b[0m |\n",
"| \u001b[0m76 \u001b[0m | \u001b[0m0.694 \u001b[0m | \u001b[0m113.9 \u001b[0m | \u001b[0m0.2165 \u001b[0m | \u001b[0m337.8 \u001b[0m | \u001b[0m0.0483 \u001b[0m | \u001b[0m6.754 \u001b[0m |\n",
"| \u001b[0m77 \u001b[0m | \u001b[0m0.6417 \u001b[0m | \u001b[0m84.97 \u001b[0m | \u001b[0m0.2491 \u001b[0m | \u001b[0m122.3 \u001b[0m | \u001b[0m0.0387 \u001b[0m | \u001b[0m5.55 \u001b[0m |\n",
"| \u001b[0m78 \u001b[0m | \u001b[0m0.7039 \u001b[0m | \u001b[0m80.77 \u001b[0m | \u001b[0m0.2552 \u001b[0m | \u001b[0m245.7 \u001b[0m | \u001b[0m0.05286 \u001b[0m | \u001b[0m3.703 \u001b[0m |\n",
"| \u001b[0m79 \u001b[0m | \u001b[0m0.6875 \u001b[0m | \u001b[0m94.24 \u001b[0m | \u001b[0m0.4 \u001b[0m | \u001b[0m244.7 \u001b[0m | \u001b[0m0.02661 \u001b[0m | \u001b[0m9.801 \u001b[0m |\n",
"| \u001b[0m80 \u001b[0m | \u001b[0m0.6482 \u001b[0m | \u001b[0m82.92 \u001b[0m | \u001b[0m0.1691 \u001b[0m | \u001b[0m102.5 \u001b[0m | \u001b[0m0.08551 \u001b[0m | \u001b[0m8.08 \u001b[0m |\n",
"| \u001b[0m81 \u001b[0m | \u001b[0m0.6568 \u001b[0m | \u001b[0m107.2 \u001b[0m | \u001b[0m0.2953 \u001b[0m | \u001b[0m336.1 \u001b[0m | \u001b[0m0.03915 \u001b[0m | \u001b[0m6.592 \u001b[0m |\n",
"| \u001b[0m82 \u001b[0m | \u001b[0m0.6977 \u001b[0m | \u001b[0m36.56 \u001b[0m | \u001b[0m0.3523 \u001b[0m | \u001b[0m209.7 \u001b[0m | \u001b[0m0.04892 \u001b[0m | \u001b[0m1.727 \u001b[0m |\n",
"| \u001b[0m83 \u001b[0m | \u001b[0m0.7008 \u001b[0m | \u001b[0m59.05 \u001b[0m | \u001b[0m0.2114 \u001b[0m | \u001b[0m29.37 \u001b[0m | \u001b[0m0.09336 \u001b[0m | \u001b[0m4.941 \u001b[0m |\n",
"| \u001b[0m84 \u001b[0m | \u001b[0m0.7015 \u001b[0m | \u001b[0m36.78 \u001b[0m | \u001b[0m0.3009 \u001b[0m | \u001b[0m209.7 \u001b[0m | \u001b[0m0.03775 \u001b[0m | \u001b[0m2.377 \u001b[0m |\n",
"| \u001b[0m85 \u001b[0m | \u001b[0m0.696 \u001b[0m | \u001b[0m39.26 \u001b[0m | \u001b[0m0.3022 \u001b[0m | \u001b[0m259.2 \u001b[0m | \u001b[0m0.03419 \u001b[0m | \u001b[0m6.198 \u001b[0m |\n",
"| \u001b[0m86 \u001b[0m | \u001b[0m0.6851 \u001b[0m | \u001b[0m60.8 \u001b[0m | \u001b[0m0.4489 \u001b[0m | \u001b[0m216.9 \u001b[0m | \u001b[0m0.04156 \u001b[0m | \u001b[0m5.798 \u001b[0m |\n",
"| \u001b[0m87 \u001b[0m | \u001b[0m0.6458 \u001b[0m | \u001b[0m64.26 \u001b[0m | \u001b[0m0.2584 \u001b[0m | \u001b[0m172.4 \u001b[0m | \u001b[0m0.09484 \u001b[0m | \u001b[0m9.84 \u001b[0m |\n",
"| \u001b[0m88 \u001b[0m | \u001b[0m0.7042 \u001b[0m | \u001b[0m121.4 \u001b[0m | \u001b[0m0.2491 \u001b[0m | \u001b[0m107.2 \u001b[0m | \u001b[0m0.01023 \u001b[0m | \u001b[0m2.042 \u001b[0m |\n",
"| \u001b[0m89 \u001b[0m | \u001b[0m0.6421 \u001b[0m | \u001b[0m94.92 \u001b[0m | \u001b[0m0.261 \u001b[0m | \u001b[0m250.1 \u001b[0m | \u001b[0m0.003578 \u001b[0m | \u001b[0m9.154 \u001b[0m |\n",
"| \u001b[0m90 \u001b[0m | \u001b[0m0.6503 \u001b[0m | \u001b[0m60.99 \u001b[0m | \u001b[0m0.3489 \u001b[0m | \u001b[0m215.9 \u001b[0m | \u001b[0m0.028 \u001b[0m | \u001b[0m5.653 \u001b[0m |\n",
"| \u001b[0m91 \u001b[0m | \u001b[0m0.6588 \u001b[0m | \u001b[0m105.5 \u001b[0m | \u001b[0m0.3812 \u001b[0m | \u001b[0m117.6 \u001b[0m | \u001b[0m0.06066 \u001b[0m | \u001b[0m5.621 \u001b[0m |\n",
"| \u001b[0m92 \u001b[0m | \u001b[0m0.6581 \u001b[0m | \u001b[0m81.6 \u001b[0m | \u001b[0m0.2883 \u001b[0m | \u001b[0m347.8 \u001b[0m | \u001b[0m0.02707 \u001b[0m | \u001b[0m6.95 \u001b[0m |\n",
"| \u001b[0m93 \u001b[0m | \u001b[0m0.7087 \u001b[0m | \u001b[0m88.41 \u001b[0m | \u001b[0m0.1724 \u001b[0m | \u001b[0m396.7 \u001b[0m | \u001b[0m0.05117 \u001b[0m | \u001b[0m8.23 \u001b[0m |\n",
"| \u001b[0m94 \u001b[0m | \u001b[0m0.6977 \u001b[0m | \u001b[0m78.53 \u001b[0m | \u001b[0m0.2813 \u001b[0m | \u001b[0m261.8 \u001b[0m | \u001b[0m0.02875 \u001b[0m | \u001b[0m6.574 \u001b[0m |\n",
"| \u001b[0m95 \u001b[0m | \u001b[0m0.6885 \u001b[0m | \u001b[0m89.24 \u001b[0m | \u001b[0m0.3107 \u001b[0m | \u001b[0m250.7 \u001b[0m | \u001b[0m0.07773 \u001b[0m | \u001b[0m9.058 \u001b[0m |\n",
"| \u001b[0m96 \u001b[0m | \u001b[0m0.6933 \u001b[0m | \u001b[0m94.24 \u001b[0m | \u001b[0m0.2658 \u001b[0m | \u001b[0m244.2 \u001b[0m | \u001b[0m0.08937 \u001b[0m | \u001b[0m9.93 \u001b[0m |\n",
"| \u001b[0m97 \u001b[0m | \u001b[0m0.6936 \u001b[0m | \u001b[0m54.42 \u001b[0m | \u001b[0m0.2317 \u001b[0m | \u001b[0m381.3 \u001b[0m | \u001b[0m0.0526 \u001b[0m | \u001b[0m8.87 \u001b[0m |\n",
"| \u001b[0m98 \u001b[0m | \u001b[0m0.6503 \u001b[0m | \u001b[0m78.67 \u001b[0m | \u001b[0m0.2715 \u001b[0m | \u001b[0m260.7 \u001b[0m | \u001b[0m0.07622 \u001b[0m | \u001b[0m6.915 \u001b[0m |\n",
"| \u001b[0m99 \u001b[0m | \u001b[0m0.6267 \u001b[0m | \u001b[0m84.9 \u001b[0m | \u001b[0m0.2625 \u001b[0m | \u001b[0m254.9 \u001b[0m | \u001b[0m0.03752 \u001b[0m | \u001b[0m9.074 \u001b[0m |\n",
"| \u001b[0m100 \u001b[0m | \u001b[0m0.6814 \u001b[0m | \u001b[0m93.55 \u001b[0m | \u001b[0m0.327 \u001b[0m | \u001b[0m250.6 \u001b[0m | \u001b[0m0.006296 \u001b[0m | \u001b[0m8.898 \u001b[0m |\n",
"| \u001b[0m101 \u001b[0m | \u001b[0m0.7032 \u001b[0m | \u001b[0m23.75 \u001b[0m | \u001b[0m0.1801 \u001b[0m | \u001b[0m343.1 \u001b[0m | \u001b[0m0.07436 \u001b[0m | \u001b[0m2.674 \u001b[0m |\n",
"| \u001b[0m102 \u001b[0m | \u001b[0m0.6954 \u001b[0m | \u001b[0m97.95 \u001b[0m | \u001b[0m0.1611 \u001b[0m | \u001b[0m79.47 \u001b[0m | \u001b[0m0.01244 \u001b[0m | \u001b[0m9.094 \u001b[0m |\n",
"| \u001b[0m103 \u001b[0m | \u001b[0m0.6865 \u001b[0m | \u001b[0m94.92 \u001b[0m | \u001b[0m0.4583 \u001b[0m | \u001b[0m213.1 \u001b[0m | \u001b[0m0.01643 \u001b[0m | \u001b[0m8.858 \u001b[0m |\n",
"| \u001b[0m104 \u001b[0m | \u001b[0m0.68 \u001b[0m | \u001b[0m60.45 \u001b[0m | \u001b[0m0.4264 \u001b[0m | \u001b[0m216.0 \u001b[0m | \u001b[0m0.01997 \u001b[0m | \u001b[0m6.157 \u001b[0m |\n",
"| \u001b[0m105 \u001b[0m | \u001b[0m0.7022 \u001b[0m | \u001b[0m18.81 \u001b[0m | \u001b[0m0.3384 \u001b[0m | \u001b[0m323.6 \u001b[0m | \u001b[0m0.09052 \u001b[0m | \u001b[0m3.01 \u001b[0m |\n",
"=====================================================================================\n"
]
},
{
"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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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"92/92 [==============================] - 1s 5ms/step\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Parameters: {'batch_size': 80.60994217700157, 'dropout_rate': 0.1720054175062761, 'epochs': 245.56376643441425, 'learning_rate': 0.04662117756562055, 'num_layers': 3.4561312110268023}\n",
"Accuracy: 0.7025273442268372\n"
]
}
],
"source": [
"def build_model(num_layers, dropout_rate):\n",
" \"\"\"Optimizing CNN Models Using Bayesian Optimization\"\"\"\n",
" model = Sequential()\n",
" model.add(Embedding(len(word_index) + 1, 100, weights=[embedding_matrix], input_length=100, trainable=False))\n",
" model.add(SpatialDropout1D(dropout_rate))\n",
" model.add(Conv1D(64, 5, activation='relu'))\n",
" model.add(MaxPooling1D(pool_size=2))\n",
" for i in range(int(num_layers)):\n",
" model.add(LSTM(100, return_sequences=True))\n",
" model.add(Flatten())\n",
" model.add(Dense(3, activation='softmax'))\n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
" return model\n",
"\n",
"def cnn_model(batch_size, epochs, learning_rate, dropout_rate, num_layers):\n",
" K.clear_session()\n",
" model = build_model(num_layers, dropout_rate)\n",
" model.fit(X_train, y_train, batch_size=int(batch_size), epochs=int(epochs), verbose=0, validation_data=(X_test, y_test), callbacks=[EarlyStopping(patience=3, restore_best_weights=True)])\n",
" score, acc = model.evaluate(X_test, y_test, verbose=0)\n",
" return acc\n",
"\n",
"cnn_bo = BayesianOptimization(cnn_model, {'batch_size': (16, 128), 'epochs': (5, 400), 'learning_rate': (0.0001, 0.1), 'dropout_rate': (0.1, 0.5), 'num_layers': (1, 10)})\n",
"cnn_bo.maximize(init_points=5, n_iter=100)\n",
"\n",
"best_params = cnn_bo.max['params']\n",
"best_model = build_model(best_params['num_layers'], best_params['dropout_rate'])\n",
"history = best_model.fit(X_train, y_train, batch_size=int(best_params['batch_size']), epochs=int(best_params['epochs']), verbose=0, validation_data=(X_test, y_test), callbacks=[EarlyStopping(patience=3, restore_best_weights=True)])\n",
"\n",
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('Model Accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['Train', 'Validation'], loc='upper left')\n",
"plt.show()\n",
"\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('Model Loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['Train', 'Validation'], loc='upper left')\n",
"plt.show()\n",
"\n",
"y_pred = np.argmax(best_model.predict(X_test), axis=1)\n",
"conf_matrix = sklearn.metrics.confusion_matrix(np.argmax(y_test, axis=1), y_pred).astype(np.float32)\n",
"\n",
"plt.imshow(conf_matrix, cmap=plt.cm.Blues)\n",
"plt.xlabel(\"Predicted labels\")\n",
"plt.ylabel(\"True labels\")\n",
"plt.xticks([], [])\n",
"plt.yticks([], [])\n",
"plt.title('Confusion matrix ')\n",
"plt.colorbar()\n",
"\n",
"thresh = conf_matrix.max() / 2.\n",
"for i in range(conf_matrix.shape[0]):\n",
" for j in range(conf_matrix.shape[1]):\n",
" plt.text(j, i, int(conf_matrix[i, j]),\n",
" horizontalalignment=\"center\",\n",
" color=\"white\" if conf_matrix[i, j] > thresh else \"black\")\n",
"\n",
"plt.show()\n",
"\n",
"\n",
"\n",
"print(\"Optimal Parameters: \", best_params)\n",
"score, acc = best_model.evaluate(X_test, y_test, verbose=0)\n",
"print(\"Accuracy: \", acc)"
]
}
],
"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": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
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