Upload 2 files
Browse files- chatbot_(0_3)_.ipynb +801 -0
- intents.json +0 -0
chatbot_(0_3)_.ipynb
ADDED
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@@ -0,0 +1,801 @@
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| 1 |
+
{
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| 2 |
+
"nbformat": 4,
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| 3 |
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"nbformat_minor": 0,
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| 4 |
+
"metadata": {
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| 5 |
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"colab": {
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| 6 |
+
"provenance": []
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| 7 |
+
},
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| 8 |
+
"kernelspec": {
|
| 9 |
+
"name": "python3",
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| 10 |
+
"display_name": "Python 3"
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| 11 |
+
},
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| 12 |
+
"language_info": {
|
| 13 |
+
"name": "python"
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| 14 |
+
}
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| 15 |
+
},
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| 16 |
+
"cells": [
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| 17 |
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{
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| 18 |
+
"cell_type": "code",
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| 19 |
+
"source": [
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| 20 |
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"from google.colab import drive\n",
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| 21 |
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"drive.mount('/content/drive')"
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| 22 |
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],
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| 23 |
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"metadata": {
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| 24 |
+
"colab": {
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| 25 |
+
"base_uri": "https://localhost:8080/"
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| 26 |
+
},
|
| 27 |
+
"id": "hmExUq6HSDSb",
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| 28 |
+
"outputId": "0a1c9821-bf29-4cd4-9888-7d2070382203"
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| 29 |
+
},
|
| 30 |
+
"execution_count": 8,
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| 31 |
+
"outputs": [
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| 32 |
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{
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| 33 |
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"output_type": "stream",
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| 34 |
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"name": "stdout",
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| 35 |
+
"text": [
|
| 36 |
+
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
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| 37 |
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]
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| 38 |
+
}
|
| 39 |
+
]
|
| 40 |
+
},
|
| 41 |
+
{
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| 42 |
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"cell_type": "code",
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| 43 |
+
"source": [
|
| 44 |
+
"import nltk\n",
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| 45 |
+
"nltk.download('punkt')\n",
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| 46 |
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"nltk.download('wordnet')\n"
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| 47 |
+
],
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+
"metadata": {
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+
"colab": {
|
| 50 |
+
"base_uri": "https://localhost:8080/"
|
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+
},
|
| 52 |
+
"id": "zEmFVujkVCkF",
|
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+
"outputId": "63c2024d-f4a0-411d-e45e-4138020c208f"
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+
},
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| 55 |
+
"execution_count": 16,
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+
"outputs": [
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+
{
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+
"output_type": "stream",
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+
"name": "stderr",
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+
"text": [
|
| 61 |
+
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
|
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+
"[nltk_data] Package punkt is already up-to-date!\n",
|
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+
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
|
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+
]
|
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+
},
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+
{
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+
"output_type": "execute_result",
|
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+
"data": {
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+
"text/plain": [
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+
"True"
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+
]
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+
},
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+
"metadata": {},
|
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+
"execution_count": 16
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+
}
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+
]
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+
},
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+
{
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+
"cell_type": "code",
|
| 80 |
+
"execution_count": 9,
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+
"metadata": {
|
| 82 |
+
"id": "QRLEoc-hR6Ym"
|
| 83 |
+
},
|
| 84 |
+
"outputs": [],
|
| 85 |
+
"source": [
|
| 86 |
+
"import json\n",
|
| 87 |
+
"import random\n",
|
| 88 |
+
"import numpy as np\n",
|
| 89 |
+
"import nltk\n",
|
| 90 |
+
"from tensorflow.keras.models import Sequential\n",
|
| 91 |
+
"from tensorflow.keras.layers import Dense, Dropout\n",
|
| 92 |
+
"from tensorflow.keras.optimizers import SGD\n",
|
| 93 |
+
"from sklearn.preprocessing import LabelEncoder\n",
|
| 94 |
+
"from nltk.stem import WordNetLemmatizer"
|
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+
]
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+
},
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+
{
|
| 98 |
+
"cell_type": "code",
|
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+
"source": [
|
| 100 |
+
"file_path = '/content/drive/MyDrive/Colab_Notebooks/Dataset/intents.json'\n",
|
| 101 |
+
"with open(file_path,'r') as file:\n",
|
| 102 |
+
" data = json.load(file)"
|
| 103 |
+
],
|
| 104 |
+
"metadata": {
|
| 105 |
+
"id": "1aX_MbxJSBJ_"
|
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+
},
|
| 107 |
+
"execution_count": 10,
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+
"outputs": []
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+
},
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+
{
|
| 111 |
+
"cell_type": "code",
|
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+
"source": [
|
| 113 |
+
"lemmatizer = WordNetLemmatizer()\n",
|
| 114 |
+
"words = []\n",
|
| 115 |
+
"classes = []\n",
|
| 116 |
+
"documents = []\n",
|
| 117 |
+
"ignore_words = ['?', '!', '.']"
|
| 118 |
+
],
|
| 119 |
+
"metadata": {
|
| 120 |
+
"id": "1jC5aPxOSJj-"
|
| 121 |
+
},
|
| 122 |
+
"execution_count": 11,
|
| 123 |
+
"outputs": []
|
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+
},
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| 125 |
+
{
|
| 126 |
+
"cell_type": "code",
|
| 127 |
+
"source": [
|
| 128 |
+
"for intent in data['intents']:\n",
|
| 129 |
+
" for pattern in intent['patterns']:\n",
|
| 130 |
+
" # Tokenize each word\n",
|
| 131 |
+
" word_list = nltk.word_tokenize(pattern)\n",
|
| 132 |
+
" words.extend(word_list)\n",
|
| 133 |
+
" documents.append((word_list, intent['tag']))\n",
|
| 134 |
+
" if intent['tag'] not in classes:\n",
|
| 135 |
+
" classes.append(intent['tag'])"
|
| 136 |
+
],
|
| 137 |
+
"metadata": {
|
| 138 |
+
"id": "eUHE55adSKXc"
|
| 139 |
+
},
|
| 140 |
+
"execution_count": 14,
|
| 141 |
+
"outputs": []
|
| 142 |
+
},
|
| 143 |
+
{
|
| 144 |
+
"cell_type": "code",
|
| 145 |
+
"source": [
|
| 146 |
+
"words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
|
| 147 |
+
"words = sorted(list(set(words)))\n",
|
| 148 |
+
"classes = sorted(list(set(classes)))"
|
| 149 |
+
],
|
| 150 |
+
"metadata": {
|
| 151 |
+
"id": "DLMKZLnOSOxW"
|
| 152 |
+
},
|
| 153 |
+
"execution_count": 17,
|
| 154 |
+
"outputs": []
|
| 155 |
+
},
|
| 156 |
+
{
|
| 157 |
+
"cell_type": "code",
|
| 158 |
+
"source": [
|
| 159 |
+
"training = []\n",
|
| 160 |
+
"output_empty = [0] * len(classes)"
|
| 161 |
+
],
|
| 162 |
+
"metadata": {
|
| 163 |
+
"id": "t3bXEHe9SQd3"
|
| 164 |
+
},
|
| 165 |
+
"execution_count": 18,
|
| 166 |
+
"outputs": []
|
| 167 |
+
},
|
| 168 |
+
{
|
| 169 |
+
"cell_type": "code",
|
| 170 |
+
"source": [
|
| 171 |
+
"for doc in documents:\n",
|
| 172 |
+
" bag = []\n",
|
| 173 |
+
" word_patterns = doc[0]\n",
|
| 174 |
+
" word_patterns = [lemmatizer.lemmatize(word.lower()) for word in word_patterns]\n",
|
| 175 |
+
" for w in words:\n",
|
| 176 |
+
" bag.append(1 if w in word_patterns else 0)\n",
|
| 177 |
+
"\n",
|
| 178 |
+
" output_row = list(output_empty)\n",
|
| 179 |
+
" output_row[classes.index(doc[1])] = 1\n",
|
| 180 |
+
" training.append([bag, output_row])"
|
| 181 |
+
],
|
| 182 |
+
"metadata": {
|
| 183 |
+
"id": "71s2dR6gSTMW"
|
| 184 |
+
},
|
| 185 |
+
"execution_count": 19,
|
| 186 |
+
"outputs": []
|
| 187 |
+
},
|
| 188 |
+
{
|
| 189 |
+
"cell_type": "code",
|
| 190 |
+
"source": [
|
| 191 |
+
"random.shuffle(training)\n",
|
| 192 |
+
"training = np.array(training, dtype=object)\n",
|
| 193 |
+
"\n",
|
| 194 |
+
"train_x = np.array(list(training[:, 0]))\n",
|
| 195 |
+
"train_y = np.array(list(training[:, 1]))"
|
| 196 |
+
],
|
| 197 |
+
"metadata": {
|
| 198 |
+
"id": "PP2T232eSVfu"
|
| 199 |
+
},
|
| 200 |
+
"execution_count": 20,
|
| 201 |
+
"outputs": []
|
| 202 |
+
},
|
| 203 |
+
{
|
| 204 |
+
"cell_type": "code",
|
| 205 |
+
"source": [
|
| 206 |
+
"model = Sequential()\n",
|
| 207 |
+
"model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))\n",
|
| 208 |
+
"model.add(Dropout(0.5))\n",
|
| 209 |
+
"model.add(Dense(64, activation='relu'))\n",
|
| 210 |
+
"model.add(Dropout(0.5))\n",
|
| 211 |
+
"model.add(Dense(len(train_y[0]), activation='softmax'))\n"
|
| 212 |
+
],
|
| 213 |
+
"metadata": {
|
| 214 |
+
"colab": {
|
| 215 |
+
"base_uri": "https://localhost:8080/"
|
| 216 |
+
},
|
| 217 |
+
"id": "JwzrpAgUSXmZ",
|
| 218 |
+
"outputId": "065d2848-7e12-410e-9060-630178f1d44b"
|
| 219 |
+
},
|
| 220 |
+
"execution_count": 21,
|
| 221 |
+
"outputs": [
|
| 222 |
+
{
|
| 223 |
+
"output_type": "stream",
|
| 224 |
+
"name": "stderr",
|
| 225 |
+
"text": [
|
| 226 |
+
"/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
|
| 227 |
+
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
|
| 228 |
+
]
|
| 229 |
+
}
|
| 230 |
+
]
|
| 231 |
+
},
|
| 232 |
+
{
|
| 233 |
+
"cell_type": "code",
|
| 234 |
+
"source": [
|
| 235 |
+
"sgd = SGD(learning_rate=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
|
| 236 |
+
"model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
|
| 237 |
+
],
|
| 238 |
+
"metadata": {
|
| 239 |
+
"colab": {
|
| 240 |
+
"base_uri": "https://localhost:8080/"
|
| 241 |
+
},
|
| 242 |
+
"id": "aX6rGbQ3SaAu",
|
| 243 |
+
"outputId": "0ed24b6e-ac06-49ed-8f5c-7b7d5799fa5d"
|
| 244 |
+
},
|
| 245 |
+
"execution_count": 22,
|
| 246 |
+
"outputs": [
|
| 247 |
+
{
|
| 248 |
+
"output_type": "stream",
|
| 249 |
+
"name": "stderr",
|
| 250 |
+
"text": [
|
| 251 |
+
"/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/base_optimizer.py:33: UserWarning: Argument `decay` is no longer supported and will be ignored.\n",
|
| 252 |
+
" warnings.warn(\n"
|
| 253 |
+
]
|
| 254 |
+
}
|
| 255 |
+
]
|
| 256 |
+
},
|
| 257 |
+
{
|
| 258 |
+
"cell_type": "code",
|
| 259 |
+
"source": [
|
| 260 |
+
"hist = model.fit(train_x, train_y, epochs=200, batch_size=5, verbose=1)\n",
|
| 261 |
+
"model.save('chatbot_model.h5', hist)\n",
|
| 262 |
+
"\n",
|
| 263 |
+
"print(\"Model created and saved successfully!\")"
|
| 264 |
+
],
|
| 265 |
+
"metadata": {
|
| 266 |
+
"colab": {
|
| 267 |
+
"base_uri": "https://localhost:8080/"
|
| 268 |
+
},
|
| 269 |
+
"id": "shBuA76PScjj",
|
| 270 |
+
"outputId": "1840358c-df15-4a74-dc12-5b2eda3c7251"
|
| 271 |
+
},
|
| 272 |
+
"execution_count": 23,
|
| 273 |
+
"outputs": [
|
| 274 |
+
{
|
| 275 |
+
"output_type": "stream",
|
| 276 |
+
"name": "stdout",
|
| 277 |
+
"text": [
|
| 278 |
+
"Epoch 1/200\n",
|
| 279 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.0160 - loss: 3.9287\n",
|
| 280 |
+
"Epoch 2/200\n",
|
| 281 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0375 - loss: 3.9009\n",
|
| 282 |
+
"Epoch 3/200\n",
|
| 283 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0701 - loss: 3.8703\n",
|
| 284 |
+
"Epoch 4/200\n",
|
| 285 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.0944 - loss: 3.8231\n",
|
| 286 |
+
"Epoch 5/200\n",
|
| 287 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1473 - loss: 3.7111\n",
|
| 288 |
+
"Epoch 6/200\n",
|
| 289 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1221 - loss: 3.6674\n",
|
| 290 |
+
"Epoch 7/200\n",
|
| 291 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1828 - loss: 3.5505\n",
|
| 292 |
+
"Epoch 8/200\n",
|
| 293 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1029 - loss: 3.4937\n",
|
| 294 |
+
"Epoch 9/200\n",
|
| 295 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.1679 - loss: 3.3554\n",
|
| 296 |
+
"Epoch 10/200\n",
|
| 297 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1912 - loss: 3.0371\n",
|
| 298 |
+
"Epoch 11/200\n",
|
| 299 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1387 - loss: 3.1178\n",
|
| 300 |
+
"Epoch 12/200\n",
|
| 301 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2451 - loss: 2.8440\n",
|
| 302 |
+
"Epoch 13/200\n",
|
| 303 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1636 - loss: 2.8542\n",
|
| 304 |
+
"Epoch 14/200\n",
|
| 305 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3178 - loss: 2.6346\n",
|
| 306 |
+
"Epoch 15/200\n",
|
| 307 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3606 - loss: 2.3944\n",
|
| 308 |
+
"Epoch 16/200\n",
|
| 309 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4408 - loss: 2.2182\n",
|
| 310 |
+
"Epoch 17/200\n",
|
| 311 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4852 - loss: 2.0750\n",
|
| 312 |
+
"Epoch 18/200\n",
|
| 313 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4978 - loss: 1.8592\n",
|
| 314 |
+
"Epoch 19/200\n",
|
| 315 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4109 - loss: 1.9696\n",
|
| 316 |
+
"Epoch 20/200\n",
|
| 317 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6099 - loss: 1.7249\n",
|
| 318 |
+
"Epoch 21/200\n",
|
| 319 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5910 - loss: 1.5929\n",
|
| 320 |
+
"Epoch 22/200\n",
|
| 321 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6170 - loss: 1.5090\n",
|
| 322 |
+
"Epoch 23/200\n",
|
| 323 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5359 - loss: 1.5326\n",
|
| 324 |
+
"Epoch 24/200\n",
|
| 325 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6133 - loss: 1.3116\n",
|
| 326 |
+
"Epoch 25/200\n",
|
| 327 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5605 - loss: 1.5211\n",
|
| 328 |
+
"Epoch 26/200\n",
|
| 329 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6173 - loss: 1.3756\n",
|
| 330 |
+
"Epoch 27/200\n",
|
| 331 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6295 - loss: 1.3038 \n",
|
| 332 |
+
"Epoch 28/200\n",
|
| 333 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6551 - loss: 1.0944\n",
|
| 334 |
+
"Epoch 29/200\n",
|
| 335 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6298 - loss: 1.1905\n",
|
| 336 |
+
"Epoch 30/200\n",
|
| 337 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6804 - loss: 1.2018\n",
|
| 338 |
+
"Epoch 31/200\n",
|
| 339 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mβοΏ½οΏ½ββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7484 - loss: 0.8515\n",
|
| 340 |
+
"Epoch 32/200\n",
|
| 341 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6634 - loss: 1.0130\n",
|
| 342 |
+
"Epoch 33/200\n",
|
| 343 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7496 - loss: 0.9618\n",
|
| 344 |
+
"Epoch 34/200\n",
|
| 345 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7764 - loss: 0.7866\n",
|
| 346 |
+
"Epoch 35/200\n",
|
| 347 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7919 - loss: 0.7579\n",
|
| 348 |
+
"Epoch 36/200\n",
|
| 349 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7773 - loss: 0.7759\n",
|
| 350 |
+
"Epoch 37/200\n",
|
| 351 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7958 - loss: 0.7796\n",
|
| 352 |
+
"Epoch 38/200\n",
|
| 353 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7835 - loss: 0.6777\n",
|
| 354 |
+
"Epoch 39/200\n",
|
| 355 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6933 - loss: 0.9080\n",
|
| 356 |
+
"Epoch 40/200\n",
|
| 357 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7136 - loss: 0.8473 \n",
|
| 358 |
+
"Epoch 41/200\n",
|
| 359 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7721 - loss: 0.7652\n",
|
| 360 |
+
"Epoch 42/200\n",
|
| 361 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8115 - loss: 0.7462\n",
|
| 362 |
+
"Epoch 43/200\n",
|
| 363 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8388 - loss: 0.6034\n",
|
| 364 |
+
"Epoch 44/200\n",
|
| 365 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8115 - loss: 0.5538\n",
|
| 366 |
+
"Epoch 45/200\n",
|
| 367 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7534 - loss: 0.7057\n",
|
| 368 |
+
"Epoch 46/200\n",
|
| 369 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7961 - loss: 0.7454 \n",
|
| 370 |
+
"Epoch 47/200\n",
|
| 371 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7371 - loss: 0.8724\n",
|
| 372 |
+
"Epoch 48/200\n",
|
| 373 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8563 - loss: 0.5258\n",
|
| 374 |
+
"Epoch 49/200\n",
|
| 375 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8373 - loss: 0.6451 \n",
|
| 376 |
+
"Epoch 50/200\n",
|
| 377 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8705 - loss: 0.4699\n",
|
| 378 |
+
"Epoch 51/200\n",
|
| 379 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8362 - loss: 0.5688 \n",
|
| 380 |
+
"Epoch 52/200\n",
|
| 381 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7597 - loss: 0.6519\n",
|
| 382 |
+
"Epoch 53/200\n",
|
| 383 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8160 - loss: 0.6018\n",
|
| 384 |
+
"Epoch 54/200\n",
|
| 385 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7629 - loss: 0.6320\n",
|
| 386 |
+
"Epoch 55/200\n",
|
| 387 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8370 - loss: 0.5583\n",
|
| 388 |
+
"Epoch 56/200\n",
|
| 389 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8217 - loss: 0.5044\n",
|
| 390 |
+
"Epoch 57/200\n",
|
| 391 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7954 - loss: 0.6656\n",
|
| 392 |
+
"Epoch 58/200\n",
|
| 393 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8589 - loss: 0.5088\n",
|
| 394 |
+
"Epoch 59/200\n",
|
| 395 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8512 - loss: 0.5375\n",
|
| 396 |
+
"Epoch 60/200\n",
|
| 397 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7823 - loss: 0.6725\n",
|
| 398 |
+
"Epoch 61/200\n",
|
| 399 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7613 - loss: 0.6719\n",
|
| 400 |
+
"Epoch 62/200\n",
|
| 401 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8928 - loss: 0.3940\n",
|
| 402 |
+
"Epoch 63/200\n",
|
| 403 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9465 - loss: 0.3387\n",
|
| 404 |
+
"Epoch 64/200\n",
|
| 405 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8715 - loss: 0.4471\n",
|
| 406 |
+
"Epoch 65/200\n",
|
| 407 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8565 - loss: 0.4623\n",
|
| 408 |
+
"Epoch 66/200\n",
|
| 409 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8506 - loss: 0.4804\n",
|
| 410 |
+
"Epoch 67/200\n",
|
| 411 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8375 - loss: 0.5659\n",
|
| 412 |
+
"Epoch 68/200\n",
|
| 413 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8722 - loss: 0.3798\n",
|
| 414 |
+
"Epoch 69/200\n",
|
| 415 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8782 - loss: 0.4696\n",
|
| 416 |
+
"Epoch 70/200\n",
|
| 417 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8051 - loss: 0.5934\n",
|
| 418 |
+
"Epoch 71/200\n",
|
| 419 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8398 - loss: 0.5181\n",
|
| 420 |
+
"Epoch 72/200\n",
|
| 421 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8964 - loss: 0.3433\n",
|
| 422 |
+
"Epoch 73/200\n",
|
| 423 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9192 - loss: 0.2828\n",
|
| 424 |
+
"Epoch 74/200\n",
|
| 425 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8640 - loss: 0.3554 \n",
|
| 426 |
+
"Epoch 75/200\n",
|
| 427 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8770 - loss: 0.3985 \n",
|
| 428 |
+
"Epoch 76/200\n",
|
| 429 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8394 - loss: 0.3654\n",
|
| 430 |
+
"Epoch 77/200\n",
|
| 431 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8361 - loss: 0.4137 \n",
|
| 432 |
+
"Epoch 78/200\n",
|
| 433 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8550 - loss: 0.4868\n",
|
| 434 |
+
"Epoch 79/200\n",
|
| 435 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8201 - loss: 0.5561\n",
|
| 436 |
+
"Epoch 80/200\n",
|
| 437 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9044 - loss: 0.3645\n",
|
| 438 |
+
"Epoch 81/200\n",
|
| 439 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8462 - loss: 0.5026\n",
|
| 440 |
+
"Epoch 82/200\n",
|
| 441 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8316 - loss: 0.6040 \n",
|
| 442 |
+
"Epoch 83/200\n",
|
| 443 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8938 - loss: 0.3195\n",
|
| 444 |
+
"Epoch 84/200\n",
|
| 445 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8799 - loss: 0.4521\n",
|
| 446 |
+
"Epoch 85/200\n",
|
| 447 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9154 - loss: 0.3184\n",
|
| 448 |
+
"Epoch 86/200\n",
|
| 449 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8469 - loss: 0.4821\n",
|
| 450 |
+
"Epoch 87/200\n",
|
| 451 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8531 - loss: 0.3506\n",
|
| 452 |
+
"Epoch 88/200\n",
|
| 453 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9220 - loss: 0.3493\n",
|
| 454 |
+
"Epoch 89/200\n",
|
| 455 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8935 - loss: 0.3584\n",
|
| 456 |
+
"Epoch 90/200\n",
|
| 457 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8833 - loss: 0.2929\n",
|
| 458 |
+
"Epoch 91/200\n",
|
| 459 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8852 - loss: 0.2960\n",
|
| 460 |
+
"Epoch 92/200\n",
|
| 461 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8893 - loss: 0.2969\n",
|
| 462 |
+
"Epoch 93/200\n",
|
| 463 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9165 - loss: 0.2847\n",
|
| 464 |
+
"Epoch 94/200\n",
|
| 465 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9291 - loss: 0.3413\n",
|
| 466 |
+
"Epoch 95/200\n",
|
| 467 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9300 - loss: 0.2546\n",
|
| 468 |
+
"Epoch 96/200\n",
|
| 469 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mβββββοΏ½οΏ½ββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9270 - loss: 0.2407\n",
|
| 470 |
+
"Epoch 97/200\n",
|
| 471 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9204 - loss: 0.2162\n",
|
| 472 |
+
"Epoch 98/200\n",
|
| 473 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8915 - loss: 0.2711\n",
|
| 474 |
+
"Epoch 99/200\n",
|
| 475 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9051 - loss: 0.3305\n",
|
| 476 |
+
"Epoch 100/200\n",
|
| 477 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8633 - loss: 0.5580\n",
|
| 478 |
+
"Epoch 101/200\n",
|
| 479 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8823 - loss: 0.3063\n",
|
| 480 |
+
"Epoch 102/200\n",
|
| 481 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9480 - loss: 0.2321\n",
|
| 482 |
+
"Epoch 103/200\n",
|
| 483 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8639 - loss: 0.3623\n",
|
| 484 |
+
"Epoch 104/200\n",
|
| 485 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8504 - loss: 0.3309\n",
|
| 486 |
+
"Epoch 105/200\n",
|
| 487 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9191 - loss: 0.3501\n",
|
| 488 |
+
"Epoch 106/200\n",
|
| 489 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9379 - loss: 0.2638\n",
|
| 490 |
+
"Epoch 107/200\n",
|
| 491 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8833 - loss: 0.3927\n",
|
| 492 |
+
"Epoch 108/200\n",
|
| 493 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8745 - loss: 0.3595\n",
|
| 494 |
+
"Epoch 109/200\n",
|
| 495 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9051 - loss: 0.3232\n",
|
| 496 |
+
"Epoch 110/200\n",
|
| 497 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8815 - loss: 0.3396\n",
|
| 498 |
+
"Epoch 111/200\n",
|
| 499 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9430 - loss: 0.1751\n",
|
| 500 |
+
"Epoch 112/200\n",
|
| 501 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9491 - loss: 0.2361\n",
|
| 502 |
+
"Epoch 113/200\n",
|
| 503 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8514 - loss: 0.3512\n",
|
| 504 |
+
"Epoch 114/200\n",
|
| 505 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9327 - loss: 0.1607\n",
|
| 506 |
+
"Epoch 115/200\n",
|
| 507 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9445 - loss: 0.2465\n",
|
| 508 |
+
"Epoch 116/200\n",
|
| 509 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8794 - loss: 0.3028\n",
|
| 510 |
+
"Epoch 117/200\n",
|
| 511 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8682 - loss: 0.3845\n",
|
| 512 |
+
"Epoch 118/200\n",
|
| 513 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9307 - loss: 0.2463\n",
|
| 514 |
+
"Epoch 119/200\n",
|
| 515 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9004 - loss: 0.3431\n",
|
| 516 |
+
"Epoch 120/200\n",
|
| 517 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7727 - loss: 0.6520\n",
|
| 518 |
+
"Epoch 121/200\n",
|
| 519 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8614 - loss: 0.4168\n",
|
| 520 |
+
"Epoch 122/200\n",
|
| 521 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8701 - loss: 0.3500\n",
|
| 522 |
+
"Epoch 123/200\n",
|
| 523 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9274 - loss: 0.3727\n",
|
| 524 |
+
"Epoch 124/200\n",
|
| 525 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8651 - loss: 0.3869\n",
|
| 526 |
+
"Epoch 125/200\n",
|
| 527 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9107 - loss: 0.2356\n",
|
| 528 |
+
"Epoch 126/200\n",
|
| 529 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9005 - loss: 0.2736\n",
|
| 530 |
+
"Epoch 127/200\n",
|
| 531 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8431 - loss: 0.3525\n",
|
| 532 |
+
"Epoch 128/200\n",
|
| 533 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8840 - loss: 0.2562\n",
|
| 534 |
+
"Epoch 129/200\n",
|
| 535 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9278 - loss: 0.3064\n",
|
| 536 |
+
"Epoch 130/200\n",
|
| 537 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9347 - loss: 0.2516\n",
|
| 538 |
+
"Epoch 131/200\n",
|
| 539 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9443 - loss: 0.2612\n",
|
| 540 |
+
"Epoch 132/200\n",
|
| 541 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8448 - loss: 0.4872\n",
|
| 542 |
+
"Epoch 133/200\n",
|
| 543 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9076 - loss: 0.3019\n",
|
| 544 |
+
"Epoch 134/200\n",
|
| 545 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8769 - loss: 0.3769\n",
|
| 546 |
+
"Epoch 135/200\n",
|
| 547 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9624 - loss: 0.1565\n",
|
| 548 |
+
"Epoch 136/200\n",
|
| 549 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9364 - loss: 0.1980\n",
|
| 550 |
+
"Epoch 137/200\n",
|
| 551 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9291 - loss: 0.2010\n",
|
| 552 |
+
"Epoch 138/200\n",
|
| 553 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8982 - loss: 0.2714\n",
|
| 554 |
+
"Epoch 139/200\n",
|
| 555 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9046 - loss: 0.2715\n",
|
| 556 |
+
"Epoch 140/200\n",
|
| 557 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9090 - loss: 0.2241\n",
|
| 558 |
+
"Epoch 141/200\n",
|
| 559 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9479 - loss: 0.2362\n",
|
| 560 |
+
"Epoch 142/200\n",
|
| 561 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9199 - loss: 0.2667\n",
|
| 562 |
+
"Epoch 143/200\n",
|
| 563 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9357 - loss: 0.2553\n",
|
| 564 |
+
"Epoch 144/200\n",
|
| 565 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9743 - loss: 0.0919\n",
|
| 566 |
+
"Epoch 145/200\n",
|
| 567 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9209 - loss: 0.2244\n",
|
| 568 |
+
"Epoch 146/200\n",
|
| 569 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9292 - loss: 0.2036 \n",
|
| 570 |
+
"Epoch 147/200\n",
|
| 571 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9155 - loss: 0.2580\n",
|
| 572 |
+
"Epoch 148/200\n",
|
| 573 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9175 - loss: 0.3135 \n",
|
| 574 |
+
"Epoch 149/200\n",
|
| 575 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9232 - loss: 0.2287\n",
|
| 576 |
+
"Epoch 150/200\n",
|
| 577 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9054 - loss: 0.3006\n",
|
| 578 |
+
"Epoch 151/200\n",
|
| 579 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8932 - loss: 0.3317\n",
|
| 580 |
+
"Epoch 152/200\n",
|
| 581 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8935 - loss: 0.2983\n",
|
| 582 |
+
"Epoch 153/200\n",
|
| 583 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9113 - loss: 0.2758\n",
|
| 584 |
+
"Epoch 154/200\n",
|
| 585 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9151 - loss: 0.2177\n",
|
| 586 |
+
"Epoch 155/200\n",
|
| 587 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9614 - loss: 0.1674\n",
|
| 588 |
+
"Epoch 156/200\n",
|
| 589 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8989 - loss: 0.2747\n",
|
| 590 |
+
"Epoch 157/200\n",
|
| 591 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8944 - loss: 0.2961\n",
|
| 592 |
+
"Epoch 158/200\n",
|
| 593 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9024 - loss: 0.3327\n",
|
| 594 |
+
"Epoch 159/200\n",
|
| 595 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9522 - loss: 0.2010 \n",
|
| 596 |
+
"Epoch 160/200\n",
|
| 597 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9321 - loss: 0.2548\n",
|
| 598 |
+
"Epoch 161/200\n",
|
| 599 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9378 - loss: 0.1737\n",
|
| 600 |
+
"Epoch 162/200\n",
|
| 601 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8975 - loss: 0.3410 \n",
|
| 602 |
+
"Epoch 163/200\n",
|
| 603 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8985 - loss: 0.3531\n",
|
| 604 |
+
"Epoch 164/200\n",
|
| 605 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9079 - loss: 0.2994\n",
|
| 606 |
+
"Epoch 165/200\n",
|
| 607 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9407 - loss: 0.1578\n",
|
| 608 |
+
"Epoch 166/200\n",
|
| 609 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8804 - loss: 0.3309 \n",
|
| 610 |
+
"Epoch 167/200\n",
|
| 611 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9003 - loss: 0.3809\n",
|
| 612 |
+
"Epoch 168/200\n",
|
| 613 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9451 - loss: 0.1439\n",
|
| 614 |
+
"Epoch 169/200\n",
|
| 615 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9341 - loss: 0.2064\n",
|
| 616 |
+
"Epoch 170/200\n",
|
| 617 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9742 - loss: 0.1526\n",
|
| 618 |
+
"Epoch 171/200\n",
|
| 619 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9393 - loss: 0.2190\n",
|
| 620 |
+
"Epoch 172/200\n",
|
| 621 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9324 - loss: 0.1997\n",
|
| 622 |
+
"Epoch 173/200\n",
|
| 623 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9670 - loss: 0.1265\n",
|
| 624 |
+
"Epoch 174/200\n",
|
| 625 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9299 - loss: 0.2026\n",
|
| 626 |
+
"Epoch 175/200\n",
|
| 627 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8800 - loss: 0.3367\n",
|
| 628 |
+
"Epoch 176/200\n",
|
| 629 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9523 - loss: 0.2110\n",
|
| 630 |
+
"Epoch 177/200\n",
|
| 631 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9474 - loss: 0.1674\n",
|
| 632 |
+
"Epoch 178/200\n",
|
| 633 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9046 - loss: 0.2604\n",
|
| 634 |
+
"Epoch 179/200\n",
|
| 635 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9552 - loss: 0.1817\n",
|
| 636 |
+
"Epoch 180/200\n",
|
| 637 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9215 - loss: 0.2647\n",
|
| 638 |
+
"Epoch 181/200\n",
|
| 639 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9105 - loss: 0.2477\n",
|
| 640 |
+
"Epoch 182/200\n",
|
| 641 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8724 - loss: 0.3489\n",
|
| 642 |
+
"Epoch 183/200\n",
|
| 643 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8908 - loss: 0.2489 \n",
|
| 644 |
+
"Epoch 184/200\n",
|
| 645 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9519 - loss: 0.1850\n",
|
| 646 |
+
"Epoch 185/200\n",
|
| 647 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9474 - loss: 0.1374\n",
|
| 648 |
+
"Epoch 186/200\n",
|
| 649 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9530 - loss: 0.1816\n",
|
| 650 |
+
"Epoch 187/200\n",
|
| 651 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9142 - loss: 0.1876\n",
|
| 652 |
+
"Epoch 188/200\n",
|
| 653 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8823 - loss: 0.3123\n",
|
| 654 |
+
"Epoch 189/200\n",
|
| 655 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9666 - loss: 0.0901\n",
|
| 656 |
+
"Epoch 190/200\n",
|
| 657 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9418 - loss: 0.2208\n",
|
| 658 |
+
"Epoch 191/200\n",
|
| 659 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9489 - loss: 0.2202\n",
|
| 660 |
+
"Epoch 192/200\n",
|
| 661 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9665 - loss: 0.2219\n",
|
| 662 |
+
"Epoch 193/200\n",
|
| 663 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9314 - loss: 0.1959\n",
|
| 664 |
+
"Epoch 194/200\n",
|
| 665 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9229 - loss: 0.3213\n",
|
| 666 |
+
"Epoch 195/200\n",
|
| 667 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9696 - loss: 0.1268\n",
|
| 668 |
+
"Epoch 196/200\n",
|
| 669 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9439 - loss: 0.1486\n",
|
| 670 |
+
"Epoch 197/200\n",
|
| 671 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9745 - loss: 0.1643\n",
|
| 672 |
+
"Epoch 198/200\n",
|
| 673 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8840 - loss: 0.3133\n",
|
| 674 |
+
"Epoch 199/200\n",
|
| 675 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9689 - loss: 0.1050\n",
|
| 676 |
+
"Epoch 200/200\n",
|
| 677 |
+
"\u001b[1m33/33\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9060 - loss: 0.3092\n"
|
| 678 |
+
]
|
| 679 |
+
},
|
| 680 |
+
{
|
| 681 |
+
"output_type": "stream",
|
| 682 |
+
"name": "stderr",
|
| 683 |
+
"text": [
|
| 684 |
+
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
|
| 685 |
+
]
|
| 686 |
+
},
|
| 687 |
+
{
|
| 688 |
+
"output_type": "stream",
|
| 689 |
+
"name": "stdout",
|
| 690 |
+
"text": [
|
| 691 |
+
"Model created and saved successfully!\n"
|
| 692 |
+
]
|
| 693 |
+
}
|
| 694 |
+
]
|
| 695 |
+
},
|
| 696 |
+
{
|
| 697 |
+
"cell_type": "code",
|
| 698 |
+
"source": [
|
| 699 |
+
"import tensorflow as tf\n",
|
| 700 |
+
"model = tf.keras.models.load_model('chatbot_model.h5')\n",
|
| 701 |
+
"\n",
|
| 702 |
+
"def clean_up_sentence(sentence):\n",
|
| 703 |
+
" sentence_words = nltk.word_tokenize(sentence)\n",
|
| 704 |
+
" sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]\n",
|
| 705 |
+
" return sentence_words\n",
|
| 706 |
+
"\n",
|
| 707 |
+
"def bag_of_words(sentence, words):\n",
|
| 708 |
+
" sentence_words = clean_up_sentence(sentence)\n",
|
| 709 |
+
" bag = [0] * len(words)\n",
|
| 710 |
+
" for s in sentence_words:\n",
|
| 711 |
+
" for i, w in enumerate(words):\n",
|
| 712 |
+
" if w == s:\n",
|
| 713 |
+
" bag[i] = 1\n",
|
| 714 |
+
" return np.array(bag)\n",
|
| 715 |
+
"\n",
|
| 716 |
+
"def predict_class(sentence, model):\n",
|
| 717 |
+
" bow = bag_of_words(sentence, words)\n",
|
| 718 |
+
" res = model.predict(np.array([bow]))[0]\n",
|
| 719 |
+
" ERROR_THRESHOLD = 0.25\n",
|
| 720 |
+
" results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]\n",
|
| 721 |
+
"\n",
|
| 722 |
+
" results.sort(key=lambda x: x[1], reverse=True)\n",
|
| 723 |
+
" return_list = [{\"intent\": classes[r[0]], \"probability\": str(r[1])} for r in results]\n",
|
| 724 |
+
" return return_list\n",
|
| 725 |
+
"\n",
|
| 726 |
+
"def get_response(intents_list, intents_json):\n",
|
| 727 |
+
" tag = intents_list[0]['intent']\n",
|
| 728 |
+
" for i in intents_json['intents']:\n",
|
| 729 |
+
" if i['tag'] == tag:\n",
|
| 730 |
+
" return random.choice(i['responses'])"
|
| 731 |
+
],
|
| 732 |
+
"metadata": {
|
| 733 |
+
"colab": {
|
| 734 |
+
"base_uri": "https://localhost:8080/"
|
| 735 |
+
},
|
| 736 |
+
"id": "b46z2vzBSfam",
|
| 737 |
+
"outputId": "fa096e3d-afc2-49ea-a216-f65fc104bfa3"
|
| 738 |
+
},
|
| 739 |
+
"execution_count": 24,
|
| 740 |
+
"outputs": [
|
| 741 |
+
{
|
| 742 |
+
"output_type": "stream",
|
| 743 |
+
"name": "stderr",
|
| 744 |
+
"text": [
|
| 745 |
+
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
|
| 746 |
+
]
|
| 747 |
+
}
|
| 748 |
+
]
|
| 749 |
+
},
|
| 750 |
+
{
|
| 751 |
+
"cell_type": "code",
|
| 752 |
+
"source": [
|
| 753 |
+
"print(\"Bot is ready to chat! Type 'quit' to stop.\")\n",
|
| 754 |
+
"while True:\n",
|
| 755 |
+
" message = input(\"You: \")\n",
|
| 756 |
+
" if message.lower() == \"quit\":\n",
|
| 757 |
+
" break\n",
|
| 758 |
+
"\n",
|
| 759 |
+
" ints = predict_class(message, model)\n",
|
| 760 |
+
" if ints:\n",
|
| 761 |
+
" res = get_response(ints, data)\n",
|
| 762 |
+
" print(\"Bot:\", res)\n",
|
| 763 |
+
" else:\n",
|
| 764 |
+
" print(\"Bot: Sorry, I don't understand that.\")\\"
|
| 765 |
+
],
|
| 766 |
+
"metadata": {
|
| 767 |
+
"colab": {
|
| 768 |
+
"base_uri": "https://localhost:8080/"
|
| 769 |
+
},
|
| 770 |
+
"id": "UITTjJ04Sh9u",
|
| 771 |
+
"outputId": "590b6824-0ba2-4af6-e20d-4bc1edc4d6dc"
|
| 772 |
+
},
|
| 773 |
+
"execution_count": 25,
|
| 774 |
+
"outputs": [
|
| 775 |
+
{
|
| 776 |
+
"name": "stdout",
|
| 777 |
+
"output_type": "stream",
|
| 778 |
+
"text": [
|
| 779 |
+
"Bot is ready to chat! Type 'quit' to stop.\n",
|
| 780 |
+
"You: hello\n",
|
| 781 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
|
| 782 |
+
"Bot: Hey! What brings you here today?\n",
|
| 783 |
+
"You: give me recommend book\n",
|
| 784 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
|
| 785 |
+
"Bot: Sure, I'd be happy to recommend a book. What type of book are you in the mood for?\n",
|
| 786 |
+
"You: fiction\n",
|
| 787 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
|
| 788 |
+
"Bot: {'Book': \"The Yiddish Policemen's Union\", 'Feedback': 'For sixty years, Jewish refugees and their descendants have prospered in the Federal District of Sitka, a \"temporary\" safe haven created in the wake of revelations of the Holocaust and the shocking 1948 collapse of the fledgling state of Israel. Proud, grateful, and longing to be American, the Jews of the Sitka District have created their own little world in the Alaskan panhandle, a vibrant, gritty, soulful, and complex frontier city that moves to the music of Yiddish. For sixty years they have been left alone, neglected and half-forgotten in a backwater of history. Now the District is set to revert to Alaskan control, and their dream is coming to an end: once again the tides of history threaten to sweep them up and carry them off into the unknown. But homicide detective Meyer Landsman of the District Police has enough problems without worrying about the upcoming Reversion. His life is a shambles, his marriage a wreck, his career a disaster. He and his half-Tlingit partner, Berko Shemets, can\\'t catch a break in any of their outstanding cases. Landsman\\'s new supervisor is the love of his lifeβand also his worst nightmare. And in the cheap hotel where he has washed up, someone has just committed a murderβright under Landsman\\'s nose. Out of habit, obligation, and a mysterious sense that it somehow offers him a shot at redeeming himself, Landsman begins to investigate the killing of his neighbor, a former chess prodigy. But when word comes down from on high that the case is to be dropped immediately, Landsman soon finds himself contending with all the powerful forces of faith, obsession, hopefulness, evil, and salvation that are his heritageβand with the unfinished business of his marriage to Bina Gelbfish, the one person who understands his darkest fears. At once a gripping whodunit, a love story, an homage to 1940s noir, and an exploration of the mysteries of exile and redemption, The Yiddish Policemen\\'s Union is a novel only Michael Chabon could have written.', 'Rate': 3.7}\n",
|
| 789 |
+
"You: give fact book\n",
|
| 790 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
|
| 791 |
+
"Bot: Hey! What brings you here today?\n",
|
| 792 |
+
"You: comic\n",
|
| 793 |
+
"\u001b[1m1/1\u001b[0m \u001b[32mββββββββββββββββββββ\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
|
| 794 |
+
"Bot: [{'Book': 'How to Make Money Like a Porn Star', 'Feedback': \"Claudia Corvette. From her tousled bedroom hair to her nameβall the porn stars in this world take their names from supermodels and sports carsβshe is adult entertainment's prototypical femme fatale. Her life is the collision of countless troubledβchildhood cliches and grownβup wet dreams, projected onto her as surely as her videos project their blue light onto lonely men around the world. From its first panel, How to Make Money Like a Porn Star draws the reader into the dark world of girls like Claudia, the men who fantasize about them, and the monsters who control them. In the hands of Rolling Stone writer Neil Strauss and illustrator Bernard Chang, this adult graphic novel weaves together black humor and blacker reality. Like all great American stories, it features humble beginnings, lifeβchanging tragedy, stripping, abuse, implants, fame, addiction, bigger implants, abduction, gunplay, downfall, and even bigger implants. Not to mention a thousand shades of latex and L'Oreal. Part parody, part morality tale, here is the truth about the porn life, its outsized visual splendor captured in a comic parade of doeβeyed centerfolds, its essence distilled in a story that will haunt every reader who has ever wondered where his next fantasy is coming from.\", 'Rate': 3.31}, {'Book': 'Inferno', 'Feedback': 'As a mad arsonist known as an Enfer scheme terrorizes the inhabitants of Gotham City, a fire at Arkham Asylum is engineered to provide an escape opportunity for its most infamous inmate, the Joker, who comes up with a scheme to launch the ultimate crime wave, disguised as the Caped Crusader himself. Original.', 'Rate': 3.6}, {'Book': 'Black Hole', 'Feedback': 'Seattle teenagers of the 1970s are suddenly faced with a devastating, disfiguring, and incurable plague that spreads only through sexual contact.', 'Rate': 3.83}, {'Book': 'Good-bye, Chunky Rice', 'Feedback': 'Chunky Rice, a small green turtle, embarks on an ocean voyage, where he meets a shady skipper and conjoined twins, Ruth and Livonia, but he also leaves behind his mouse deer girlfriend Dandel, who sends him letters in a bottle. Reprint. 20,000 first printing.', 'Rate': 3.8}, {'Book': \"Will Eisner's New York\", 'Feedback': 'A quartet of graphic works explores the lives and landscapes of the diverse inhabitants of the urban jungle of the Big Apple.', 'Rate': 4.36}, {'Book': 'Amphigorey', 'Feedback': 'Fifteen works by the American artist and author provide a journey into a macabre world', 'Rate': 4.05}, {'Book': 'Calvin and Hobbes: Sunday Pages 1985-1995', 'Feedback': 'Compiles a selection of Sunday cartoons selected and commented upon by the author.', 'Rate': 4.71}, {'Book': 'The Complete Calvin and Hobbes', 'Feedback': 'Brings together every \"Calvin and Hobbes\" cartoon that has ever appeared in syndication, along with stories and poems from classic collections.', 'Rate': 4.82}, {'Book': 'The Best of Ray Bradbury', 'Feedback': 'Collects top adaptations of the popular science-fiction author\\'s works, in a fan\\'s compendium that includes \"Come Into My Cellar,\" \"The Golden Apples of the Sun,\" and \"A Sound of Thunder.\" Original.', 'Rate': 4.07}, {'Book': 'The Marvel Comics Encyclopedia', 'Feedback': \"Introduces all of Marvel's greatest heroes and villains, with full details about their powers and careers.\", 'Rate': 4.36}]\n",
|
| 795 |
+
"You: quit\n"
|
| 796 |
+
]
|
| 797 |
+
}
|
| 798 |
+
]
|
| 799 |
+
}
|
| 800 |
+
]
|
| 801 |
+
}
|
intents.json
ADDED
|
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|
|