Kulo_Chatbot_Artificial_Neural_Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "acf27ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Dense\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "#nltk.download(\"punkt\")\n",
    "#nltk.download('wordnet')\n",
    "#nltk.download('omw-1.4')\n",
    "import json\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72e457c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The \"intents\" file could also be written directly in the notebook.\n",
    "with open(file = \"Intents.json\") as data:\n",
    "    data = data.read()\n",
    "\n",
    "intents = json.loads(s = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1cf133df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A list in which all the tokens of each pattern will be stored. Will be referred to as the vocabulary.\n",
    "words = []\n",
    "\n",
    "#A list in which all the tags will be stored.\n",
    "classes = []\n",
    "\n",
    "#A list in which all the patterns and their related tag will be stored as pairs.\n",
    "documents = []\n",
    "\n",
    "#The tokenization process will be performed on each pattern.\n",
    "for intent in intents[\"intents\"]:\n",
    "    for pattern in intent[\"patterns\"]:\n",
    "        \n",
    "        #Will perform the tokenization on each pattern.\n",
    "        tokens = nltk.word_tokenize(text = pattern)\n",
    "        words.extend(tokens)\n",
    "        documents.append((tokens, intent[\"tag\"]))\n",
    "        \n",
    "    #Will append each tag to the “classes” list if it is not there already.\n",
    "    if intent[\"tag\"] not in classes: classes.append(intent[\"tag\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1c7cae34",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "#List comprehension is being used to update the \"words\" list with the lemmatized lowercased version of each word that is not\n",
    "#a punctuation.\n",
    "words = [lemmatizer.lemmatize(word = word.lower()) for word in words if word not in string.punctuation]\n",
    "words = sorted(set(words))\n",
    "classes = sorted(set(classes))\n",
    "\n",
    "#Both lists could also be saved as txt instead of json.\n",
    "json.dump(obj = words, fp = open(file = \"Words.json\", mode = \"w\"))\n",
    "json.dump(obj = classes, fp = open(file = \"Classes.json\", mode = \"w\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2cb2abe6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "training = []\n",
    "output = np.zeros(shape = len(classes), dtype = \"int\")\n",
    "\n",
    "#Creation of the Bag of Words model.\n",
    "for document in documents:\n",
    "    #A list for the encoded version of each pattern.\n",
    "    bag_of_words = []\n",
    "    \n",
    "    #Will extract each pattern's list of tokens.\n",
    "    pattern_words = document[0]\n",
    "    \n",
    "    #List comprehension is being used to update the \"pattern_words\" list with the lemmatized lowercased version of each word.\n",
    "    #We have to lemmatize the pattern words, because the words in the vocabulary (The “words” list) are also lemmatized, \n",
    "    #and therefore we can compare oranges to oranges.\n",
    "    pattern_words = [lemmatizer.lemmatize(word = word.lower()) for word in pattern_words]\n",
    "    \n",
    "    #The encoding will be performed upon each pattern.\n",
    "    for word in words:\n",
    "        bag_of_words.append(1) if word in pattern_words else bag_of_words.append(0)\n",
    "        \n",
    "    #For every current considered pattern, the index of the tag the pattern belongs to will be marked with 1. \n",
    "    #This is for the neural network to learn later what kind of patterns belong to what kind of tag.\n",
    "    y = list(output)\n",
    "    y[classes.index(document[1])] = 1\n",
    "    \n",
    "    #Every encoded pattern-tag pair will be stored as training data for the neural network.\n",
    "    training.append([bag_of_words, y])\n",
    "    \n",
    "#Will shuffle the training data to avoid optional biases.\n",
    "random.shuffle(x = training)\n",
    "\n",
    "#Will convert the “training” list into a numpy array, so we can split the data to x and y later on.\n",
    "training = np.array(object = training, dtype = \"object\")\n",
    "\n",
    "#We have to perform the conversion into numpy array, so we can feed it into the neural network later on.\n",
    "X_train = np.array(object = list(training[:, 0]))\n",
    "y_train = np.array(object = list(training[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fca392f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = Sequential()\n",
    "\n",
    "#The \"input_shape\" parameter accepts a tuple form values, so there is a need to \"convert\" the length size into a tuple.\n",
    "ann.add(layer = Dense(units = 128, activation = \"relu\", input_shape = (len(X_train[0]),)))\n",
    "ann.add(layer = Dropout(rate = 0.5))\n",
    "\n",
    "ann.add(layer = Dense(units = 64, activation = \"relu\"))\n",
    "ann.add(layer = Dropout(rate = 0.3))\n",
    "\n",
    "ann.add(layer = Dense(units = len(y_train[0]), activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3476a725",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = SGD(learning_rate = 0.01, momentum = 0.9, nesterov = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96d2358",
   "metadata": {},
   "source": [
    "The cross entropy cost (or loss) function has been chosen, because we are predicting the likelihood of an example belonging to one of more than two classes, and the choice of the cost (or loss) function is directly related to the activation function we chose in the output layer of our neural network, which is the softmax activation function\n",
    "\n",
    "The labels expected to be provided to the neural network as OneHot and not integers, and hence the categorical_crossentropy has been selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "84536ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.compile(optimizer = optimizer, loss = \"categorical_crossentropy\", metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7d6ef921",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 0s 8ms/step - loss: 0.2325 - accuracy: 0.9667\n",
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      "1/1 [==============================] - 0s 3ms/step - loss: 0.2956 - accuracy: 0.9333\n",
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      "1/1 [==============================] - 0s 2ms/step - loss: 0.2332 - accuracy: 0.9667\n",
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      "1/1 [==============================] - 0s 2ms/step - loss: 0.1512 - accuracy: 1.0000\n",
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      "1/1 [==============================] - 0s 3ms/step - loss: 0.2403 - accuracy: 1.0000\n",
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      "1/1 [==============================] - 0s 4ms/step - loss: 0.2017 - accuracy: 1.0000\n",
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      "1/1 [==============================] - 0s 3ms/step - loss: 0.2461 - accuracy: 1.0000\n",
      "Epoch 145/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1812 - accuracy: 1.0000\n",
      "Epoch 146/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2274 - accuracy: 0.9667\n",
      "Epoch 147/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2143 - accuracy: 0.9667\n",
      "Epoch 148/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2025 - accuracy: 1.0000\n",
      "Epoch 149/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2088 - accuracy: 0.9333\n",
      "Epoch 150/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2467 - accuracy: 0.9000\n",
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      "1/1 [==============================] - 0s 2ms/step - loss: 0.1901 - accuracy: 1.0000\n",
      "Epoch 152/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2901 - accuracy: 0.9333\n",
      "Epoch 153/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2213 - accuracy: 0.9333\n",
      "Epoch 154/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2392 - accuracy: 0.9333\n",
      "Epoch 155/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2335 - accuracy: 0.9667\n",
      "Epoch 156/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2179 - accuracy: 0.9667\n",
      "Epoch 157/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1662 - accuracy: 0.9333\n",
      "Epoch 158/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1874 - accuracy: 0.9667\n",
      "Epoch 159/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1637 - accuracy: 1.0000\n",
      "Epoch 160/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1502 - accuracy: 1.0000\n",
      "Epoch 161/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1882 - accuracy: 0.9333\n",
      "Epoch 162/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1427 - accuracy: 1.0000\n",
      "Epoch 163/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1449 - accuracy: 1.0000\n",
      "Epoch 164/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1381 - accuracy: 1.0000\n",
      "Epoch 165/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.2184 - accuracy: 0.9333\n",
      "Epoch 166/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1728 - accuracy: 0.9667\n",
      "Epoch 167/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1291 - accuracy: 0.9667\n",
      "Epoch 168/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1158 - accuracy: 1.0000\n",
      "Epoch 169/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.2113 - accuracy: 0.9667\n",
      "Epoch 170/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1520 - accuracy: 0.9667\n",
      "Epoch 171/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1047 - accuracy: 1.0000\n",
      "Epoch 172/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1148 - accuracy: 1.0000\n",
      "Epoch 173/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1294 - accuracy: 1.0000\n",
      "Epoch 174/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1385 - accuracy: 0.9667\n",
      "Epoch 175/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1420 - accuracy: 1.0000\n",
      "Epoch 176/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1711 - accuracy: 0.9333\n",
      "Epoch 177/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1374 - accuracy: 0.9667\n",
      "Epoch 178/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1093 - accuracy: 1.0000\n",
      "Epoch 179/200\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1970 - accuracy: 0.9667\n",
      "Epoch 180/200\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1699 - accuracy: 1.0000\n",
      "Epoch 181/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1881 - accuracy: 0.9667\n",
      "Epoch 182/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1597 - accuracy: 0.9667\n",
      "Epoch 183/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1394 - accuracy: 1.0000\n",
      "Epoch 184/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1497 - accuracy: 0.9667\n",
      "Epoch 185/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1399 - accuracy: 1.0000\n",
      "Epoch 186/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0823 - accuracy: 1.0000\n",
      "Epoch 187/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0880 - accuracy: 1.0000\n",
      "Epoch 188/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1526 - accuracy: 1.0000\n",
      "Epoch 189/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0752 - accuracy: 1.0000\n",
      "Epoch 190/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1781 - accuracy: 0.9667\n",
      "Epoch 191/200\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.1118 - accuracy: 1.0000\n",
      "Epoch 192/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0988 - accuracy: 1.0000\n",
      "Epoch 193/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0757 - accuracy: 1.0000\n",
      "Epoch 194/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1248 - accuracy: 0.9667\n",
      "Epoch 195/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0777 - accuracy: 1.0000\n",
      "Epoch 196/200\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0605 - accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1213 - accuracy: 1.0000\n",
      "Epoch 198/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.1040 - accuracy: 1.0000\n",
      "Epoch 199/200\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0630 - accuracy: 1.0000\n",
      "Epoch 200/200\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0867 - accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "training = ann.fit(x = X_train, y = y_train, batch_size = 30, epochs = 200, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2d5bd21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.save(filepath = \"final_model.h5\")"
   ]
  }
 ],
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