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data.json

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+{
+  "intents": [
+    {
+      "tag": "greeting",
+      "patterns": [
+        "Hi there",
+        "How are you",
+        "Is anyone there?",
+        "Hey",
+        "Hola",
+        "Hello",
+        "Good day"
+      ],
+      "responses": [
+        "Hello, thanks for asking",
+        "Good to see you again",
+        "Hi there, how can I help?"
+      ],
+      "context": [""]
+    },
+    {
+      "tag": "goodbye",
+      "patterns": [
+        "Bye",
+        "See you later",
+        "Goodbye",
+        "Nice chatting to you, bye",
+        "Till next time"
+      ],
+      "responses": [
+        "See you!",
+        "Have a nice day",
+        "Bye! Come back again soon."
+      ],
+      "context": [""]
+    },
+    {
+      "tag": "thanks",
+      "patterns": [
+        "Thanks",
+        "Thank you",
+        "That's helpful",
+        "Awesome, thanks",
+        "Thanks for helping me"
+      ],
+      "responses": ["Happy to help!", "Any time!", "My pleasure"],
+      "context": [""]
+    },
+    {
+      "tag": "noanswer",
+      "patterns": [],
+      "responses": [
+        "Sorry, can't understand you",
+        "Please give me more info",
+        "Not sure I understand"
+      ],
+      "context": [""]
+    },
+    {
+      "tag": "options",
+      "patterns": [
+        "How you could help me?",
+        "What you can do?",
+        "What help you provide?",
+        "How you can be helpful?",
+        "What support is offered"
+      ],
+      "responses": [
+        "I can guide you through Adverse drug reaction list, Blood pressure tracking, Hospitals and Pharmacies",
+        "Offering support for Adverse drug reaction, Blood pressure, Hospitals and Pharmacies"
+      ],
+      "context": [""]
+    },
+    {
+      "tag": "adverse_drug",
+      "patterns": [
+        "How to check Adverse drug reaction?",
+        "Open adverse drugs module",
+        "Give me a list of drugs causing adverse behavior",
+        "List all drugs suitable for patient with adverse reaction",
+        "Which drugs dont have adverse reaction?"
+      ],
+      "responses": ["Navigating to Adverse drug reaction module"],
+      "context": [""]
+    },
+    {
+      "tag": "blood_pressure",
+      "patterns": [
+        "Open blood pressure module",
+        "Task related to blood pressure",
+        "Blood pressure data entry",
+        "I want to log blood pressure results",
+        "Blood pressure data management"
+      ],
+      "responses": ["Navigating to Blood Pressure module"],
+      "context": [""]
+    },
+    {
+      "tag": "blood_pressure_search",
+      "patterns": [
+        "I want to search for blood pressure result history",
+        "Blood pressure for patient",
+        "Load patient blood pressure result",
+        "Show blood pressure results for patient",
+        "Find blood pressure results by ID"
+      ],
+      "responses": ["Please provide Patient ID", "Patient ID?"],
+      "context": ["search_blood_pressure_by_patient_id"]
+    },
+    {
+      "tag": "search_blood_pressure_by_patient_id",
+      "patterns": [],
+      "responses": ["Loading Blood pressure result for Patient"],
+      "context": [""]
+    },
+    {
+      "tag": "pharmacy_search",
+      "patterns": [
+        "Find me a pharmacy",
+        "Find pharmacy",
+        "List of pharmacies nearby",
+        "Locate pharmacy",
+        "Search pharmacy"
+      ],
+      "responses": ["Please provide pharmacy name"],
+      "context": ["search_pharmacy_by_name"]
+    },
+    {
+      "tag": "search_pharmacy_by_name",
+      "patterns": [],
+      "responses": ["Loading pharmacy details"],
+      "context": [""]
+    },
+    {
+      "tag": "hospital_search",
+      "patterns": [
+        "Lookup for hospital",
+        "Searching for hospital to transfer patient",
+        "I want to search hospital data",
+        "Hospital lookup for patient",
+        "Looking up hospital details"
+      ],
+      "responses": ["Please provide hospital name or location"],
+      "context": ["search_hospital_by_params"]
+    },
+    {
+      "tag": "search_hospital_by_params",
+      "patterns": [],
+      "responses": ["Please provide hospital type"],
+      "context": ["search_hospital_by_type"]
+    },
+    {
+      "tag": "search_hospital_by_type",
+      "patterns": [],
+      "responses": ["Loading hospital details"],
+      "context": [""]
+    }
+  ]
+}

labels.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d26d7e2c44fbb50d869f9d5c79e647353af3b0c10a82736a930032deb8cbeb0
+size 176

main.py

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+from flask import Flask, render_template, request
+import random
+import json
+from keras.models import load_model
+import numpy as np
+import pickle
+from nltk.stem import WordNetLemmatizer
+import nltk
+nltk.download('popular')
+lemmatizer = WordNetLemmatizer()
+
+model = load_model('model.h5')
+intents = json.loads(open('data.json').read())
+words = pickle.load(open('texts.pkl', 'rb'))
+classes = pickle.load(open('labels.pkl', 'rb'))
+
+
+def clean_up_sentence(sentence):
+    # tokenize the pattern - split words into array
+    sentence_words = nltk.word_tokenize(sentence)
+    # stem each word - create short form for word
+    sentence_words = [lemmatizer.lemmatize(
+        word.lower()) for word in sentence_words]
+    return sentence_words
+
+# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
+
+
+def bow(sentence, words, show_details=True):
+    # tokenize the pattern
+    sentence_words = clean_up_sentence(sentence)
+    # bag of words - matrix of N words, vocabulary matrix
+    bag = [0]*len(words)
+    for s in sentence_words:
+        for i, w in enumerate(words):
+            if w == s:
+                # assign 1 if current word is in the vocabulary position
+                bag[i] = 1
+                if show_details:
+                    print("found in bag: %s" % w)
+    return (np.array(bag))
+
+
+def predict_class(sentence, model):
+    # filter out predictions below a threshold
+    p = bow(sentence, words, show_details=False)
+    res = model.predict(np.array([p]))[0]
+    ERROR_THRESHOLD = 0.25
+    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
+    # sort by strength of probability
+    results.sort(key=lambda x: x[1], reverse=True)
+    return_list = []
+    for r in results:
+        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
+    return return_list
+
+
+def getResponse(ints, intents_json):
+    tag = ints[0]['intent']
+    list_of_intents = intents_json['intents']
+    for i in list_of_intents:
+        if (i['tag'] == tag):
+            result = random.choice(i['responses'])
+            break
+    return result
+
+
+def chatbot_response(msg):
+    ints = predict_class(msg, model)
+    res = getResponse(ints, intents)
+    return res
+
+
+app = Flask(__name__)
+app.static_folder = 'static'
+
+
+@app.route("/")
+def home():
+    return render_template("index.html")
+
+
+@app.route("/get")
+def get_bot_response():
+    userText = request.args.get('msg')
+    return chatbot_response(userText)
+
+
+# if __name__ == "__main__":
+#     app.run()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:37e6e59d3a8573d4a76cd77d3eeb0037b93cb618d57bcf52c1dd22bf154d4d90
+size 192816

requirements.txt

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+gunicorn
+flask
+tensorflow 
+keras 
+pickle
+nltk

static/styles/style.css

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+:root {
+  --body-bg: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+  --msger-bg: #fff;
+  --border: 2px solid #ddd;
+  --left-msg-bg: #ececec;
+  --right-msg-bg: #579ffb;
+}
+
+html {
+  box-sizing: border-box;
+}
+
+*,
+*:before,
+*:after {
+  margin: 0;
+  padding: 0;
+  box-sizing: inherit;
+}
+
+body {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  height: 100vh;
+  background-image: var(--body-bg);
+  font-family: Helvetica, sans-serif;
+}
+
+.msger {
+  display: flex;
+  flex-flow: column wrap;
+  justify-content: space-between;
+  width: 100%;
+  max-width: 867px;
+  margin: 25px 10px;
+  height: calc(100% - 50px);
+  border: var(--border);
+  border-radius: 5px;
+  background: var(--msger-bg);
+  box-shadow: 0 15px 15px -5px rgba(0, 0, 0, 0.2);
+}
+
+.msger-header {
+  /* display: flex; */
+  font-size: medium;
+  justify-content: space-between;
+  padding: 10px;
+  text-align: center;
+  border-bottom: var(--border);
+  background: #eee;
+  color: #666;
+}
+
+.msger-chat {
+  flex: 1;
+  overflow-y: auto;
+  padding: 10px;
+}
+.msger-chat::-webkit-scrollbar {
+  width: 6px;
+}
+.msger-chat::-webkit-scrollbar-track {
+  background: #ddd;
+}
+.msger-chat::-webkit-scrollbar-thumb {
+  background: #bdbdbd;
+}
+.msg {
+  display: flex;
+  align-items: flex-end;
+  margin-bottom: 10px;
+}
+
+.msg-img {
+  width: 50px;
+  height: 50px;
+  margin-right: 10px;
+  background: #ddd;
+  background-repeat: no-repeat;
+  background-position: center;
+  background-size: cover;
+  border-radius: 50%;
+}
+.msg-bubble {
+  max-width: 450px;
+  padding: 15px;
+  border-radius: 15px;
+  background: var(--left-msg-bg);
+}
+.msg-info {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 10px;
+}
+.msg-info-name {
+  margin-right: 10px;
+  font-weight: bold;
+}
+.msg-info-time {
+  font-size: 0.85em;
+}
+
+.left-msg .msg-bubble {
+  border-bottom-left-radius: 0;
+}
+
+.right-msg {
+  flex-direction: row-reverse;
+}
+.right-msg .msg-bubble {
+  background: var(--right-msg-bg);
+  color: #fff;
+  border-bottom-right-radius: 0;
+}
+.right-msg .msg-img {
+  margin: 0 0 0 10px;
+}
+
+.msger-inputarea {
+  display: flex;
+  padding: 10px;
+  border-top: var(--border);
+  background: #eee;
+}
+.msger-inputarea * {
+  padding: 10px;
+  border: none;
+  border-radius: 3px;
+  font-size: 1em;
+}
+.msger-input {
+  flex: 1;
+  background: #ddd;
+}
+.msger-send-btn {
+  margin-left: 10px;
+  background: rgb(0, 196, 65);
+  color: #fff;
+  font-weight: bold;
+  cursor: pointer;
+  transition: background 0.23s;
+}
+.msger-send-btn:hover {
+  background: rgb(0, 180, 50);
+}
+
+.msger-chat {
+  background-color: #fcfcfe;
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+}

templates/index.html

@@ -0,0 +1,126 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <title>Chatbot</title>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta http-equiv="X-UA-Compatible" content="ie=edge">
+    <link rel="stylesheet" href="{{ url_for('static', filename='styles/style.css') }}">
+    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
+</head>
+
+<body>
+    <!-- partial:index.partial.html -->
+    <section class="msger">
+        <header class="msger-header">
+            <div class="msger-header-title">
+                <i class="fas fa-bug"></i> Chatbot <i class="fas fa-bug"></i>
+            </div>
+        </header>
+
+        <main class="msger-chat">
+            <div class="msg left-msg">
+                <div class="msg-img" style="background-image: url(https://image.flaticon.com/icons/svg/327/327779.svg)">
+                </div>
+
+                <div class="msg-bubble">
+                    <div class="msg-info">
+                        <div class="msg-info-name">Chatbot</div>
+                        <div class="msg-info-time">12:45</div>
+                    </div>
+
+                    <div class="msg-text">
+                        Hi, welcome to ChatBot! Go ahead and send me a message. 😄
+                    </div>
+                </div>
+            </div>
+
+        </main>
+
+        <form class="msger-inputarea">
+            <input type="text" class="msger-input" id="textInput" placeholder="Enter your message...">
+            <button type="submit" class="msger-send-btn">Send</button>
+        </form>
+    </section>
+    <!-- partial -->
+    <script src='https://use.fontawesome.com/releases/v5.0.13/js/all.js'></script>
+    <script>
+
+        const msgerForm = get(".msger-inputarea");
+        const msgerInput = get(".msger-input");
+        const msgerChat = get(".msger-chat");
+
+
+        // Icons made by Freepik from www.flaticon.com
+        const BOT_IMG = "https://image.flaticon.com/icons/svg/327/327779.svg";
+        const PERSON_IMG = "https://image.flaticon.com/icons/svg/145/145867.svg";
+        const BOT_NAME = "    ChatBot";
+        const PERSON_NAME = "You";
+
+        msgerForm.addEventListener("submit", event => {
+            event.preventDefault();
+
+            const msgText = msgerInput.value;
+            if (!msgText) return;
+
+            appendMessage(PERSON_NAME, PERSON_IMG, "right", msgText);
+            msgerInput.value = "";
+            botResponse(msgText);
+        });
+
+        function appendMessage(name, img, side, text) {
+            //   Simple solution for small apps
+            const msgHTML = `
+<div class="msg ${side}-msg">
+  <div class="msg-img" style="background-image: url(${img})"></div>
+
+  <div class="msg-bubble">
+    <div class="msg-info">
+      <div class="msg-info-name">${name}</div>
+      <div class="msg-info-time">${formatDate(new Date())}</div>
+    </div>
+
+    <div class="msg-text">${text}</div>
+  </div>
+</div>
+`;
+
+            msgerChat.insertAdjacentHTML("beforeend", msgHTML);
+            msgerChat.scrollTop += 500;
+        }
+
+        function botResponse(rawText) {
+
+            // Bot Response
+            $.get("/get", { msg: rawText }).done(function (data) {
+                console.log(rawText);
+                console.log(data);
+                const msgText = data;
+                appendMessage(BOT_NAME, BOT_IMG, "left", msgText);
+
+            });
+
+        }
+
+
+        // Utils
+        function get(selector, root = document) {
+            return root.querySelector(selector);
+        }
+
+        function formatDate(date) {
+            const h = "0" + date.getHours();
+            const m = "0" + date.getMinutes();
+
+            return `${h.slice(-2)}:${m.slice(-2)}`;
+        }
+
+
+
+    </script>
+
+</body>
+
+</html>

texts.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:672df62425bc41198ca7671f9af837184b3670273e684de2aa188c1817a9045d
+size 1043

training.ipynb

@@ -0,0 +1,755 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "wUWIn56C2EVy"
+      },
+      "outputs": [],
+      "source": [
+        "import nltk\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "lemmatizer = WordNetLemmatizer()\n",
+        "import json\n",
+        "import pickle\n",
+        "import numpy as np\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense, Activation, Dropout\n",
+        "# from keras.optimizers import SGD\n",
+        "from tensorflow.keras.optimizers import SGD\n",
+        "import random"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hBb-ddKr2zlg",
+        "outputId": "d216a15f-5142-4cad-a214-cc911a214394"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package punkt to C:\\Users\\Makara\n",
+            "[nltk_data]     PC\\AppData\\Roaming\\nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "nltk.download('punkt')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WJNKSOig29LD",
+        "outputId": "4a6505c1-4080-4097-d661-95275788348f"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package wordnet to C:\\Users\\Makara\n",
+            "[nltk_data]     PC\\AppData\\Roaming\\nltk_data...\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "nltk.download('wordnet')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package omw-1.4 to C:\\Users\\Makara\n",
+            "[nltk_data]     PC\\AppData\\Roaming\\nltk_data...\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "nltk.download('omw-1.4')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "CcRMqaqy2aXK"
+      },
+      "outputs": [],
+      "source": [
+        "words=[]\n",
+        "classes = []\n",
+        "documents = []\n",
+        "ignore_words = ['?', '!']\n",
+        "data_file = open('data.json').read()\n",
+        "intents = json.loads(data_file)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "85GcOWiP2iWf"
+      },
+      "outputs": [],
+      "source": [
+        "for intent in intents['intents']:\n",
+        "    for pattern in intent['patterns']:\n",
+        "        #tokenize each word\n",
+        "        w = nltk.word_tokenize(pattern)\n",
+        "        words.extend(w)\n",
+        "        #add documents in the corpus\n",
+        "        documents.append((w, intent['tag']))\n",
+        "        # add to our classes list\n",
+        "        if intent['tag'] not in classes:\n",
+        "            classes.append(intent['tag'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "p1iYlVBm2i8v",
+        "outputId": "a0696f92-8558-484d-fab1-8287685658cc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "47 documents\n",
+            "9 classes ['adverse_drug', 'blood_pressure', 'blood_pressure_search', 'goodbye', 'greeting', 'hospital_search', 'options', 'pharmacy_search', 'thanks']\n",
+            "88 unique lemmatized words [\"'s\", ',', 'a', 'adverse', 'all', 'anyone', 'are', 'awesome', 'be', 'behavior', 'blood', 'by', 'bye', 'can', 'causing', 'chatting', 'check', 'could', 'data', 'day', 'detail', 'do', 'dont', 'drug', 'entry', 'find', 'for', 'give', 'good', 'goodbye', 'have', 'hello', 'help', 'helpful', 'helping', 'hey', 'hi', 'history', 'hola', 'hospital', 'how', 'i', 'id', 'is', 'later', 'list', 'load', 'locate', 'log', 'looking', 'lookup', 'management', 'me', 'module', 'nearby', 'next', 'nice', 'of', 'offered', 'open', 'patient', 'pharmacy', 'pressure', 'provide', 'reaction', 'related', 'result', 'search', 'searching', 'see', 'show', 'suitable', 'support', 'task', 'thank', 'thanks', 'that', 'there', 'till', 'time', 'to', 'transfer', 'up', 'want', 'what', 'which', 'with', 'you']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# lemmaztize and lower each word and remove duplicates\n",
+        "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
+        "words = sorted(list(set(words)))\n",
+        "# sort classes\n",
+        "classes = sorted(list(set(classes)))\n",
+        "# documents = combination between patterns and intents\n",
+        "print (len(documents), \"documents\")\n",
+        "# classes = intents\n",
+        "print (len(classes), \"classes\", classes)\n",
+        "# words = all words, vocabulary\n",
+        "print (len(words), \"unique lemmatized words\", words)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "H5EZ1wf325dH"
+      },
+      "outputs": [],
+      "source": [
+        "pickle.dump(words,open('texts.pkl','wb'))\n",
+        "pickle.dump(classes,open('labels.pkl','wb'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "oTj9egGz3CMZ"
+      },
+      "outputs": [],
+      "source": [
+        "# create our training data\n",
+        "training = []\n",
+        "# create an empty array for our output\n",
+        "output_empty = [0] * len(classes)\n",
+        "# training set, bag of words for each sentence\n",
+        "for doc in documents:\n",
+        "    # initialize our bag of words\n",
+        "    bag = []\n",
+        "    # list of tokenized words for the pattern\n",
+        "    pattern_words = doc[0]\n",
+        "    # lemmatize each word - create base word, in attempt to represent related words\n",
+        "    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]\n",
+        "    # create our bag of words array with 1, if word match found in current pattern\n",
+        "    for w in words:\n",
+        "        bag.append(1) if w in pattern_words else bag.append(0)\n",
+        "\n",
+        "    # output is a '0' for each tag and '1' for current tag (for each pattern)\n",
+        "    output_row = list(output_empty)\n",
+        "    output_row[classes.index(doc[1])] = 1\n",
+        "\n",
+        "    training.append([bag, output_row])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TWZKpn-43KaH",
+        "outputId": "c2b89f6a-d1e8-4e25-908f-5b8f1a5bb84a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training data created\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\Makara PC\\.conda\\envs\\chat-bot-app\\lib\\site-packages\\ipykernel_launcher.py:3: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
+            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+          ]
+        }
+      ],
+      "source": [
+        "# shuffle our features and turn into np.array\n",
+        "random.shuffle(training)\n",
+        "training = np.array(training)\n",
+        "# create train and test lists. X - patterns, Y - intents\n",
+        "train_x = list(training[:,0])\n",
+        "train_y = list(training[:,1])\n",
+        "print(\"Training data created\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "c4rbUrWB3MAX"
+      },
+      "outputs": [],
+      "source": [
+        "# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons\n",
+        "# equal to number of intents to predict output intent with softmax\n",
+        "model = Sequential()\n",
+        "model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(64, activation='relu'))\n",
+        "model.add(Dropout(0.5))\n",
+        "model.add(Dense(len(train_y[0]), activation='softmax'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fRmg-rBd3OnQ",
+        "outputId": "5369506c-da45-4dd5-8773-59f52875bc68"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model\n",
+        "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
+        "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DeD0fV0c3RBn",
+        "outputId": "392059e2-dfe7-46a0-b2c8-db2f3702a483"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/200\n",
+            "10/10 [==============================] - 1s 2ms/step - loss: 2.2413 - accuracy: 0.1064\n",
+            "Epoch 2/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 2.1823 - accuracy: 0.2340\n",
+            "Epoch 3/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 2.1345 - accuracy: 0.2128\n",
+            "Epoch 4/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 1.9794 - accuracy: 0.3191\n",
+            "Epoch 5/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 1.8818 - accuracy: 0.3191\n",
+            "Epoch 6/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 1.7872 - accuracy: 0.4043\n",
+            "Epoch 7/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 1.6584 - accuracy: 0.5106\n",
+            "Epoch 8/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 1.5289 - accuracy: 0.5319\n",
+            "Epoch 9/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 1.4448 - accuracy: 0.5957\n",
+            "Epoch 10/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 1.2668 - accuracy: 0.5957\n",
+            "Epoch 11/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 1.2086 - accuracy: 0.6809\n",
+            "Epoch 12/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.9905 - accuracy: 0.8085\n",
+            "Epoch 13/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 1.0099 - accuracy: 0.7872\n",
+            "Epoch 14/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.9804 - accuracy: 0.7234\n",
+            "Epoch 15/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.8112 - accuracy: 0.8298\n",
+            "Epoch 16/200\n",
+            "10/10 [==============================] - 0s 7ms/step - loss: 0.7849 - accuracy: 0.7447\n",
+            "Epoch 17/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.6714 - accuracy: 0.7872\n",
+            "Epoch 18/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.6601 - accuracy: 0.7872\n",
+            "Epoch 19/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.8936\n",
+            "Epoch 20/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.7604 - accuracy: 0.7447\n",
+            "Epoch 21/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.7019 - accuracy: 0.7872\n",
+            "Epoch 22/200\n",
+            "10/10 [==============================] - 0s 8ms/step - loss: 0.5007 - accuracy: 0.8936\n",
+            "Epoch 23/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.4494 - accuracy: 0.8723\n",
+            "Epoch 24/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.3297 - accuracy: 0.9362\n",
+            "Epoch 25/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.3112 - accuracy: 0.9362\n",
+            "Epoch 26/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.3624 - accuracy: 0.9362\n",
+            "Epoch 27/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.2498 - accuracy: 0.9362\n",
+            "Epoch 28/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.2607 - accuracy: 0.9362\n",
+            "Epoch 29/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.2573 - accuracy: 0.9362\n",
+            "Epoch 30/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1811 - accuracy: 0.9787\n",
+            "Epoch 31/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.3079 - accuracy: 0.8936\n",
+            "Epoch 32/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.2232 - accuracy: 0.9574\n",
+            "Epoch 33/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1904 - accuracy: 0.9787\n",
+            "Epoch 34/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.2117 - accuracy: 0.9149\n",
+            "Epoch 35/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1476 - accuracy: 0.9787\n",
+            "Epoch 36/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1317 - accuracy: 1.0000\n",
+            "Epoch 37/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0762 - accuracy: 1.0000\n",
+            "Epoch 38/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1502 - accuracy: 0.9149\n",
+            "Epoch 39/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1191 - accuracy: 0.9574\n",
+            "Epoch 40/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1490 - accuracy: 0.9787\n",
+            "Epoch 41/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.2177 - accuracy: 0.9574\n",
+            "Epoch 42/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.1596 - accuracy: 0.9574\n",
+            "Epoch 43/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1574 - accuracy: 0.9574\n",
+            "Epoch 44/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.2133 - accuracy: 0.9149\n",
+            "Epoch 45/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.1228 - accuracy: 0.9787\n",
+            "Epoch 46/200\n",
+            "10/10 [==============================] - 0s 7ms/step - loss: 0.1345 - accuracy: 0.9574\n",
+            "Epoch 47/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1022 - accuracy: 0.9787\n",
+            "Epoch 48/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1116 - accuracy: 0.9574\n",
+            "Epoch 49/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1366 - accuracy: 0.9362\n",
+            "Epoch 50/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1418 - accuracy: 0.9574\n",
+            "Epoch 51/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1272 - accuracy: 1.0000\n",
+            "Epoch 52/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0773 - accuracy: 1.0000\n",
+            "Epoch 53/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0499 - accuracy: 1.0000\n",
+            "Epoch 54/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1199 - accuracy: 0.9574\n",
+            "Epoch 55/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.1400 - accuracy: 1.0000\n",
+            "Epoch 56/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.0842 - accuracy: 0.9787\n",
+            "Epoch 57/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1037 - accuracy: 0.9787\n",
+            "Epoch 58/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.1494 - accuracy: 0.9362\n",
+            "Epoch 59/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0432 - accuracy: 1.0000\n",
+            "Epoch 60/200\n",
+            "10/10 [==============================] - 0s 6ms/step - loss: 0.0823 - accuracy: 0.9787\n",
+            "Epoch 61/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0534 - accuracy: 1.0000\n",
+            "Epoch 62/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0671 - accuracy: 1.0000\n",
+            "Epoch 63/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0628 - accuracy: 1.0000\n",
+            "Epoch 64/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0229 - accuracy: 1.0000\n",
+            "Epoch 65/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0617 - accuracy: 1.0000\n",
+            "Epoch 66/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0603 - accuracy: 0.9787\n",
+            "Epoch 67/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0239 - accuracy: 1.0000\n",
+            "Epoch 68/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1004 - accuracy: 0.9574\n",
+            "Epoch 69/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0582 - accuracy: 0.9787\n",
+            "Epoch 70/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1214 - accuracy: 0.9362\n",
+            "Epoch 71/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0270 - accuracy: 1.0000\n",
+            "Epoch 72/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0916 - accuracy: 0.9787\n",
+            "Epoch 73/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0413 - accuracy: 1.0000\n",
+            "Epoch 74/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0502 - accuracy: 1.0000\n",
+            "Epoch 75/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0618 - accuracy: 0.9787\n",
+            "Epoch 76/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0723 - accuracy: 1.0000\n",
+            "Epoch 77/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.1292 - accuracy: 0.9574\n",
+            "Epoch 78/200\n",
+            "10/10 [==============================] - ETA: 0s - loss: 0.0038 - accuracy: 1.00 - 0s 1ms/step - loss: 0.0298 - accuracy: 1.0000\n",
+            "Epoch 79/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0176 - accuracy: 1.0000\n",
+            "Epoch 80/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1068 - accuracy: 0.9574\n",
+            "Epoch 81/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0200 - accuracy: 1.0000\n",
+            "Epoch 82/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0183 - accuracy: 1.0000\n",
+            "Epoch 83/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0467 - accuracy: 1.0000\n",
+            "Epoch 84/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0539 - accuracy: 1.0000\n",
+            "Epoch 85/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.0998 - accuracy: 0.9574\n",
+            "Epoch 86/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.1305 - accuracy: 0.9574\n",
+            "Epoch 87/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0236 - accuracy: 1.0000\n",
+            "Epoch 88/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 1.0000\n",
+            "Epoch 89/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0752 - accuracy: 0.9787\n",
+            "Epoch 90/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0443 - accuracy: 1.0000\n",
+            "Epoch 91/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0955 - accuracy: 0.9787\n",
+            "Epoch 92/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0447 - accuracy: 1.0000\n",
+            "Epoch 93/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0775 - accuracy: 0.9787\n",
+            "Epoch 94/200\n",
+            "10/10 [==============================] - 0s 8ms/step - loss: 0.0479 - accuracy: 1.0000\n",
+            "Epoch 95/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0529 - accuracy: 1.0000\n",
+            "Epoch 96/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0087 - accuracy: 1.0000\n",
+            "Epoch 97/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0415 - accuracy: 1.0000\n",
+            "Epoch 98/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0348 - accuracy: 1.0000\n",
+            "Epoch 99/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0130 - accuracy: 1.0000\n",
+            "Epoch 100/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0250 - accuracy: 1.0000\n",
+            "Epoch 101/200\n",
+            "10/10 [==============================] - 0s 6ms/step - loss: 0.0513 - accuracy: 0.9787\n",
+            "Epoch 102/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0326 - accuracy: 0.9787\n",
+            "Epoch 103/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0516 - accuracy: 0.9787\n",
+            "Epoch 104/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0376 - accuracy: 1.0000\n",
+            "Epoch 105/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.0236 - accuracy: 1.0000\n",
+            "Epoch 106/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0146 - accuracy: 1.0000\n",
+            "Epoch 107/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0327 - accuracy: 1.0000\n",
+            "Epoch 108/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0333 - accuracy: 1.0000\n",
+            "Epoch 109/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000\n",
+            "Epoch 110/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0920 - accuracy: 0.9574\n",
+            "Epoch 111/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 1.0000\n",
+            "Epoch 112/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 1.0000\n",
+            "Epoch 113/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0248 - accuracy: 1.0000\n",
+            "Epoch 114/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0260 - accuracy: 1.0000\n",
+            "Epoch 115/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0162 - accuracy: 1.0000\n",
+            "Epoch 116/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0659 - accuracy: 0.9787\n",
+            "Epoch 117/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0618 - accuracy: 0.9787\n",
+            "Epoch 118/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0301 - accuracy: 1.0000\n",
+            "Epoch 119/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0334 - accuracy: 1.0000\n",
+            "Epoch 120/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0224 - accuracy: 1.0000\n",
+            "Epoch 121/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.1810 - accuracy: 0.9574\n",
+            "Epoch 122/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0677 - accuracy: 1.0000\n",
+            "Epoch 123/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0693 - accuracy: 0.9787\n",
+            "Epoch 124/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0523 - accuracy: 0.9787\n",
+            "Epoch 125/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0281 - accuracy: 1.0000\n",
+            "Epoch 126/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0209 - accuracy: 1.0000\n",
+            "Epoch 127/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0405 - accuracy: 0.9787\n",
+            "Epoch 128/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0093 - accuracy: 1.0000\n",
+            "Epoch 129/200\n",
+            "10/10 [==============================] - ETA: 0s - loss: 0.0834 - accuracy: 1.00 - 0s 2ms/step - loss: 0.0413 - accuracy: 1.0000\n",
+            "Epoch 130/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0122 - accuracy: 1.0000\n",
+            "Epoch 131/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0125 - accuracy: 1.0000\n",
+            "Epoch 132/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0099 - accuracy: 1.0000\n",
+            "Epoch 133/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0281 - accuracy: 1.0000\n",
+            "Epoch 134/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0179 - accuracy: 1.0000\n",
+            "Epoch 135/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000\n",
+            "Epoch 136/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0456 - accuracy: 1.0000\n",
+            "Epoch 137/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0493 - accuracy: 0.9787\n",
+            "Epoch 138/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0211 - accuracy: 1.0000\n",
+            "Epoch 139/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0098 - accuracy: 1.0000\n",
+            "Epoch 140/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0306 - accuracy: 1.0000\n",
+            "Epoch 141/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0076 - accuracy: 1.0000\n",
+            "Epoch 142/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0605 - accuracy: 0.9787\n",
+            "Epoch 143/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0273 - accuracy: 1.0000\n",
+            "Epoch 144/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0450 - accuracy: 1.0000\n",
+            "Epoch 145/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0090 - accuracy: 1.0000\n",
+            "Epoch 146/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0230 - accuracy: 1.0000\n",
+            "Epoch 147/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0096 - accuracy: 1.0000\n",
+            "Epoch 148/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 1.0000\n",
+            "Epoch 149/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0288 - accuracy: 1.0000\n",
+            "Epoch 150/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0313 - accuracy: 1.0000\n",
+            "Epoch 151/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0315 - accuracy: 1.0000\n",
+            "Epoch 152/200\n",
+            "10/10 [==============================] - ETA: 0s - loss: 4.1381e-04 - accuracy: 1.00 - 0s 2ms/step - loss: 0.0146 - accuracy: 1.0000\n",
+            "Epoch 153/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0198 - accuracy: 1.0000\n",
+            "Epoch 154/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0291 - accuracy: 0.9787\n",
+            "Epoch 155/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0294 - accuracy: 0.9787\n",
+            "Epoch 156/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0085 - accuracy: 1.0000\n",
+            "Epoch 157/200\n",
+            "10/10 [==============================] - 0s 998us/step - loss: 0.0434 - accuracy: 0.9787\n",
+            "Epoch 158/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0236 - accuracy: 1.0000\n",
+            "Epoch 159/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000\n",
+            "Epoch 160/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0170 - accuracy: 1.0000\n",
+            "Epoch 161/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0199 - accuracy: 1.0000\n",
+            "Epoch 162/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0073 - accuracy: 1.0000\n",
+            "Epoch 163/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0289 - accuracy: 1.0000\n",
+            "Epoch 164/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0165 - accuracy: 1.0000\n",
+            "Epoch 165/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0180 - accuracy: 1.0000\n",
+            "Epoch 166/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0083 - accuracy: 1.0000\n",
+            "Epoch 167/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 1.0000\n",
+            "Epoch 168/200\n",
+            "10/10 [==============================] - 0s 6ms/step - loss: 0.0112 - accuracy: 1.0000\n",
+            "Epoch 169/200\n",
+            "10/10 [==============================] - 0s 15ms/step - loss: 0.0166 - accuracy: 1.0000\n",
+            "Epoch 170/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000\n",
+            "Epoch 171/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.0424 - accuracy: 0.9787\n",
+            "Epoch 172/200\n",
+            "10/10 [==============================] - 0s 5ms/step - loss: 0.0393 - accuracy: 0.9787\n",
+            "Epoch 173/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0543 - accuracy: 0.9787\n",
+            "Epoch 174/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0177 - accuracy: 1.0000\n",
+            "Epoch 175/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0305 - accuracy: 0.9787\n",
+            "Epoch 176/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0069 - accuracy: 1.0000\n",
+            "Epoch 177/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0440 - accuracy: 0.9787\n",
+            "Epoch 178/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0337 - accuracy: 1.0000\n",
+            "Epoch 179/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0526 - accuracy: 0.9787\n",
+            "Epoch 180/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 1.0000\n",
+            "Epoch 181/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0091 - accuracy: 1.0000\n",
+            "Epoch 182/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0177 - accuracy: 1.0000\n",
+            "Epoch 183/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0137 - accuracy: 1.0000\n",
+            "Epoch 184/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0099 - accuracy: 1.0000\n",
+            "Epoch 185/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.1220 - accuracy: 0.9574\n",
+            "Epoch 186/200\n",
+            "10/10 [==============================] - 0s 740us/step - loss: 0.0532 - accuracy: 0.9787\n",
+            "Epoch 187/200\n",
+            "10/10 [==============================] - ETA: 0s - loss: 5.3321e-04 - accuracy: 1.00 - 0s 3ms/step - loss: 0.0055 - accuracy: 1.0000\n",
+            "Epoch 188/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 1.0000\n",
+            "Epoch 189/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0052 - accuracy: 1.0000\n",
+            "Epoch 190/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0429 - accuracy: 1.0000\n",
+            "Epoch 191/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0163 - accuracy: 1.0000\n",
+            "Epoch 192/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0160 - accuracy: 1.0000\n",
+            "Epoch 193/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0227 - accuracy: 1.0000\n",
+            "Epoch 194/200\n",
+            "10/10 [==============================] - 0s 4ms/step - loss: 0.0065 - accuracy: 1.0000\n",
+            "Epoch 195/200\n",
+            "10/10 [==============================] - 0s 3ms/step - loss: 0.0052 - accuracy: 1.0000\n",
+            "Epoch 196/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 9.3289e-04 - accuracy: 1.0000\n",
+            "Epoch 197/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0115 - accuracy: 1.0000\n",
+            "Epoch 198/200\n",
+            "10/10 [==============================] - 0s 1ms/step - loss: 0.0444 - accuracy: 0.9787\n",
+            "Epoch 199/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0101 - accuracy: 1.0000\n",
+            "Epoch 200/200\n",
+            "10/10 [==============================] - 0s 2ms/step - loss: 0.0175 - accuracy: 1.0000\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fitting and saving the model\n",
+        "hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)\n",
+        "model.save('model.h5', hist)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}