Chatbot_using_LSTM.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chatbot_using_LSTM.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xoKYBBO6xaV",
        "colab_type": "text"
      },
      "source": [
        "# **Chatbot using Seq2Seq LSTM models**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2SOmE99B7Is0",
        "colab_type": "text"
      },
      "source": [
        "This project is to create conversational chatbot using Sequence to sequence LSTM models. \n",
        "Sequence to sequence learning is about training models to convert from one domain to sequences another domain. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVuZTAV08qWY",
        "colab_type": "text"
      },
      "source": [
        "# Step 1: Import all the packages "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0mJXRse83hp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import tensorflow as tf\n",
        "import pickle\n",
        "from tensorflow.keras import layers, activations, models, preprocessing"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aL0Rz6JZ9eLW",
        "colab_type": "text"
      },
      "source": [
        "# Step 2: Download all the data from kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79eY9jkA9lnq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "c30e4676-d727-41b8-e70e-efbc0cd56378"
      },
      "source": [
        "!pip install kaggle "
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2020.6.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.41.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cU-sP689zyx",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "020ea742-0c0f-44d3-d60e-17d131309bc2"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-21bec02d-02f8-4774-a40b-ddf6c295ef02\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-21bec02d-02f8-4774-a40b-ddf6c295ef02\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"shrishtihore\",\"key\":\"cfdd5a2231a91244dfbb3ec69c6a3bd2\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raW7lVdQ9z7k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ~/.kaggle"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Adp0zjFP90Bl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPpi-Suj9z4s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8f723913-3e53-4ab4-9ec8-d32fbba47eea"
      },
      "source": [
        "!ls ~/.kaggle"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gocz2gG9zsC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rq0zmSYy-S0R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "d59ff550-19d4-4128-8b10-b3cdcec427de"
      },
      "source": [
        "!kaggle datasets download -d kausr25/chatterbotenglish"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading chatterbotenglish.zip to /content\n",
            "\r  0% 0.00/23.2k [00:00<?, ?B/s]\n",
            "\r100% 23.2k/23.2k [00:00<00:00, 18.8MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yk6kTtM-bQ0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "09396f22-f3e1-4099-8d26-fe6b88011690"
      },
      "source": [
        "!unzip /content/chatterbotenglish.zip"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/chatterbotenglish.zip\n",
            "  inflating: ai.yml                  \n",
            "  inflating: botprofile.yml          \n",
            "  inflating: computers.yml           \n",
            "  inflating: emotion.yml             \n",
            "  inflating: food.yml                \n",
            "  inflating: gossip.yml              \n",
            "  inflating: greetings.yml           \n",
            "  inflating: health.yml              \n",
            "  inflating: history.yml             \n",
            "  inflating: humor.yml               \n",
            "  inflating: literature.yml          \n",
            "  inflating: money.yml               \n",
            "  inflating: movies.yml              \n",
            "  inflating: politics.yml            \n",
            "  inflating: psychology.yml          \n",
            "  inflating: science.yml             \n",
            "  inflating: sports.yml              \n",
            "  inflating: trivia.yml              \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PMKhVnUHwGi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "834926aa-0696-4ca9-e88c-c73d4df4d7db"
      },
      "source": [
        "!wget https://github.com/shubham0204/Dataset_Archives/blob/master/chatbot_nlp.zip?raw=true -O chatbot_nlp.zip\n",
        "!unzip chatbot_nlp.zip"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-30 09:57:06--  https://github.com/shubham0204/Dataset_Archives/blob/master/chatbot_nlp.zip?raw=true\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/shubham0204/Dataset_Archives/raw/master/chatbot_nlp.zip [following]\n",
            "--2020-07-30 09:57:06--  https://github.com/shubham0204/Dataset_Archives/raw/master/chatbot_nlp.zip\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/shubham0204/Dataset_Archives/master/chatbot_nlp.zip [following]\n",
            "--2020-07-30 09:57:06--  https://raw.githubusercontent.com/shubham0204/Dataset_Archives/master/chatbot_nlp.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 24292 (24K) [application/zip]\n",
            "Saving to: ‘chatbot_nlp.zip’\n",
            "\n",
            "\rchatbot_nlp.zip       0%[                    ]       0  --.-KB/s               \rchatbot_nlp.zip     100%[===================>]  23.72K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-07-30 09:57:06 (1.90 MB/s) - ‘chatbot_nlp.zip’ saved [24292/24292]\n",
            "\n",
            "Archive:  chatbot_nlp.zip\n",
            "   creating: chatbot_nlp/\n",
            "   creating: chatbot_nlp/data/\n",
            "  inflating: chatbot_nlp/data/ai.yml  \n",
            "  inflating: chatbot_nlp/data/botprofile.yml  \n",
            "  inflating: chatbot_nlp/data/computers.yml  \n",
            "  inflating: chatbot_nlp/data/emotion.yml  \n",
            "  inflating: chatbot_nlp/data/food.yml  \n",
            "  inflating: chatbot_nlp/data/gossip.yml  \n",
            "  inflating: chatbot_nlp/data/greetings.yml  \n",
            "  inflating: chatbot_nlp/data/health.yml  \n",
            "  inflating: chatbot_nlp/data/history.yml  \n",
            "  inflating: chatbot_nlp/data/humor.yml  \n",
            "  inflating: chatbot_nlp/data/literature.yml  \n",
            "  inflating: chatbot_nlp/data/money.yml  \n",
            "  inflating: chatbot_nlp/data/movies.yml  \n",
            "  inflating: chatbot_nlp/data/politics.yml  \n",
            "  inflating: chatbot_nlp/data/psychology.yml  \n",
            "  inflating: chatbot_nlp/data/science.yml  \n",
            "  inflating: chatbot_nlp/data/sports.yml  \n",
            "  inflating: chatbot_nlp/data/trivia.yml  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4kJp6uO-fQE",
        "colab_type": "text"
      },
      "source": [
        "# Step 3: Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEV_hSXs-7mF",
        "colab_type": "text"
      },
      "source": [
        "### a) Reading the data from the files\n",
        "We parse each of the .yaml files.\n",
        "\n",
        "1. Concatenate two or more sentences if the answer has two or more of them.\n",
        "2. Remove unwanted data types which are produced while parsing the data.\n",
        "3. Append <START> and <END> to all the answers.\n",
        "4. Create a Tokenizer and load the whole vocabulary ( questions + answers ) into it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWYOjzOc_iQi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import preprocessing, utils\n",
        "import os\n",
        "import yaml"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qyUnopqjDjud",
        "colab_type": "text"
      },
      "source": [
        "The dataset contains .yml files which have pairs of different questions and their answers on varied subjects like history, bot profile, science etc.\n",
        "We can easily read them as folows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxG-s4k0CowI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dir_path = '/content/chatbot_nlp/data'\n",
        "files_list = os.listdir(dir_path + os.sep)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bRvbQ00Coy5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ef129a1c-3071-4d10-e6b2-7ac5a6ff2bac"
      },
      "source": [
        "questions = list()\n",
        "answers = list()\n",
        "\n",
        "for filepath in files_list:\n",
        "    stream = open( dir_path + os.sep + filepath , 'rb')\n",
        "    docs = yaml.safe_load(stream)\n",
        "    conversations = docs['conversations']\n",
        "    for con in conversations:\n",
        "        if len( con ) > 2 :\n",
        "            questions.append(con[0])\n",
        "            replies = con[ 1 : ]\n",
        "            ans = ''\n",
        "            for rep in replies:\n",
        "                ans += ' ' + rep\n",
        "            answers.append( ans )\n",
        "        elif len( con )> 1:\n",
        "            questions.append(con[0])\n",
        "            answers.append(con[1])\n",
        "\n",
        "answers_with_tags = list()\n",
        "for i in range( len( answers ) ):\n",
        "    if type( answers[i] ) == str:\n",
        "        answers_with_tags.append( answers[i] )\n",
        "    else:\n",
        "        questions.pop( i )\n",
        "\n",
        "answers = list()\n",
        "for i in range( len( answers_with_tags ) ) :\n",
        "    answers.append( '<START> ' + answers_with_tags[i] + ' <END>' )\n",
        "\n",
        "tokenizer = preprocessing.text.Tokenizer()\n",
        "tokenizer.fit_on_texts( questions + answers )\n",
        "VOCAB_SIZE = len( tokenizer.word_index )+1\n",
        "print( 'VOCAB SIZE : {}'.format( VOCAB_SIZE ))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "VOCAB SIZE : 1894\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMPqb8LxIeGI",
        "colab_type": "text"
      },
      "source": [
        "### b) Preparing data for Seq2Seq model\n",
        "\n",
        "This model requires 3 arrays encoder_input_data, decoder_input_data and decoder_output_data.\n",
        "\n",
        "For encoder_input_data:\n",
        "Tokensize the Questions and Pad them to their maximum Length.\n",
        "\n",
        "For decoder_input_data:\n",
        "Tokensize the Answers and Pad them to their maximum Length.\n",
        "\n",
        "For decoder_output_data:\n",
        "Tokensize the Answers and Remove the 1st element from all the tokenized_answers. This is the <START> element which was added earlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEfAPL4HCo1t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.models import Word2Vec\n",
        "import re"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqYoDsbSCo4f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab = []\n",
        "for word in tokenizer.word_index:\n",
        "  vocab.append(word)\n",
        "\n",
        "def tokenize(sentences):\n",
        "  tokens_list = []\n",
        "  vocabulary = []\n",
        "  for sentence in sentences:\n",
        "    sentence = sentence.lower()\n",
        "    sentence = re.sub('[^a-zA-Z]', ' ', sentence)\n",
        "    tokens = sentence.split()\n",
        "    vocabulary += tokens\n",
        "    tokens_list.append(tokens)\n",
        "  return tokens_list, vocabulary"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vKhieIwCo7J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e97b4a74-7384-478c-d4ae-082513257107"
      },
      "source": [
        "#encoder_input_data\n",
        "tokenized_questions = tokenizer.texts_to_sequences( questions )\n",
        "maxlen_questions = max( [len(x) for x in tokenized_questions ] )\n",
        "padded_questions = preprocessing.sequence.pad_sequences( tokenized_questions, maxlen = maxlen_questions, padding = 'post')\n",
        "encoder_input_data = np.array(padded_questions)\n",
        "print(encoder_input_data.shape, maxlen_questions)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(564, 22) 22\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJo7WPjLCo-q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "28b5e209-5389-4313-f3cd-f5451fa8c519"
      },
      "source": [
        "# decoder_input_data\n",
        "tokenized_answers = tokenizer.texts_to_sequences( answers )\n",
        "maxlen_answers = max( [ len(x) for x in tokenized_answers ] )\n",
        "padded_answers = preprocessing.sequence.pad_sequences( tokenized_answers , maxlen=maxlen_answers , padding='post' )\n",
        "decoder_input_data = np.array( padded_answers )\n",
        "print( decoder_input_data.shape , maxlen_answers )"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(564, 74) 74\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccY0wWdRCpCa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "07877cda-7e07-42b2-bb7e-772d118a3cf0"
      },
      "source": [
        "# decoder_output_data\n",
        "tokenized_answers = tokenizer.texts_to_sequences( answers )\n",
        "for i in range(len(tokenized_answers)) :\n",
        "    tokenized_answers[i] = tokenized_answers[i][1:]\n",
        "padded_answers = preprocessing.sequence.pad_sequences( tokenized_answers , maxlen=maxlen_answers , padding='post' )\n",
        "onehot_answers = utils.to_categorical( padded_answers , VOCAB_SIZE )\n",
        "decoder_output_data = np.array( onehot_answers )\n",
        "print( decoder_output_data.shape )"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(564, 74, 1894)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-D53pyucPCnk",
        "colab_type": "text"
      },
      "source": [
        "# Step 4: Defining Encoder Decoder Model\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3YjCFDwPRVN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "7bc112a0-6945-4100-e8d9-3bc5691797e5"
      },
      "source": [
        "encoder_inputs = tf.keras.layers.Input(shape=( maxlen_questions , ))\n",
        "encoder_embedding = tf.keras.layers.Embedding( VOCAB_SIZE, 200 , mask_zero=True ) (encoder_inputs)\n",
        "encoder_outputs , state_h , state_c = tf.keras.layers.LSTM( 200 , return_state=True )( encoder_embedding )\n",
        "encoder_states = [ state_h , state_c ]\n",
        "\n",
        "decoder_inputs = tf.keras.layers.Input(shape=( maxlen_answers ,  ))\n",
        "decoder_embedding = tf.keras.layers.Embedding( VOCAB_SIZE, 200 , mask_zero=True) (decoder_inputs)\n",
        "decoder_lstm = tf.keras.layers.LSTM( 200 , return_state=True , return_sequences=True )\n",
        "decoder_outputs , _ , _ = decoder_lstm ( decoder_embedding , initial_state=encoder_states )\n",
        "decoder_dense = tf.keras.layers.Dense( VOCAB_SIZE , activation=tf.keras.activations.softmax ) \n",
        "output = decoder_dense ( decoder_outputs )\n",
        "\n",
        "model = tf.keras.models.Model([encoder_inputs, decoder_inputs], output )\n",
        "model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='categorical_crossentropy')\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 22)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_2 (InputLayer)            [(None, 74)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding (Embedding)           (None, 22, 200)      378800      input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 74, 200)      378800      input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lstm (LSTM)                     [(None, 200), (None, 320800      embedding[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lstm_1 (LSTM)                   [(None, 74, 200), (N 320800      embedding_1[0][0]                \n",
            "                                                                 lstm[0][1]                       \n",
            "                                                                 lstm[0][2]                       \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 74, 1894)     380694      lstm_1[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 1,779,894\n",
            "Trainable params: 1,779,894\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVfSormAPb3w",
        "colab_type": "text"
      },
      "source": [
        "# Step 5: Training the Model\n",
        "\n",
        "We train the model for a number of epochs with RMSprop optimizer and categorical_crossentropy loss function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHlqQq64PYTH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c477c08f-55e4-41ac-b8b7-1973aa38f48a"
      },
      "source": [
        "model.fit([encoder_input_data , decoder_input_data], decoder_output_data, batch_size=50, epochs=150 ) \n",
        "model.save( 'model.h5' )"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "12/12 [==============================] - 10s 794ms/step - loss: 1.2930\n",
            "Epoch 2/150\n",
            "12/12 [==============================] - 9s 787ms/step - loss: 1.1173\n",
            "Epoch 3/150\n",
            "12/12 [==============================] - 9s 784ms/step - loss: 1.0944\n",
            "Epoch 4/150\n",
            "12/12 [==============================] - 9s 785ms/step - loss: 1.0743\n",
            "Epoch 5/150\n",
            "12/12 [==============================] - 9s 767ms/step - loss: 1.0536\n",
            "Epoch 6/150\n",
            "12/12 [==============================] - 9s 782ms/step - loss: 1.0380\n",
            "Epoch 7/150\n",
            "12/12 [==============================] - 9s 781ms/step - loss: 1.0224\n",
            "Epoch 8/150\n",
            "12/12 [==============================] - 9s 784ms/step - loss: 1.0106\n",
            "Epoch 9/150\n",
            "12/12 [==============================] - 9s 785ms/step - loss: 0.9956\n",
            "Epoch 10/150\n",
            "12/12 [==============================] - 9s 782ms/step - loss: 0.9817\n",
            "Epoch 11/150\n",
            "12/12 [==============================] - 9s 782ms/step - loss: 0.9674\n",
            "Epoch 12/150\n",
            "12/12 [==============================] - 9s 788ms/step - loss: 0.9527\n",
            "Epoch 13/150\n",
            "12/12 [==============================] - 9s 778ms/step - loss: 0.9378\n",
            "Epoch 14/150\n",
            "12/12 [==============================] - 9s 790ms/step - loss: 0.9227\n",
            "Epoch 15/150\n",
            "12/12 [==============================] - 9s 787ms/step - loss: 0.9085\n",
            "Epoch 16/150\n",
            "12/12 [==============================] - 10s 796ms/step - loss: 0.8940\n",
            "Epoch 17/150\n",
            "12/12 [==============================] - 9s 763ms/step - loss: 0.8800\n",
            "Epoch 18/150\n",
            "12/12 [==============================] - 10s 804ms/step - loss: 0.8669\n",
            "Epoch 19/150\n",
            "12/12 [==============================] - 9s 790ms/step - loss: 0.8526\n",
            "Epoch 20/150\n",
            "12/12 [==============================] - 9s 747ms/step - loss: 0.8400\n",
            "Epoch 21/150\n",
            "12/12 [==============================] - 9s 744ms/step - loss: 0.8272\n",
            "Epoch 22/150\n",
            "12/12 [==============================] - 9s 761ms/step - loss: 0.8152\n",
            "Epoch 23/150\n",
            "12/12 [==============================] - 9s 760ms/step - loss: 0.8022\n",
            "Epoch 24/150\n",
            "12/12 [==============================] - 9s 769ms/step - loss: 0.7914\n",
            "Epoch 25/150\n",
            "12/12 [==============================] - 9s 763ms/step - loss: 0.7781\n",
            "Epoch 26/150\n",
            "12/12 [==============================] - 9s 769ms/step - loss: 0.7673\n",
            "Epoch 27/150\n",
            "12/12 [==============================] - 9s 754ms/step - loss: 0.7555\n",
            "Epoch 28/150\n",
            "12/12 [==============================] - 9s 772ms/step - loss: 0.7441\n",
            "Epoch 29/150\n",
            "12/12 [==============================] - 9s 749ms/step - loss: 0.7332\n",
            "Epoch 30/150\n",
            "12/12 [==============================] - 9s 766ms/step - loss: 0.7222\n",
            "Epoch 31/150\n",
            "12/12 [==============================] - 9s 738ms/step - loss: 0.7116\n",
            "Epoch 32/150\n",
            "12/12 [==============================] - 9s 772ms/step - loss: 0.7008\n",
            "Epoch 33/150\n",
            "12/12 [==============================] - 9s 776ms/step - loss: 0.6900\n",
            "Epoch 34/150\n",
            "12/12 [==============================] - 9s 778ms/step - loss: 0.6796\n",
            "Epoch 35/150\n",
            "12/12 [==============================] - 9s 776ms/step - loss: 0.6686\n",
            "Epoch 36/150\n",
            "12/12 [==============================] - 9s 769ms/step - loss: 0.6581\n",
            "Epoch 37/150\n",
            "12/12 [==============================] - 9s 768ms/step - loss: 0.6480\n",
            "Epoch 38/150\n",
            "12/12 [==============================] - 9s 777ms/step - loss: 0.6375\n",
            "Epoch 39/150\n",
            "12/12 [==============================] - 9s 771ms/step - loss: 0.6273\n",
            "Epoch 40/150\n",
            "12/12 [==============================] - 9s 769ms/step - loss: 0.6179\n",
            "Epoch 41/150\n",
            "12/12 [==============================] - 9s 713ms/step - loss: 0.6070\n",
            "Epoch 42/150\n",
            "12/12 [==============================] - 9s 770ms/step - loss: 0.5982\n",
            "Epoch 43/150\n",
            "12/12 [==============================] - 9s 767ms/step - loss: 0.5875\n",
            "Epoch 44/150\n",
            "12/12 [==============================] - 9s 766ms/step - loss: 0.5781\n",
            "Epoch 45/150\n",
            "12/12 [==============================] - 9s 761ms/step - loss: 0.5682\n",
            "Epoch 46/150\n",
            "12/12 [==============================] - 9s 754ms/step - loss: 0.5578\n",
            "Epoch 47/150\n",
            "12/12 [==============================] - 9s 772ms/step - loss: 0.5503\n",
            "Epoch 48/150\n",
            "12/12 [==============================] - 9s 773ms/step - loss: 0.5380\n",
            "Epoch 49/150\n",
            "12/12 [==============================] - 9s 768ms/step - loss: 0.5296\n",
            "Epoch 50/150\n",
            "12/12 [==============================] - 9s 786ms/step - loss: 0.5213\n",
            "Epoch 51/150\n",
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            "Epoch 150/150\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1MIy1j9aVTo",
        "colab_type": "text"
      },
      "source": [
        "# Step 6: Defining Inference Models\n",
        "\n",
        "Encoder Inference Model: Takes questions as input and outputs LSTM states (h and c)\n",
        "\n",
        "Decoder Inference Model: Takes in 2 inputs one are the LSTM states, second are the answer input sequences. it will o/p the answers for questions which fed to the encoder model and it's state values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpLowS27cn8X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_inference_models():\n",
        "    \n",
        "    encoder_model = tf.keras.models.Model(encoder_inputs, encoder_states)\n",
        "    \n",
        "    decoder_state_input_h = tf.keras.layers.Input(shape=( 200 ,))\n",
        "    decoder_state_input_c = tf.keras.layers.Input(shape=( 200 ,))\n",
        "    \n",
        "    decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
        "    \n",
        "    decoder_outputs, state_h, state_c = decoder_lstm(\n",
        "        decoder_embedding , initial_state=decoder_states_inputs)\n",
        "    \n",
        "    decoder_states = [state_h, state_c]\n",
        "\n",
        "    decoder_outputs = decoder_dense(decoder_outputs)\n",
        "    \n",
        "    decoder_model = tf.keras.models.Model(\n",
        "        [decoder_inputs] + decoder_states_inputs,\n",
        "        [decoder_outputs] + decoder_states)\n",
        "    \n",
        "    return encoder_model , decoder_model"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwoYVsBTeYra",
        "colab_type": "text"
      },
      "source": [
        "# Step 7: Talking with the Chatbot\n",
        "\n",
        "define a method str_to_tokens which converts str questions to Integer tokens with padding.\n",
        "\n",
        "1. First, we take a question as input and predict the state values using enc_model.\n",
        "2. We set the state values in the decoder's LSTM.\n",
        "3. Then, we generate a sequence which contains the <start> element.\n",
        "4. We input this sequence in the dec_model.\n",
        "5. We replace the <start> element with the element which was predicted by the dec_model and update the state values.\n",
        "6. We carry out the above steps iteratively till we hit the <end> tag or the maximum answer length.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oA7Yx45Li3wo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def str_to_tokens( sentence : str ):\n",
        "\n",
        "    words = sentence.lower().split()\n",
        "    tokens_list = list()\n",
        "  \n",
        "    for word in words:\n",
        "        tokens_list.append( tokenizer.word_index[ word ] ) \n",
        "    return preprocessing.sequence.pad_sequences( [tokens_list] , maxlen=maxlen_questions , padding='post')\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUr4SQDveVb0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "abafd831-63b1-4eee-e35a-323ce20e9fba"
      },
      "source": [
        "enc_model , dec_model = make_inference_models()\n",
        "\n",
        "for _ in range(10):\n",
        "    states_values = enc_model.predict( str_to_tokens( input( 'Enter question : ' ) ) )\n",
        "    empty_target_seq = np.zeros( ( 1 , 1 ) )\n",
        "    empty_target_seq[0, 0] = tokenizer.word_index['start']\n",
        "    stop_condition = False\n",
        "    decoded_translation = ''\n",
        "    while not stop_condition :\n",
        "        dec_outputs , h , c = dec_model.predict([ empty_target_seq ] + states_values )\n",
        "        sampled_word_index = np.argmax( dec_outputs[0, -1, :] )\n",
        "        sampled_word = None\n",
        "        for word , index in tokenizer.word_index.items() :\n",
        "            if sampled_word_index == index :\n",
        "                decoded_translation += ' {}'.format( word )\n",
        "                sampled_word = word\n",
        "        \n",
        "        if sampled_word == 'end' or len(decoded_translation.split()) > maxlen_answers:\n",
        "            stop_condition = True\n",
        "            \n",
        "        empty_target_seq = np.zeros( ( 1 , 1 ) )  \n",
        "        empty_target_seq[ 0 , 0 ] = sampled_word_index\n",
        "        states_values = [ h , c ] \n",
        "\n",
        "    print( decoded_translation )"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Enter question : what is your name\n",
            "WARNING:tensorflow:Model was constructed with shape (None, 74) for input Tensor(\"input_2:0\", shape=(None, 74), dtype=float32), but it was called on an input with incompatible shape (None, 1).\n",
            " i am in the chat bot i am not that end\n",
            "Enter question : who are you\n",
            " i am just an artificial intelligence end\n",
            "Enter question : who am i\n",
            " i like to re read dune once every couple of years it's very inspirational end\n",
            "Enter question : who is spiderman\n",
            " a comic book story made into a movie end\n",
            "Enter question : how old are you\n",
            " i'm expecting a raise soon end\n",
            "Enter question : what is your age\n",
            " i am still young by your standards end\n",
            "Enter question : who made you\n",
            " my favorite subject has an few far far by end\n",
            "Enter question : what is the diameter of the earth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-a3576c66748e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mstates_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menc_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mstr_to_tokens\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'Enter question : '\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mempty_target_seq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mempty_target_seq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_index\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'start'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-25-28189ff90081>\u001b[0m in \u001b[0;36mstr_to_tokens\u001b[0;34m(sentence)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mtokens_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_index\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mword\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_sequences\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtokens_list\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxlen_questions\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'post'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'diameter'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPNjNwxUl2DO",
        "colab_type": "text"
      },
      "source": [
        "# Conversion to TFLite \n",
        "\n",
        "We can convert our seq2seq model to a TensorFlow Lite model so that we can use it on edge devices\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ywh_aJ-Ulxme",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 891
        },
        "outputId": "9744d53e-8655-4d78-bb73-d3b28a65eb95"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/f9/4314143fc7b5850858a08f8011a9ef1fd85086332af253bd87861c9bb32c/tf_nightly-2.4.0.dev20200730-cp36-cp36m-manylinux2010_x86_64.whl (325.2MB)\n",
            "\u001b[K     |████████████████████████████████| 325.2MB 28kB/s \n",
            "\u001b[?25hRequirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (2.10.0)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.3.3)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.2.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.34.2)\n",
            "Collecting tb-nightly<3.0.0a0,>=2.4.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/32/6e061abc3227d12203434bb696a825c69e170d883d06ee541b1cddc3cb8a/tb_nightly-2.4.0a20200729-py3-none-any.whl (6.7MB)\n",
            "\u001b[K     |████████████████████████████████| 6.7MB 45.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.30.0)\n",
            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.2)\n",
            "Collecting flatbuffers>=1.12\n",
            "  Downloading https://files.pythonhosted.org/packages/eb/26/712e578c5f14e26ae3314c39a1bdc4eb2ec2f4ddc89b708cf8e0a0d20423/flatbuffers-1.12-py2.py3-none-any.whl\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Collecting tf-estimator-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/88/f7/bf4c38ce1a8dc8cfd1937644f86e3a6801ca9e5a47bc47ed2d876a031285/tf_estimator_nightly-2.4.0.dev2020073001-py2.py3-none-any.whl (459kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 41.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.12.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.3.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.15.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.9.0)\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.18.5)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (49.1.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (1.7.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (1.17.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (1.0.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (3.2.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (4.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (0.2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (3.0.4)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (1.7.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (1.3.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (3.1.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<3.0.0a0,>=2.4.0a0->tf-nightly) (3.1.0)\n",
            "Installing collected packages: tb-nightly, flatbuffers, tf-estimator-nightly, tf-nightly\n",
            "Successfully installed flatbuffers-1.12 tb-nightly-2.4.0a20200729 tf-estimator-nightly-2.4.0.dev2020073001 tf-nightly-2.4.0.dev20200730\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorboard",
                  "tensorflow",
                  "tensorflow_estimator"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3C3SlT-mboI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "3b4f6af1-4a57-4f7e-8c6f-efcc3abc3edd"
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model( enc_model )\n",
        "buffer = converter.convert()\n",
        "open( 'enc_model.tflite' , 'wb' ).write( buffer )\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model( dec_model )\n",
        "open( 'dec_model.tflite' , 'wb' ).write( buffer )"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3472252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    }
  ]
}