Lab15_Chatbot.ipynb

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  "metadata": {
    "colab": {
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      "mount_file_id": "1Jvn8DS7e0oE-sLFlk3QP8y_Md8-89DDk",
      "authorship_tag": "ABX9TyOxnbreXgtIgwveR72VQTGU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TheSpaceMan915/Artificial-Intelligence/blob/main/Lab15_Chatbot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Лабораторная работа №15  Колычев Никита  ВПР34**"
      ],
      "metadata": {
        "id": "DJ-1o8vx23JN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Imports**"
      ],
      "metadata": {
        "id": "QKsPrp_OT4qN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WfsOnTgCaeXV"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import nltk\n",
        "import json\n",
        "import string\n",
        "import random\n",
        "from random import shuffle\n",
        "from pathlib import Path\n",
        "from nltk import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.saving import load_model\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Dense, Dropout, \\\n",
        "BatchNormalization, Input, ReLU, Softmax"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gjndptDUKx_",
        "outputId": "1cc325a6-bc82-4b8e-f322-9778a0119a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSSsiYspcgfx"
      },
      "source": [
        "# **Text Functions**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# removing punctuation marks from a string\n",
        "def remove_punctuation(text):\n",
        "  table = str.maketrans('', '', string.punctuation)\n",
        "  cleared_text = text.translate(table)\n",
        "  return cleared_text"
      ],
      "metadata": {
        "id": "NBy9Rr71ky_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q_XGnKXSdvo0"
      },
      "outputs": [],
      "source": [
        "def create_list_tokens(text):\n",
        "  list_tokens = word_tokenize(text, language=\"russian\")\n",
        "  return list_tokens"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_tokes_to_lowercase(list_tokens):\n",
        "  list_lowercase_tokes = [token.lower() for token in list_tokens]\n",
        "  return list_lowercase_tokes"
      ],
      "metadata": {
        "id": "M4-PbzPYLSWv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_stopwords(list_tokens):\n",
        "  list_stopwords = nltk.corpus.stopwords.words(\"russian\")\n",
        "  cleared_list_tokens = [token for token in list_tokens if token not in list_stopwords]\n",
        "  return cleared_list_tokens"
      ],
      "metadata": {
        "id": "vhgaADqP6kXV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_nonalphabetic_words(list_tokens):\n",
        "  cleared_list_tokens = [token for token in list_tokens if token.isalpha()]\n",
        "  return cleared_list_tokens"
      ],
      "metadata": {
        "id": "r4TgchIbXqV7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_list_lemmas(list_tokens):\n",
        "  lemmatizer = WordNetLemmatizer()\n",
        "  list_lemmas = [lemmatizer.lemmatize(token) for token in list_tokens]\n",
        "  return list_lemmas"
      ],
      "metadata": {
        "id": "qy7pX0MHXFTv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clear_text(text):\n",
        "  cleared_text = remove_punctuation(text)\n",
        "  list_tokens = create_list_tokens(cleared_text)\n",
        "  cleared_list_tokens = remove_nonalphabetic_words(list_tokens)\n",
        "  cleared_list_tokens = remove_stopwords(cleared_list_tokens)\n",
        "  list_lowercase_tokens = convert_tokes_to_lowercase(cleared_list_tokens)\n",
        "  list_lemmas = create_list_lemmas(list_lowercase_tokens)\n",
        "  return list_lemmas"
      ],
      "metadata": {
        "id": "r_EFcMmo-FFV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Functions**"
      ],
      "metadata": {
        "id": "vSAnxav5eZR8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model(input_layer_shape, NUMBER_CLASSES):\n",
        "  inputs = Input(input_layer_shape)\n",
        "  x = Dense(128, kernel_initializer=\"he_uniform\")(inputs)\n",
        "  x = BatchNormalization()(x)\n",
        "  x = ReLU()(x)\n",
        "  x = Dropout(0.2)(x)\n",
        "\n",
        "  x = Dense(64, kernel_initializer=\"he_uniform\")(x)\n",
        "  x = BatchNormalization()(x)\n",
        "  x = ReLU()(x)\n",
        "  x = Dropout(0.2)(x)\n",
        "\n",
        "  x = Dense(NUMBER_CLASSES, kernel_initializer=\"he_uniform\")(x)\n",
        "  outputs = Softmax()(x)\n",
        "\n",
        "  model = Model(inputs, outputs)\n",
        "  model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "  return model"
      ],
      "metadata": {
        "id": "fEJbSF4meTXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Plotting Functions**"
      ],
      "metadata": {
        "id": "f5CG04UusJwh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_model_history(model_history):\n",
        "    fig, (axes1, axes2) = plt.subplots(1, 2, figsize=(14, 5))\n",
        "    plt.rcParams.update({\"font.size\": 14, \"font.family\": \"serif\"})\n",
        "\n",
        "    axes1.plot(model_history.history[\"accuracy\"], label=\"accuracy\")\n",
        "    axes1.plot(model_history.history[\"val_accuracy\"], label=\"val_accuracy\")\n",
        "    axes1.set(title=\"Accuracy of the model\")\n",
        "    axes1.set(xlabel=\"Epoch\")\n",
        "    axes1.set(ylabel=\"Accuracy\")\n",
        "    axes1.legend(loc=4)\n",
        "\n",
        "    axes2.plot(model_history.history[\"loss\"], label=\"loss\")\n",
        "    axes2.plot(model_history.history[\"val_loss\"], label=\"val_loss\")\n",
        "    axes2.set(title=\"Losses of the model\")\n",
        "    axes2.set(xlabel=\"Epoch\")\n",
        "    axes2.set(ylabel=\"Loss\")\n",
        "    axes2.legend(loc=1)\n",
        "\n",
        "    fig.tight_layout()\n",
        "    plt.savefig(\"plot_model_history{}\".format(random.randint(0, 10000)))"
      ],
      "metadata": {
        "id": "Wc68G15BsJLu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **User Input Functions**"
      ],
      "metadata": {
        "id": "Bf2S3w2NN020"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_list_tags(json_file):\n",
        "  list_tags = []\n",
        "  for intent in json_file[\"intents\"]:\n",
        "      list_tags.append(intent[\"tag\"])  \n",
        "  return list_tags        "
      ],
      "metadata": {
        "id": "Dp1jRuALQM4Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_bag_words(text, list_vocabulary_words, show_details=False):\n",
        "  cleared_list_tokens = clear_text(text)\n",
        "\n",
        "  # creating a bag of words for the user's input\n",
        "  list_length = len(list_vocabulary_words)\n",
        "  bag_words = [0] * list_length\n",
        "  for s in cleared_list_tokens:\n",
        "      for i, w in enumerate(list_vocabulary_words):\n",
        "          if w == s:\n",
        "              bag_words[i] = 1\n",
        "              if show_details:\n",
        "                  print(\"Found in bag: %s\"% w)\n",
        "  \n",
        "  # reshaping the 1d array to 2d array in order to pass it to the model\n",
        "  arr_bag_words = np.array(bag_words)\n",
        "  arr_bag_words = np.reshape(arr_bag_words, (-1, list_length))\n",
        "  return arr_bag_words"
      ],
      "metadata": {
        "id": "TNlZPywJNzS0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classify(text, list_vocabulary_words, list_tags):\n",
        "  ERROR_THRESHOLD = 0.25\n",
        "  bag_words = create_bag_words(text, list_vocabulary_words)\n",
        "  print(\"bag_words for the text:\", bag_words)\n",
        "  arr_predictions = model.predict(bag_words)[0]\n",
        "\n",
        "  # filtering the predictions\n",
        "  arr_predictions  = [[i, prediction] for i, prediction in enumerate(arr_predictions) if prediction > ERROR_THRESHOLD]\n",
        "\n",
        "  # sorting the predictions according to their probability\n",
        "  arr_predictions .sort(key=lambda x: x[1], reverse=True)\n",
        "\n",
        "  # getting the list of labels and predictions\n",
        "  list_labels = []\n",
        "  for prediction in arr_predictions:\n",
        "      list_labels.append((list_tags[prediction[0]], prediction[1]))\n",
        "  return list_labels"
      ],
      "metadata": {
        "id": "ZO3fbXgw861c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def query(text, list_vocabulary_words, list_tags, intents, userID=\"123\", show_details=False):\n",
        "  dict_context = {}\n",
        "  list_results = classify(text, list_vocabulary_words, list_tags)\n",
        "\n",
        "  if list_results:\n",
        "      while list_results:\n",
        "          for intent in intents[\"intents\"]:    \n",
        "              # finding the tag for the first result\n",
        "              if intent[\"tag\"] == list_results[0][0]:\n",
        "                  \n",
        "                  # setting the context for this intent\n",
        "                  if \"context_set\" in intent:\n",
        "                      dict_context[userID] = intent[\"context_set\"]\n",
        "\n",
        "                  # checking if the context is relevant to the user\n",
        "                  if not \"context_filter\" in intent or \\\n",
        "                      (userID in dict_context and \"context_filter\" in intent and intent[\"context_filter\"] == dict_context[userID]):\n",
        "                      if show_details: \n",
        "                        print (\"tag:\", intent[\"tag\"]) \n",
        "                      \n",
        "                      # getting a random respons\n",
        "                      return print(random.choice(intent[\"responses\"]))\n",
        "          list_results.pop(0)"
      ],
      "metadata": {
        "id": "mAyXia7wAhvP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Задание 1**\n",
        "Разработать чат бота"
      ],
      "metadata": {
        "id": "LdnjHllG5G05"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the data from the json file\n",
        "path = Path(\"/content/drive/MyDrive/Colab Notebooks/AI/Other files/my_intents.json\")\n",
        "with open(path) as f:\n",
        "  my_intents = json.load(f)"
      ],
      "metadata": {
        "id": "nGSde5o65Uzc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_vocabulary_words = []\n",
        "list_documents = []\n",
        "list_tags = create_list_tags(my_intents)\n",
        "\n",
        "# clearing the text\n",
        "for intent in my_intents[\"intents\"]:\n",
        "  for pattern in intent[\"patterns\"]:\n",
        "    cleared_list_tokens = clear_text(pattern)\n",
        "    list_vocabulary_words.extend(cleared_list_tokens)\n",
        "\n",
        "    # saving a pattern as a document\n",
        "    list_documents.append((cleared_list_tokens, intent[\"tag\"]))"
      ],
      "metadata": {
        "id": "yGXikqCGNixB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of words:\", len(list_vocabulary_words))\n",
        "print(\"words:\")\n",
        "print(list_vocabulary_words)\n",
        "print()\n",
        "\n",
        "print(\"number of  documents:\", len(list_documents))\n",
        "print(\"documents:\")\n",
        "print(list_documents)\n",
        "print()\n",
        "\n",
        "print(\"number of tags:\", len(list_tags))\n",
        "print(\"tags:\")\n",
        "print(list_tags)\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NqgE7_rDTD98",
        "outputId": "e98a4dbe-d30d-4f84-ae3e-3aa4032eeff2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of words: 42\n",
            "words:\n",
            "['привет', 'как', 'дела', 'ктонибудь', 'привет', 'добрый', 'день', 'пока', 'увидимся', 'позже', 'до', 'свидания', 'спасибо', 'спасибо', 'это', 'полезно', 'как', 'просмотреть', 'данные', 'своём', 'прогрессе', 'где', 'могу', 'узнать', 'количество', 'оставшихся', 'уроков', 'как', 'узнать', 'ежедневную', 'активность', 'сайте', 'где', 'могу', 'найти', 'уроки', 'где', 'находятся', 'уроки', 'как', 'просмотреть', 'уроки']\n",
            "\n",
            "number of  documents: 17\n",
            "documents:\n",
            "[(['привет'], 'приветствие'), (['как', 'дела'], 'приветствие'), (['ктонибудь'], 'приветствие'), (['привет'], 'приветствие'), (['добрый', 'день'], 'приветствие'), (['пока'], 'до свидания'), (['увидимся', 'позже'], 'до свидания'), (['до', 'свидания'], 'до свидания'), (['спасибо'], 'спасибо'), (['спасибо'], 'спасибо'), (['это', 'полезно'], 'спасибо'), (['как', 'просмотреть', 'данные', 'своём', 'прогрессе'], 'личный кабинет'), (['где', 'могу', 'узнать', 'количество', 'оставшихся', 'уроков'], 'личный кабинет'), (['как', 'узнать', 'ежедневную', 'активность', 'сайте'], 'личный кабинет'), (['где', 'могу', 'найти', 'уроки'], 'уроки'), (['где', 'находятся', 'уроки'], 'уроки'), (['как', 'просмотреть', 'уроки'], 'уроки')]\n",
            "\n",
            "number of tags: 5\n",
            "tags:\n",
            "['приветствие', 'до свидания', 'спасибо', 'личный кабинет', 'уроки']\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# deleting duplicates\n",
        "cleared_list_vocabulary_words = sorted(list(set(list_vocabulary_words)))\n",
        "print(\"number of words:\", len(cleared_list_vocabulary_words))\n",
        "print(cleared_list_vocabulary_words)"
      ],
      "metadata": {
        "id": "UA_Uah40kZ5I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6db1f42f-1ca0-48ff-ee23-906231b3902b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of words: 30\n",
            "['активность', 'где', 'данные', 'дела', 'день', 'до', 'добрый', 'ежедневную', 'как', 'количество', 'ктонибудь', 'могу', 'найти', 'находятся', 'оставшихся', 'позже', 'пока', 'полезно', 'привет', 'прогрессе', 'просмотреть', 'сайте', 'свидания', 'своём', 'спасибо', 'увидимся', 'узнать', 'уроки', 'уроков', 'это']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating data to train the model\n",
        "list_training = []\n",
        "\n",
        "# creating a list to store outputs\n",
        "list_empty_outputs = [0] * len(list_tags)\n",
        "for document in list_documents:\n",
        "  bag_words = []\n",
        "\n",
        "  # saving the patterns from the tuples ([patters], tag)\n",
        "  list_patterns = document[0]\n",
        "  \n",
        "  # filling the bag of words\n",
        "  for word in cleared_list_vocabulary_words:\n",
        "    bag_words.append(1) if word in list_patterns else bag_words.append(0)\n",
        "\n",
        "  # marking the tag of a document as 1\n",
        "  list_output = list_empty_outputs.copy()\n",
        "  list_output[list_tags.index(document[1])] = 1\n",
        "  list_training.append([bag_words, list_output])\n",
        "  print(list_training)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ev9t4BBiKGUX",
        "outputId": "fad62a9f-2baa-4f0f-a826-298bc6c2e4ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0]], [[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0]], [[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0]], [[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]]]\n",
            "[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], [[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0]], [[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]], [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]], [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# shuffling the list\n",
        "shuffle(list_training)\n",
        "for list_words in list_training:\n",
        "  print(list_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4SSXm75giFZ",
        "outputId": "5af2fef3-22a3-45db-de85-706052f3ac81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0]]\n",
            "[[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]]\n",
            "[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]\n",
            "[[0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0], [0, 0, 0, 1, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]\n",
            "[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0]]\n",
            "[[0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0]]\n",
            "[[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0]]\n",
            "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0]]\n",
            "[[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# preparing the data\n",
        "arr_training = np.array(list_training, dtype=object)\n",
        "dataset = list(arr_training[:, 0])\n",
        "labels = list(arr_training[:, 1])\n",
        "train_dataset, test_dataset, train_labels, test_labels = train_test_split(dataset, labels, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "YZEhnCD2d11J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NUMBER_CLASSES = len(list_tags)\n",
        "input_layer_shape = (len(train_dataset[0]), )\n",
        "print(\"input_layer_shape:\", input_layer_shape)"
      ],
      "metadata": {
        "id": "0bVWNffKj28h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dbfaa62-1b9a-498d-d570-62c3510aff75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_layer_shape: (30,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = create_model(input_layer_shape, NUMBER_CLASSES)\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "dPhd0OOahPwS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "181f84cd-7ca9-4d45-bb94-48eb4177d6b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 30)]              0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 128)               3968      \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_6 (ReLU)              (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " re_lu_7 (ReLU)              (None, 64)                0         \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 5)                 325       \n",
            "                                                                 \n",
            " softmax_3 (Softmax)         (None, 5)                 0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 13,317\n",
            "Trainable params: 12,933\n",
            "Non-trainable params: 384\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history = model.fit(train_dataset, train_labels,\n",
        "          validation_data=(test_dataset, test_labels),\n",
        "          epochs=200, batch_size=1024,\n",
        "          verbose=1)"
      ],
      "metadata": {
        "id": "6IbIo0D2jyId",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16d055bd-b270-43ee-957d-7d1fc2b1b945"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 2.1302 - accuracy: 0.1538 - val_loss: 1.4286 - val_accuracy: 0.5000\n",
            "Epoch 2/200\n",
            "1/1 [==============================] - 0s 98ms/step - loss: 1.8983 - accuracy: 0.2308 - val_loss: 1.4152 - val_accuracy: 0.5000\n",
            "Epoch 3/200\n",
            "1/1 [==============================] - 0s 90ms/step - loss: 1.8833 - accuracy: 0.1538 - val_loss: 1.4043 - val_accuracy: 0.5000\n",
            "Epoch 4/200\n",
            "1/1 [==============================] - 0s 106ms/step - loss: 1.5359 - accuracy: 0.3077 - val_loss: 1.3935 - val_accuracy: 0.5000\n",
            "Epoch 5/200\n",
            "1/1 [==============================] - 0s 95ms/step - loss: 1.4073 - accuracy: 0.6154 - val_loss: 1.3855 - val_accuracy: 0.5000\n",
            "Epoch 6/200\n",
            "1/1 [==============================] - 0s 90ms/step - loss: 1.4214 - accuracy: 0.3846 - val_loss: 1.3767 - val_accuracy: 0.5000\n",
            "Epoch 7/200\n",
            "1/1 [==============================] - 0s 105ms/step - loss: 1.2236 - accuracy: 0.4615 - val_loss: 1.3672 - val_accuracy: 0.5000\n",
            "Epoch 8/200\n",
            "1/1 [==============================] - 0s 101ms/step - loss: 1.2380 - accuracy: 0.5385 - val_loss: 1.3573 - val_accuracy: 0.5000\n",
            "Epoch 9/200\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 1.1577 - accuracy: 0.6154 - val_loss: 1.3474 - val_accuracy: 0.5000\n",
            "Epoch 10/200\n",
            "1/1 [==============================] - 0s 103ms/step - loss: 1.1187 - accuracy: 0.7692 - val_loss: 1.3375 - val_accuracy: 0.5000\n",
            "Epoch 11/200\n",
            "1/1 [==============================] - 0s 104ms/step - loss: 1.0235 - accuracy: 0.6923 - val_loss: 1.3282 - val_accuracy: 0.5000\n",
            "Epoch 12/200\n",
            "1/1 [==============================] - 0s 112ms/step - loss: 0.9476 - accuracy: 0.6923 - val_loss: 1.3185 - val_accuracy: 0.5000\n",
            "Epoch 13/200\n",
            "1/1 [==============================] - 0s 116ms/step - loss: 0.7735 - accuracy: 0.8462 - val_loss: 1.3101 - val_accuracy: 0.5000\n",
            "Epoch 14/200\n",
            "1/1 [==============================] - 0s 103ms/step - loss: 0.9123 - accuracy: 0.6923 - val_loss: 1.3002 - val_accuracy: 0.5000\n",
            "Epoch 15/200\n",
            "1/1 [==============================] - 0s 83ms/step - loss: 0.9062 - accuracy: 0.6923 - val_loss: 1.2908 - val_accuracy: 0.5000\n",
            "Epoch 16/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.7143 - accuracy: 0.8462 - val_loss: 1.2813 - val_accuracy: 0.5000\n",
            "Epoch 17/200\n",
            "1/1 [==============================] - 0s 107ms/step - loss: 0.8138 - accuracy: 0.7692 - val_loss: 1.2731 - val_accuracy: 0.7500\n",
            "Epoch 18/200\n",
            "1/1 [==============================] - 0s 88ms/step - loss: 0.8173 - accuracy: 0.7692 - val_loss: 1.2644 - val_accuracy: 0.7500\n",
            "Epoch 19/200\n",
            "1/1 [==============================] - 0s 97ms/step - loss: 0.7218 - accuracy: 0.6923 - val_loss: 1.2548 - val_accuracy: 0.7500\n",
            "Epoch 20/200\n",
            "1/1 [==============================] - 0s 102ms/step - loss: 0.5649 - accuracy: 0.9231 - val_loss: 1.2452 - val_accuracy: 0.7500\n",
            "Epoch 21/200\n",
            "1/1 [==============================] - 0s 103ms/step - loss: 0.6817 - accuracy: 0.8462 - val_loss: 1.2341 - val_accuracy: 0.7500\n",
            "Epoch 22/200\n",
            "1/1 [==============================] - 0s 88ms/step - loss: 0.4268 - accuracy: 1.0000 - val_loss: 1.2237 - val_accuracy: 0.7500\n",
            "Epoch 23/200\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.4868 - accuracy: 1.0000 - val_loss: 1.2131 - val_accuracy: 0.7500\n",
            "Epoch 24/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.5067 - accuracy: 0.8462 - val_loss: 1.2023 - val_accuracy: 0.7500\n",
            "Epoch 25/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.4450 - accuracy: 1.0000 - val_loss: 1.1939 - val_accuracy: 0.7500\n",
            "Epoch 26/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.4014 - accuracy: 1.0000 - val_loss: 1.1822 - val_accuracy: 0.7500\n",
            "Epoch 27/200\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.3407 - accuracy: 1.0000 - val_loss: 1.1730 - val_accuracy: 0.7500\n",
            "Epoch 28/200\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.3558 - accuracy: 1.0000 - val_loss: 1.1628 - val_accuracy: 0.7500\n",
            "Epoch 29/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.3271 - accuracy: 0.9231 - val_loss: 1.1525 - val_accuracy: 0.7500\n",
            "Epoch 30/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.3853 - accuracy: 1.0000 - val_loss: 1.1428 - val_accuracy: 0.7500\n",
            "Epoch 31/200\n",
            "1/1 [==============================] - 0s 76ms/step - loss: 0.3258 - accuracy: 0.9231 - val_loss: 1.1334 - val_accuracy: 0.7500\n",
            "Epoch 32/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.3469 - accuracy: 1.0000 - val_loss: 1.1244 - val_accuracy: 0.7500\n",
            "Epoch 33/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.3635 - accuracy: 1.0000 - val_loss: 1.1152 - val_accuracy: 0.7500\n",
            "Epoch 34/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.3477 - accuracy: 1.0000 - val_loss: 1.1070 - val_accuracy: 0.7500\n",
            "Epoch 35/200\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.2600 - accuracy: 1.0000 - val_loss: 1.0985 - val_accuracy: 0.7500\n",
            "Epoch 36/200\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.2758 - accuracy: 1.0000 - val_loss: 1.0900 - val_accuracy: 0.7500\n",
            "Epoch 37/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.1712 - accuracy: 1.0000 - val_loss: 1.0816 - val_accuracy: 0.7500\n",
            "Epoch 38/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.2790 - accuracy: 1.0000 - val_loss: 1.0744 - val_accuracy: 0.7500\n",
            "Epoch 39/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.2064 - accuracy: 1.0000 - val_loss: 1.0661 - val_accuracy: 0.7500\n",
            "Epoch 40/200\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.2775 - accuracy: 1.0000 - val_loss: 1.0589 - val_accuracy: 0.7500\n",
            "Epoch 41/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.2015 - accuracy: 1.0000 - val_loss: 1.0506 - val_accuracy: 0.7500\n",
            "Epoch 42/200\n",
            "1/1 [==============================] - 0s 83ms/step - loss: 0.1799 - accuracy: 1.0000 - val_loss: 1.0427 - val_accuracy: 0.7500\n",
            "Epoch 43/200\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.2952 - accuracy: 1.0000 - val_loss: 1.0343 - val_accuracy: 0.7500\n",
            "Epoch 44/200\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.1741 - accuracy: 1.0000 - val_loss: 1.0251 - val_accuracy: 0.7500\n",
            "Epoch 45/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.3046 - accuracy: 1.0000 - val_loss: 1.0170 - val_accuracy: 0.7500\n",
            "Epoch 46/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.1964 - accuracy: 1.0000 - val_loss: 1.0096 - val_accuracy: 0.7500\n",
            "Epoch 47/200\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.2460 - accuracy: 1.0000 - val_loss: 1.0027 - val_accuracy: 0.7500\n",
            "Epoch 48/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.1793 - accuracy: 1.0000 - val_loss: 0.9967 - val_accuracy: 0.7500\n",
            "Epoch 49/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.1650 - accuracy: 1.0000 - val_loss: 0.9902 - val_accuracy: 0.7500\n",
            "Epoch 50/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.1633 - accuracy: 1.0000 - val_loss: 0.9842 - val_accuracy: 0.7500\n",
            "Epoch 51/200\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 0.1779 - accuracy: 1.0000 - val_loss: 0.9787 - val_accuracy: 0.7500\n",
            "Epoch 52/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.1602 - accuracy: 1.0000 - val_loss: 0.9723 - val_accuracy: 0.7500\n",
            "Epoch 53/200\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.1312 - accuracy: 1.0000 - val_loss: 0.9658 - val_accuracy: 0.7500\n",
            "Epoch 54/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.1205 - accuracy: 1.0000 - val_loss: 0.9585 - val_accuracy: 0.7500\n",
            "Epoch 55/200\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.0888 - accuracy: 1.0000 - val_loss: 0.9528 - val_accuracy: 0.7500\n",
            "Epoch 56/200\n",
            "1/1 [==============================] - 0s 88ms/step - loss: 0.1323 - accuracy: 1.0000 - val_loss: 0.9467 - val_accuracy: 0.7500\n",
            "Epoch 57/200\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.1840 - accuracy: 1.0000 - val_loss: 0.9406 - val_accuracy: 0.7500\n",
            "Epoch 58/200\n",
            "1/1 [==============================] - 0s 79ms/step - loss: 0.0937 - accuracy: 1.0000 - val_loss: 0.9354 - val_accuracy: 0.7500\n",
            "Epoch 59/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.1384 - accuracy: 1.0000 - val_loss: 0.9309 - val_accuracy: 0.7500\n",
            "Epoch 60/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.1284 - accuracy: 1.0000 - val_loss: 0.9258 - val_accuracy: 0.7500\n",
            "Epoch 61/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.1374 - accuracy: 1.0000 - val_loss: 0.9213 - val_accuracy: 0.7500\n",
            "Epoch 62/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0952 - accuracy: 1.0000 - val_loss: 0.9162 - val_accuracy: 0.7500\n",
            "Epoch 63/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.1024 - accuracy: 1.0000 - val_loss: 0.9130 - val_accuracy: 0.7500\n",
            "Epoch 64/200\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.2176 - accuracy: 0.9231 - val_loss: 0.9077 - val_accuracy: 0.7500\n",
            "Epoch 65/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.1359 - accuracy: 1.0000 - val_loss: 0.9021 - val_accuracy: 0.7500\n",
            "Epoch 66/200\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.1255 - accuracy: 1.0000 - val_loss: 0.8978 - val_accuracy: 0.7500\n",
            "Epoch 67/200\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0972 - accuracy: 1.0000 - val_loss: 0.8934 - val_accuracy: 0.7500\n",
            "Epoch 68/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.1675 - accuracy: 1.0000 - val_loss: 0.8895 - val_accuracy: 0.7500\n",
            "Epoch 69/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0989 - accuracy: 1.0000 - val_loss: 0.8838 - val_accuracy: 0.7500\n",
            "Epoch 70/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.1184 - accuracy: 1.0000 - val_loss: 0.8796 - val_accuracy: 0.7500\n",
            "Epoch 71/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.1119 - accuracy: 1.0000 - val_loss: 0.8749 - val_accuracy: 0.7500\n",
            "Epoch 72/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0795 - accuracy: 1.0000 - val_loss: 0.8719 - val_accuracy: 0.7500\n",
            "Epoch 73/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0781 - accuracy: 1.0000 - val_loss: 0.8671 - val_accuracy: 0.7500\n",
            "Epoch 74/200\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.1062 - accuracy: 1.0000 - val_loss: 0.8647 - val_accuracy: 0.7500\n",
            "Epoch 75/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0631 - accuracy: 1.0000 - val_loss: 0.8598 - val_accuracy: 0.7500\n",
            "Epoch 76/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0573 - accuracy: 1.0000 - val_loss: 0.8561 - val_accuracy: 0.7500\n",
            "Epoch 77/200\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0855 - accuracy: 1.0000 - val_loss: 0.8512 - val_accuracy: 0.7500\n",
            "Epoch 78/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0586 - accuracy: 1.0000 - val_loss: 0.8471 - val_accuracy: 0.7500\n",
            "Epoch 79/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.1481 - accuracy: 1.0000 - val_loss: 0.8435 - val_accuracy: 0.7500\n",
            "Epoch 80/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0996 - accuracy: 1.0000 - val_loss: 0.8401 - val_accuracy: 0.7500\n",
            "Epoch 81/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0840 - accuracy: 1.0000 - val_loss: 0.8381 - val_accuracy: 0.7500\n",
            "Epoch 82/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0815 - accuracy: 1.0000 - val_loss: 0.8343 - val_accuracy: 0.7500\n",
            "Epoch 83/200\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.1004 - accuracy: 1.0000 - val_loss: 0.8320 - val_accuracy: 0.7500\n",
            "Epoch 84/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0661 - accuracy: 1.0000 - val_loss: 0.8283 - val_accuracy: 0.7500\n",
            "Epoch 85/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0820 - accuracy: 1.0000 - val_loss: 0.8231 - val_accuracy: 0.7500\n",
            "Epoch 86/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0682 - accuracy: 1.0000 - val_loss: 0.8197 - val_accuracy: 0.7500\n",
            "Epoch 87/200\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.0568 - accuracy: 1.0000 - val_loss: 0.8162 - val_accuracy: 0.7500\n",
            "Epoch 88/200\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 0.0550 - accuracy: 1.0000 - val_loss: 0.8131 - val_accuracy: 0.7500\n",
            "Epoch 89/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0820 - accuracy: 1.0000 - val_loss: 0.8091 - val_accuracy: 0.7500\n",
            "Epoch 90/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0534 - accuracy: 1.0000 - val_loss: 0.8054 - val_accuracy: 0.7500\n",
            "Epoch 91/200\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0993 - accuracy: 1.0000 - val_loss: 0.8010 - val_accuracy: 0.7500\n",
            "Epoch 92/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0801 - accuracy: 1.0000 - val_loss: 0.7978 - val_accuracy: 0.7500\n",
            "Epoch 93/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0656 - accuracy: 1.0000 - val_loss: 0.7942 - val_accuracy: 0.7500\n",
            "Epoch 94/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0919 - accuracy: 1.0000 - val_loss: 0.7898 - val_accuracy: 0.7500\n",
            "Epoch 95/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0848 - accuracy: 1.0000 - val_loss: 0.7844 - val_accuracy: 0.7500\n",
            "Epoch 96/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0823 - accuracy: 1.0000 - val_loss: 0.7811 - val_accuracy: 0.7500\n",
            "Epoch 97/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0613 - accuracy: 1.0000 - val_loss: 0.7785 - val_accuracy: 0.7500\n",
            "Epoch 98/200\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0508 - accuracy: 1.0000 - val_loss: 0.7747 - val_accuracy: 0.7500\n",
            "Epoch 99/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0636 - accuracy: 1.0000 - val_loss: 0.7715 - val_accuracy: 0.7500\n",
            "Epoch 100/200\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0982 - accuracy: 1.0000 - val_loss: 0.7683 - val_accuracy: 0.7500\n",
            "Epoch 101/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0702 - accuracy: 1.0000 - val_loss: 0.7648 - val_accuracy: 0.7500\n",
            "Epoch 102/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0665 - accuracy: 1.0000 - val_loss: 0.7617 - val_accuracy: 0.7500\n",
            "Epoch 103/200\n",
            "1/1 [==============================] - 0s 100ms/step - loss: 0.0439 - accuracy: 1.0000 - val_loss: 0.7606 - val_accuracy: 0.7500\n",
            "Epoch 104/200\n",
            "1/1 [==============================] - 0s 91ms/step - loss: 0.0380 - accuracy: 1.0000 - val_loss: 0.7578 - val_accuracy: 0.7500\n",
            "Epoch 105/200\n",
            "1/1 [==============================] - 0s 95ms/step - loss: 0.0334 - accuracy: 1.0000 - val_loss: 0.7551 - val_accuracy: 0.7500\n",
            "Epoch 106/200\n",
            "1/1 [==============================] - 0s 105ms/step - loss: 0.0503 - accuracy: 1.0000 - val_loss: 0.7526 - val_accuracy: 0.7500\n",
            "Epoch 107/200\n",
            "1/1 [==============================] - 0s 92ms/step - loss: 0.0640 - accuracy: 1.0000 - val_loss: 0.7500 - val_accuracy: 0.7500\n",
            "Epoch 108/200\n",
            "1/1 [==============================] - 0s 120ms/step - loss: 0.1264 - accuracy: 1.0000 - val_loss: 0.7467 - val_accuracy: 0.7500\n",
            "Epoch 109/200\n",
            "1/1 [==============================] - 0s 104ms/step - loss: 0.0957 - accuracy: 1.0000 - val_loss: 0.7445 - val_accuracy: 0.7500\n",
            "Epoch 110/200\n",
            "1/1 [==============================] - 0s 94ms/step - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.7418 - val_accuracy: 0.7500\n",
            "Epoch 111/200\n",
            "1/1 [==============================] - 0s 83ms/step - loss: 0.0648 - accuracy: 1.0000 - val_loss: 0.7395 - val_accuracy: 0.7500\n",
            "Epoch 112/200\n",
            "1/1 [==============================] - 0s 89ms/step - loss: 0.0427 - accuracy: 1.0000 - val_loss: 0.7372 - val_accuracy: 0.7500\n",
            "Epoch 113/200\n",
            "1/1 [==============================] - 0s 112ms/step - loss: 0.0584 - accuracy: 1.0000 - val_loss: 0.7343 - val_accuracy: 0.7500\n",
            "Epoch 114/200\n",
            "1/1 [==============================] - 0s 89ms/step - loss: 0.0575 - accuracy: 1.0000 - val_loss: 0.7307 - val_accuracy: 0.7500\n",
            "Epoch 115/200\n",
            "1/1 [==============================] - 0s 90ms/step - loss: 0.0367 - accuracy: 1.0000 - val_loss: 0.7286 - val_accuracy: 0.7500\n",
            "Epoch 116/200\n",
            "1/1 [==============================] - 0s 108ms/step - loss: 0.0576 - accuracy: 1.0000 - val_loss: 0.7258 - val_accuracy: 0.7500\n",
            "Epoch 117/200\n",
            "1/1 [==============================] - 0s 112ms/step - loss: 0.0322 - accuracy: 1.0000 - val_loss: 0.7233 - val_accuracy: 0.7500\n",
            "Epoch 118/200\n",
            "1/1 [==============================] - 0s 111ms/step - loss: 0.0612 - accuracy: 1.0000 - val_loss: 0.7198 - val_accuracy: 0.7500\n",
            "Epoch 119/200\n",
            "1/1 [==============================] - 0s 93ms/step - loss: 0.0357 - accuracy: 1.0000 - val_loss: 0.7174 - val_accuracy: 0.7500\n",
            "Epoch 120/200\n",
            "1/1 [==============================] - 0s 102ms/step - loss: 0.0560 - accuracy: 1.0000 - val_loss: 0.7139 - val_accuracy: 0.7500\n",
            "Epoch 121/200\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.0420 - accuracy: 1.0000 - val_loss: 0.7112 - val_accuracy: 0.7500\n",
            "Epoch 122/200\n",
            "1/1 [==============================] - 0s 99ms/step - loss: 0.0533 - accuracy: 1.0000 - val_loss: 0.7091 - val_accuracy: 0.7500\n",
            "Epoch 123/200\n",
            "1/1 [==============================] - 0s 100ms/step - loss: 0.0630 - accuracy: 1.0000 - val_loss: 0.7062 - val_accuracy: 0.7500\n",
            "Epoch 124/200\n",
            "1/1 [==============================] - 0s 107ms/step - loss: 0.0416 - accuracy: 1.0000 - val_loss: 0.7031 - val_accuracy: 0.7500\n",
            "Epoch 125/200\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.7009 - val_accuracy: 0.7500\n",
            "Epoch 126/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0365 - accuracy: 1.0000 - val_loss: 0.6995 - val_accuracy: 0.7500\n",
            "Epoch 127/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0539 - accuracy: 1.0000 - val_loss: 0.6984 - val_accuracy: 0.7500\n",
            "Epoch 128/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0393 - accuracy: 1.0000 - val_loss: 0.6966 - val_accuracy: 0.7500\n",
            "Epoch 129/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0491 - accuracy: 1.0000 - val_loss: 0.6946 - val_accuracy: 0.7500\n",
            "Epoch 130/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0459 - accuracy: 1.0000 - val_loss: 0.6934 - val_accuracy: 0.7500\n",
            "Epoch 131/200\n",
            "1/1 [==============================] - 0s 79ms/step - loss: 0.0504 - accuracy: 1.0000 - val_loss: 0.6932 - val_accuracy: 0.7500\n",
            "Epoch 132/200\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0264 - accuracy: 1.0000 - val_loss: 0.6919 - val_accuracy: 0.7500\n",
            "Epoch 133/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0582 - accuracy: 1.0000 - val_loss: 0.6901 - val_accuracy: 0.7500\n",
            "Epoch 134/200\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0383 - accuracy: 1.0000 - val_loss: 0.6880 - val_accuracy: 0.7500\n",
            "Epoch 135/200\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.6852 - val_accuracy: 0.7500\n",
            "Epoch 136/200\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.0952 - accuracy: 1.0000 - val_loss: 0.6828 - val_accuracy: 0.7500\n",
            "Epoch 137/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0299 - accuracy: 1.0000 - val_loss: 0.6810 - val_accuracy: 0.7500\n",
            "Epoch 138/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0346 - accuracy: 1.0000 - val_loss: 0.6791 - val_accuracy: 0.7500\n",
            "Epoch 139/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0264 - accuracy: 1.0000 - val_loss: 0.6778 - val_accuracy: 0.7500\n",
            "Epoch 140/200\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.6772 - val_accuracy: 0.7500\n",
            "Epoch 141/200\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0388 - accuracy: 1.0000 - val_loss: 0.6760 - val_accuracy: 0.7500\n",
            "Epoch 142/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0692 - accuracy: 1.0000 - val_loss: 0.6749 - val_accuracy: 0.7500\n",
            "Epoch 143/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0354 - accuracy: 1.0000 - val_loss: 0.6740 - val_accuracy: 0.7500\n",
            "Epoch 144/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0601 - accuracy: 1.0000 - val_loss: 0.6724 - val_accuracy: 0.7500\n",
            "Epoch 145/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0285 - accuracy: 1.0000 - val_loss: 0.6720 - val_accuracy: 0.7500\n",
            "Epoch 146/200\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 0.0358 - accuracy: 1.0000 - val_loss: 0.6720 - val_accuracy: 0.7500\n",
            "Epoch 147/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0159 - accuracy: 1.0000 - val_loss: 0.6721 - val_accuracy: 0.7500\n",
            "Epoch 148/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0374 - accuracy: 1.0000 - val_loss: 0.6714 - val_accuracy: 0.7500\n",
            "Epoch 149/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0879 - accuracy: 1.0000 - val_loss: 0.6705 - val_accuracy: 0.7500\n",
            "Epoch 150/200\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0342 - accuracy: 1.0000 - val_loss: 0.6701 - val_accuracy: 0.7500\n",
            "Epoch 151/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0577 - accuracy: 1.0000 - val_loss: 0.6678 - val_accuracy: 0.7500\n",
            "Epoch 152/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.6666 - val_accuracy: 0.7500\n",
            "Epoch 153/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0350 - accuracy: 1.0000 - val_loss: 0.6665 - val_accuracy: 0.7500\n",
            "Epoch 154/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0625 - accuracy: 1.0000 - val_loss: 0.6656 - val_accuracy: 0.7500\n",
            "Epoch 155/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0244 - accuracy: 1.0000 - val_loss: 0.6655 - val_accuracy: 0.7500\n",
            "Epoch 156/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0170 - accuracy: 1.0000 - val_loss: 0.6649 - val_accuracy: 0.7500\n",
            "Epoch 157/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0336 - accuracy: 1.0000 - val_loss: 0.6633 - val_accuracy: 0.7500\n",
            "Epoch 158/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.6617 - val_accuracy: 0.7500\n",
            "Epoch 159/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.6615 - val_accuracy: 0.7500\n",
            "Epoch 160/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0227 - accuracy: 1.0000 - val_loss: 0.6590 - val_accuracy: 0.7500\n",
            "Epoch 161/200\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.0324 - accuracy: 1.0000 - val_loss: 0.6590 - val_accuracy: 0.7500\n",
            "Epoch 162/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 0.6586 - val_accuracy: 0.7500\n",
            "Epoch 163/200\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0318 - accuracy: 1.0000 - val_loss: 0.6587 - val_accuracy: 0.7500\n",
            "Epoch 164/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0142 - accuracy: 1.0000 - val_loss: 0.6581 - val_accuracy: 0.7500\n",
            "Epoch 165/200\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.0296 - accuracy: 1.0000 - val_loss: 0.6565 - val_accuracy: 0.7500\n",
            "Epoch 166/200\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0192 - accuracy: 1.0000 - val_loss: 0.6551 - val_accuracy: 0.7500\n",
            "Epoch 167/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0457 - accuracy: 1.0000 - val_loss: 0.6537 - val_accuracy: 0.7500\n",
            "Epoch 168/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0252 - accuracy: 1.0000 - val_loss: 0.6543 - val_accuracy: 0.7500\n",
            "Epoch 169/200\n",
            "1/1 [==============================] - 0s 82ms/step - loss: 0.0263 - accuracy: 1.0000 - val_loss: 0.6531 - val_accuracy: 0.7500\n",
            "Epoch 170/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0175 - accuracy: 1.0000 - val_loss: 0.6532 - val_accuracy: 0.7500\n",
            "Epoch 171/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0241 - accuracy: 1.0000 - val_loss: 0.6527 - val_accuracy: 0.7500\n",
            "Epoch 172/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0540 - accuracy: 1.0000 - val_loss: 0.6522 - val_accuracy: 0.7500\n",
            "Epoch 173/200\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0198 - accuracy: 1.0000 - val_loss: 0.6525 - val_accuracy: 0.7500\n",
            "Epoch 174/200\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0208 - accuracy: 1.0000 - val_loss: 0.6519 - val_accuracy: 0.7500\n",
            "Epoch 175/200\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.6513 - val_accuracy: 0.7500\n",
            "Epoch 176/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0320 - accuracy: 1.0000 - val_loss: 0.6514 - val_accuracy: 0.7500\n",
            "Epoch 177/200\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0255 - accuracy: 1.0000 - val_loss: 0.6518 - val_accuracy: 0.7500\n",
            "Epoch 178/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0312 - accuracy: 1.0000 - val_loss: 0.6506 - val_accuracy: 0.7500\n",
            "Epoch 179/200\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.6501 - val_accuracy: 0.7500\n",
            "Epoch 180/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0315 - accuracy: 1.0000 - val_loss: 0.6495 - val_accuracy: 0.7500\n",
            "Epoch 181/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.6486 - val_accuracy: 0.7500\n",
            "Epoch 182/200\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 0.0306 - accuracy: 1.0000 - val_loss: 0.6471 - val_accuracy: 0.7500\n",
            "Epoch 183/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0241 - accuracy: 1.0000 - val_loss: 0.6475 - val_accuracy: 0.7500\n",
            "Epoch 184/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0407 - accuracy: 1.0000 - val_loss: 0.6468 - val_accuracy: 0.7500\n",
            "Epoch 185/200\n",
            "1/1 [==============================] - 0s 79ms/step - loss: 0.0588 - accuracy: 1.0000 - val_loss: 0.6470 - val_accuracy: 0.7500\n",
            "Epoch 186/200\n",
            "1/1 [==============================] - 0s 78ms/step - loss: 0.0205 - accuracy: 1.0000 - val_loss: 0.6469 - val_accuracy: 0.7500\n",
            "Epoch 187/200\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0194 - accuracy: 1.0000 - val_loss: 0.6471 - val_accuracy: 0.7500\n",
            "Epoch 188/200\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.6446 - val_accuracy: 0.7500\n",
            "Epoch 189/200\n",
            "1/1 [==============================] - 0s 83ms/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 0.6436 - val_accuracy: 0.7500\n",
            "Epoch 190/200\n",
            "1/1 [==============================] - 0s 75ms/step - loss: 0.0162 - accuracy: 1.0000 - val_loss: 0.6434 - val_accuracy: 0.7500\n",
            "Epoch 191/200\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 0.0238 - accuracy: 1.0000 - val_loss: 0.6427 - val_accuracy: 0.7500\n",
            "Epoch 192/200\n",
            "1/1 [==============================] - 0s 77ms/step - loss: 0.0187 - accuracy: 1.0000 - val_loss: 0.6424 - val_accuracy: 0.7500\n",
            "Epoch 193/200\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.6428 - val_accuracy: 0.7500\n",
            "Epoch 194/200\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0137 - accuracy: 1.0000 - val_loss: 0.6441 - val_accuracy: 0.7500\n",
            "Epoch 195/200\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.6438 - val_accuracy: 0.7500\n",
            "Epoch 196/200\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0135 - accuracy: 1.0000 - val_loss: 0.6433 - val_accuracy: 0.7500\n",
            "Epoch 197/200\n",
            "1/1 [==============================] - 0s 82ms/step - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.6423 - val_accuracy: 0.7500\n",
            "Epoch 198/200\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.6438 - val_accuracy: 0.7500\n",
            "Epoch 199/200\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0139 - accuracy: 1.0000 - val_loss: 0.6442 - val_accuracy: 0.7500\n",
            "Epoch 200/200\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.6451 - val_accuracy: 0.7500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model_history(model_history)"
      ],
      "metadata": {
        "id": "KhOL8wcYstJq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "outputId": "6413786a-a085-49a7-ed36-95ae4d034075"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"lab15_model.h5\")"
      ],
      "metadata": {
        "id": "5Sncq7IUCIaa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the saved model\n",
        "path = Path(\"/content/drive/MyDrive/Colab Notebooks/AI/Other files/lab15_model.h5\")\n",
        "model = load_model(path)"
      ],
      "metadata": {
        "id": "SznYvPZLn6tj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query(\"Привет\", cleared_list_vocabulary_words, \n",
        "      list_tags, my_intents, show_details=True)"
      ],
      "metadata": {
        "id": "NN97-ucxLYxJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c5d0d3b-9fc8-4fcb-a690-b325d2ee32a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bag_words for the text: [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]]\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "tag: приветствие\n",
            "Здравствуйте, спасибо за визит\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query(\"Как дела\", cleared_list_vocabulary_words, \n",
        "      list_tags, my_intents, show_details=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HY0vQ4oKXQEK",
        "outputId": "5789f664-3eb4-42a6-8f85-da1708c7b224"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bag_words for the text: [[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "tag: приветствие\n",
            "Рад вас снова видеть\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query(\"Где я могу найти уроки?\", cleared_list_vocabulary_words, \n",
        "      list_tags, my_intents, show_details=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCePefIkWbzh",
        "outputId": "54ec4c6f-92fa-4a13-c62d-ea837102cb83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bag_words for the text: [[0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]]\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "tag: уроки\n",
            "Вы можете просмотреть все имеющиеся на сайте уроки в разделе \"Каталог уроков\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query(\"Спасибо\", cleared_list_vocabulary_words, \n",
        "      list_tags, my_intents, show_details=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WaBUqCo5W8XM",
        "outputId": "2096a412-3d5d-4638-ab7f-a9e39c749d25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bag_words for the text: [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "tag: спасибо\n",
            "Рады помочь!\n"
          ]
        }
      ]
    }
  ]
}