5_lstm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "AIM: To perform intent classification using LSTM (vanilla, stacked, and bidirectional)."
      ],
      "metadata": {
        "id": "i2fv8bOaoXtW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u-fY0v8Onsb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33878bf0-35d6-4b80-e436-1d87b3623169"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[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",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "# import libraries\n",
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "import string\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mount google drive and connect working directory\n",
        "drive.mount('/content/gdrive', force_remount = True)\n",
        "print (sorted(os.listdir('gdrive/My Drive/Colab Helper/ICData/Dataset')))\n",
        "os.chdir('gdrive/My Drive/Colab Helper/ICData/Dataset') # Connect to the directory"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uL4CPprgorGN",
        "outputId": "84927594-b03d-41d5-ae83-4bfc79361cb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "['data_file.csv', 'glove.6B.100d.txt', 'glove.6B.50d.txt', 'model', 'saved_model', 'test_.csv', 'train_.csv', 'valid_.csv']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read train file and print it's shape\n",
        "traindf = pd.read_csv('train_.csv', usecols=['text','intent'])\n",
        "testdf = pd.read_csv('test_.csv', usecols=['text','intent'])\n",
        "validdf = pd.read_csv('valid_.csv', usecols=['text','intent'])\n",
        "\n",
        "print('Train Shape: ', traindf.shape)\n",
        "print('Test Shape: ', testdf.shape)\n",
        "print('Valid Shape: ', validdf.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbRPziQBosdj",
        "outputId": "7c8c4fe0-358b-4202-9653-bd7224dbec9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Shape:  (9951, 2)\n",
            "Test Shape:  (1280, 2)\n",
            "Valid Shape:  (2985, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation:\n",
        "\n",
        "1.   No NULL value present."
      ],
      "metadata": {
        "id": "M8tlwyUvowd-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NULL value presence check\n",
        "print('NULL values: ', traindf['text'].isnull().sum())"
      ],
      "metadata": {
        "id": "2y0KcCuiovtk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "524e5f09-6144-4414-efc9-57676d34c13c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NULL values:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation:\n",
        "\n",
        "1.   Texts may not require a lot of text cleaning."
      ],
      "metadata": {
        "id": "4aNponEIo1m6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# display first 10 rows\n",
        "print('First 10 rows:\\n', traindf.head(10))"
      ],
      "metadata": {
        "id": "C49vJGUMo1OD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b93957f-fb3a-47f1-b7e6-f6c8afe14779"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 10 rows:\n",
            "                                                 text              intent\n",
            "0                          find under night in-birth  SearchCreativeWork\n",
            "1         book a spot in town in the same area as me      BookRestaurant\n",
            "2                                         fimd glory  SearchCreativeWork\n",
            "3       i need a restaurant on july 18th in rose bud      BookRestaurant\n",
            "4  book a spot for 4 in oklahoma at south street ...      BookRestaurant\n",
            "5  i need to know what the weather will be like i...          GetWeather\n",
            "6  book a bistro in new zealand in 119 and a half...      BookRestaurant\n",
            "7           need a table for nine in east prairie la      BookRestaurant\n",
            "8  book a table for mindy and angelita at a resta...      BookRestaurant\n",
            "9  find me a table for two at seven somewhere in ...      BookRestaurant\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Text Processing**\n",
        "\n",
        "**Text Standardization, Tokenization, and Vectorization** (using tf.keras.layers.TextVectorization layer)\n",
        "\n",
        "1.   Convert to lowercase. ✅\n",
        "2.   Keep alphanumeric (remove punctuations if any). ✅\n",
        "3.   Remove stopwords. ✅\n",
        "4.   Perform stemming/lemmatization. ❌"
      ],
      "metadata": {
        "id": "-4a1icJAo_X0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to perform standardization\n",
        "@tf.keras.utils.register_keras_serializable() # if this line is not present; then the exported model will raise error while performing predictions\n",
        "def custom_standardization(input_data):\n",
        "  lowercase = tf.strings.lower(input_data)\n",
        "  remove_punctuation = tf.strings.regex_replace(lowercase, '[%s]' % re.escape(string.punctuation), '')\n",
        "  remove_stopwords = tf.strings.regex_replace(remove_punctuation, r'\\b(' + r'|'.join(stopwords.words('english')) + r')\\b\\s*', '')\n",
        "  return remove_stopwords"
      ],
      "metadata": {
        "id": "yEdjNB951Y38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating TextVectorization layer\n",
        "max_features = 10000\n",
        "sequence_length = 65\n",
        "\n",
        "vectorize_layer = layers.TextVectorization(\n",
        "    standardize=custom_standardization,\n",
        "    max_tokens=max_features,\n",
        "    output_mode='int',\n",
        "    output_sequence_length=sequence_length)"
      ],
      "metadata": {
        "id": "nI5KBixoWGbo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calling adapt on a text-only dataset (without labels) of training data\n",
        "raw_train_ds = tf.data.Dataset.from_tensor_slices(traindf['text']).batch(64)\n",
        "vectorize_layer.adapt(raw_train_ds)"
      ],
      "metadata": {
        "id": "526Ge1M9z7L8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get vocabulary size\n",
        "voc = vectorize_layer.get_vocabulary()\n",
        "print('Vocabulary size: {}'.format(len(voc)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcKQ_uGeIeCF",
        "outputId": "3409bcf3-0b4f-4211-c7b2-344acedfd0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocabulary size: 9438\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Process Inputs**"
      ],
      "metadata": {
        "id": "SbFO_bqsdnyu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# label encode\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "label_encoder.fit(traindf['intent']) # fit label encoder\n",
        "print('Label Encoder Classes: ', label_encoder.classes_)\n",
        "\n",
        "# transform labels\n",
        "traindf['intent'] = label_encoder.transform(traindf['intent'])\n",
        "testdf['intent'] = label_encoder.transform(testdf['intent'])\n",
        "validdf['intent'] = label_encoder.transform(validdf['intent'])"
      ],
      "metadata": {
        "id": "MC_W0lhd4EcK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "755f644f-b65e-4c5c-84a6-4a28fb28303e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Encoder Classes:  ['AddToPlaylist' 'BookRestaurant' 'GetWeather' 'PlayMusic' 'RateBook'\n",
            " 'SearchCreativeWork' 'SearchScreeningEvent']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert model input data to arrays\n",
        "x_train = np.array([np.array(val) for val in traindf['text']])\n",
        "x_val = np.array([np.array(val) for val in validdf['text']])\n",
        "\n",
        "y_train = np.array([np.array(val) for val in traindf['intent']])\n",
        "y_val = np.array([np.array(val) for val in validdf['intent']])"
      ],
      "metadata": {
        "id": "3agWaghCEeCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**MLP Training**\n",
        "\n",
        "Model 1"
      ],
      "metadata": {
        "id": "DjFmAMQxbJc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model definition\n",
        "num_tokens = len(voc) + 2\n",
        "embedding_dim = 8\n",
        "\n",
        "model1 = tf.keras.Sequential([\n",
        "  vectorize_layer,\n",
        "  layers.Embedding(num_tokens, embedding_dim, mask_zero=True, name='Embedding_1'), # mask_zero set to True\n",
        "  layers.LSTM(12, name='LSTM_1'),\n",
        "  layers.Dense(8, activation='relu', name='Dense_1'),\n",
        "  layers.Dropout(0.2, name='Dropout_1'),\n",
        "  layers.Dense(7, activation='softmax', name='Dense_2')])\n",
        "\n",
        "model1._name = 'Modelwith1LSTMLayer'\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFiI-qTxvz0l",
        "outputId": "3f910938-8edc-4f5b-8994-b656dd3e59a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Modelwith1LSTMLayer\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " text_vectorization (TextVec  (None, 65)               0         \n",
            " torization)                                                     \n",
            "                                                                 \n",
            " Embedding_1 (Embedding)     (None, 65, 8)             75520     \n",
            "                                                                 \n",
            " LSTM_1 (LSTM)               (None, 12)                1008      \n",
            "                                                                 \n",
            " Dense_1 (Dense)             (None, 8)                 104       \n",
            "                                                                 \n",
            " Dropout_1 (Dropout)         (None, 8)                 0         \n",
            "                                                                 \n",
            " Dense_2 (Dense)             (None, 7)                 63        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 76,695\n",
            "Trainable params: 76,695\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model compiling\n",
        "model1.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics='accuracy')\n",
        "\n",
        "# model training\n",
        "epochs = 20\n",
        "%time history = model1.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTcA1h9GwUzr",
        "outputId": "05e2854a-ebbe-4631-d56e-df3308e6251a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "311/311 [==============================] - 24s 48ms/step - loss: 1.3660 - accuracy: 0.4848 - val_loss: 0.6053 - val_accuracy: 0.9283\n",
            "Epoch 2/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.5141 - accuracy: 0.8397 - val_loss: 0.2186 - val_accuracy: 0.9621\n",
            "Epoch 3/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.3113 - accuracy: 0.9004 - val_loss: 0.1560 - val_accuracy: 0.9685\n",
            "Epoch 4/20\n",
            "311/311 [==============================] - 16s 50ms/step - loss: 0.2308 - accuracy: 0.9228 - val_loss: 0.1523 - val_accuracy: 0.9705\n",
            "Epoch 5/20\n",
            "311/311 [==============================] - 14s 45ms/step - loss: 0.1991 - accuracy: 0.9308 - val_loss: 0.1519 - val_accuracy: 0.9682\n",
            "Epoch 6/20\n",
            "311/311 [==============================] - 15s 47ms/step - loss: 0.1742 - accuracy: 0.9348 - val_loss: 0.1619 - val_accuracy: 0.9662\n",
            "Epoch 7/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.1572 - accuracy: 0.9395 - val_loss: 0.1774 - val_accuracy: 0.9682\n",
            "Epoch 8/20\n",
            "311/311 [==============================] - 14s 44ms/step - loss: 0.1410 - accuracy: 0.9459 - val_loss: 0.1829 - val_accuracy: 0.9685\n",
            "Epoch 9/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.1375 - accuracy: 0.9485 - val_loss: 0.1955 - val_accuracy: 0.9678\n",
            "Epoch 10/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.1300 - accuracy: 0.9512 - val_loss: 0.2085 - val_accuracy: 0.9658\n",
            "Epoch 11/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.1229 - accuracy: 0.9555 - val_loss: 0.1941 - val_accuracy: 0.9688\n",
            "Epoch 12/20\n",
            "311/311 [==============================] - 13s 43ms/step - loss: 0.1207 - accuracy: 0.9565 - val_loss: 0.2008 - val_accuracy: 0.9682\n",
            "Epoch 13/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.1172 - accuracy: 0.9582 - val_loss: 0.2131 - val_accuracy: 0.9665\n",
            "Epoch 14/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.1040 - accuracy: 0.9632 - val_loss: 0.2338 - val_accuracy: 0.9652\n",
            "Epoch 15/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.0999 - accuracy: 0.9639 - val_loss: 0.2293 - val_accuracy: 0.9685\n",
            "Epoch 16/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.0953 - accuracy: 0.9632 - val_loss: 0.2518 - val_accuracy: 0.9652\n",
            "Epoch 17/20\n",
            "311/311 [==============================] - 17s 55ms/step - loss: 0.0917 - accuracy: 0.9652 - val_loss: 0.2703 - val_accuracy: 0.9655\n",
            "Epoch 18/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.0925 - accuracy: 0.9662 - val_loss: 0.3039 - val_accuracy: 0.9635\n",
            "Epoch 19/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.0864 - accuracy: 0.9717 - val_loss: 0.3094 - val_accuracy: 0.9631\n",
            "Epoch 20/20\n",
            "311/311 [==============================] - 13s 42ms/step - loss: 0.0901 - accuracy: 0.9677 - val_loss: 0.3097 - val_accuracy: 0.9658\n",
            "CPU times: user 7min 16s, sys: 21.5 s, total: 7min 37s\n",
            "Wall time: 5min 31s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the graphs\n",
        "history_dict = history.history # dictionary of result values\n",
        "epochs = range(1, len(history_dict['accuracy']) + 1) # number of epochs\n",
        "\n",
        "# plot for loss\n",
        "plt.plot(epochs, history_dict['loss'], 'bo', label='Training loss')\n",
        "plt.plot(epochs, history_dict['val_loss'], 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# plot for accuracy\n",
        "plt.plot(epochs, history_dict['accuracy'], 'bo', label='Training acc')\n",
        "plt.plot(epochs, history_dict['val_accuracy'], 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "VA8DK2ENzwND",
        "outputId": "4c92162f-a48e-4c05-8769-7a9c7b469cda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model 2"
      ],
      "metadata": {
        "id": "opkuqK5UxACp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# model definition\n",
        "num_tokens = len(voc) + 2\n",
        "embedding_dim = 8\n",
        "\n",
        "model2 = tf.keras.Sequential([\n",
        "  vectorize_layer,\n",
        "  layers.Embedding(num_tokens, embedding_dim, mask_zero=True, name='Embedding_1'), # mask_zero set to True\n",
        "  layers.Bidirectional(tf.keras.layers.LSTM(12), name='Bidirectional_1'),\n",
        "  layers.Dense(8, activation='relu', name='Dense_1'),\n",
        "  layers.Dropout(0.2, name='Dropout_1'),\n",
        "  layers.Dense(7, activation='softmax', name='Dense_2')])\n",
        "\n",
        "model2._name = 'Modelwith1BidirectionalLayer'\n",
        "model2.summary()"
      ],
      "metadata": {
        "id": "-tiAKnUJJhlQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "789f1983-d134-45f3-ede4-fe1046f97ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Modelwith1BidirectionalLayer\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " text_vectorization (TextVec  (None, 65)               0         \n",
            " torization)                                                     \n",
            "                                                                 \n",
            " Embedding_1 (Embedding)     (None, 65, 8)             75520     \n",
            "                                                                 \n",
            " Bidirectional_1 (Bidirectio  (None, 24)               2016      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " Dense_1 (Dense)             (None, 8)                 200       \n",
            "                                                                 \n",
            " Dropout_1 (Dropout)         (None, 8)                 0         \n",
            "                                                                 \n",
            " Dense_2 (Dense)             (None, 7)                 63        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 77,799\n",
            "Trainable params: 77,799\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model compiling\n",
        "model2.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics='accuracy')\n",
        "\n",
        "# model training\n",
        "epochs = 20\n",
        "%time history = model2.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=epochs)"
      ],
      "metadata": {
        "id": "VvfM-Kh6bdFx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d21e2822-30d1-43c9-8dda-7040d072c20f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "311/311 [==============================] - 32s 77ms/step - loss: 1.4919 - accuracy: 0.4047 - val_loss: 0.8255 - val_accuracy: 0.7765\n",
            "Epoch 2/20\n",
            "311/311 [==============================] - 20s 66ms/step - loss: 0.6760 - accuracy: 0.7791 - val_loss: 0.3112 - val_accuracy: 0.9524\n",
            "Epoch 3/20\n",
            "311/311 [==============================] - 21s 66ms/step - loss: 0.3530 - accuracy: 0.8854 - val_loss: 0.1535 - val_accuracy: 0.9702\n",
            "Epoch 4/20\n",
            "311/311 [==============================] - 21s 67ms/step - loss: 0.2702 - accuracy: 0.9049 - val_loss: 0.1335 - val_accuracy: 0.9685\n",
            "Epoch 5/20\n",
            "311/311 [==============================] - 21s 66ms/step - loss: 0.2291 - accuracy: 0.9108 - val_loss: 0.1249 - val_accuracy: 0.9672\n",
            "Epoch 6/20\n",
            "311/311 [==============================] - 21s 66ms/step - loss: 0.1974 - accuracy: 0.9203 - val_loss: 0.1209 - val_accuracy: 0.9672\n",
            "Epoch 7/20\n",
            "311/311 [==============================] - 25s 81ms/step - loss: 0.1832 - accuracy: 0.9234 - val_loss: 0.1207 - val_accuracy: 0.9662\n",
            "Epoch 8/20\n",
            "311/311 [==============================] - 21s 68ms/step - loss: 0.1674 - accuracy: 0.9305 - val_loss: 0.1115 - val_accuracy: 0.9685\n",
            "Epoch 9/20\n",
            "311/311 [==============================] - 21s 69ms/step - loss: 0.1553 - accuracy: 0.9379 - val_loss: 0.1263 - val_accuracy: 0.9662\n",
            "Epoch 10/20\n",
            "311/311 [==============================] - 21s 69ms/step - loss: 0.1474 - accuracy: 0.9457 - val_loss: 0.1300 - val_accuracy: 0.9665\n",
            "Epoch 11/20\n",
            "311/311 [==============================] - 21s 68ms/step - loss: 0.1434 - accuracy: 0.9510 - val_loss: 0.1451 - val_accuracy: 0.9642\n",
            "Epoch 12/20\n",
            "311/311 [==============================] - 21s 69ms/step - loss: 0.1334 - accuracy: 0.9595 - val_loss: 0.1429 - val_accuracy: 0.9642\n",
            "Epoch 13/20\n",
            "311/311 [==============================] - 21s 68ms/step - loss: 0.1318 - accuracy: 0.9592 - val_loss: 0.1314 - val_accuracy: 0.9665\n",
            "Epoch 14/20\n",
            "311/311 [==============================] - 21s 68ms/step - loss: 0.1271 - accuracy: 0.9589 - val_loss: 0.1452 - val_accuracy: 0.9662\n",
            "Epoch 15/20\n",
            "311/311 [==============================] - 21s 66ms/step - loss: 0.1185 - accuracy: 0.9614 - val_loss: 0.1432 - val_accuracy: 0.9645\n",
            "Epoch 16/20\n",
            "311/311 [==============================] - 21s 67ms/step - loss: 0.1170 - accuracy: 0.9618 - val_loss: 0.1390 - val_accuracy: 0.9662\n",
            "Epoch 17/20\n",
            "311/311 [==============================] - 21s 66ms/step - loss: 0.1113 - accuracy: 0.9638 - val_loss: 0.1401 - val_accuracy: 0.9668\n",
            "Epoch 18/20\n",
            "311/311 [==============================] - 22s 70ms/step - loss: 0.1117 - accuracy: 0.9626 - val_loss: 0.1575 - val_accuracy: 0.9668\n",
            "Epoch 19/20\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.1082 - accuracy: 0.9630 - val_loss: 0.1513 - val_accuracy: 0.9678\n",
            "Epoch 20/20\n",
            "311/311 [==============================] - 21s 67ms/step - loss: 0.1004 - accuracy: 0.9654 - val_loss: 0.1602 - val_accuracy: 0.9672\n",
            "CPU times: user 12min 18s, sys: 7.32 s, total: 12min 25s\n",
            "Wall time: 7min 29s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the graphs\n",
        "history_dict = history.history # dictionary of result values\n",
        "epochs = range(1, len(history_dict['accuracy']) + 1) # number of epochs\n",
        "\n",
        "# plot for loss\n",
        "plt.plot(epochs, history_dict['loss'], 'bo', label='Training loss')\n",
        "plt.plot(epochs, history_dict['val_loss'], 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# plot for accuracy\n",
        "plt.plot(epochs, history_dict['accuracy'], 'bo', label='Training acc')\n",
        "plt.plot(epochs, history_dict['val_accuracy'], 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "xALkGV6Pznr5",
        "outputId": "7c9e0c98-cdd4-45cd-ed48-c712fb196747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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BtbXwxBMtWxpozu2foDN6EYlXnIlgNnCImfUzsw7A+cC09AnMrIeZpWL4HnBPjPHkZc4cWLOm5RJBcx/oEhGJW2yJwN1rgSuAx4BXgQfdfYGZjTezEdFkw4BFZvY6sC9Q9HtZUtcHTjmlZZbX3Ns/RUTiZu5e7Bgapby83Kurq2Nb/umnw7//DfPnt8zy2rULJYFMZqGqR0SkEMzsRXcvzzau2BeLW5VNm+Dpp1v2+kBLPNAlIhInJYI0zz4bkkFLJgI90CUirZ0SQZqqqtDS6EkntdwydfuniLR2anQuzYwZcOyxsMceLbtcNbomIq2ZSgSRNWvghRdax9PEIiKFpEQQmTULtm4NDc2JiCSJEkFkxgwoLYWhQ4sdiYhIYSkRRKqq4IQTQjIQEUkSJQK2P0Cm6wMikkRKBMDMmeFb1wdEJImUCAjXB/bYAwYPLnYkIiKFp0RAuD4wbBi011MVIpJAiU8ES5bAm2+qWkhEkivxiWBG9MZkXSgWkaRKfCKoqoJ994Wjjip2JCIixZHoROAeEsGpp4YG4UREkijRieC11+D993V9QESSLdGJIPVaSl0fEJEkS3wi6NcvfEREkiqxiWDrVnjiCVULiYgkNhG89BKsWqVqIRGRxCaC1PUBlQhEJOkSnQj+4z/CMwQiIkmWyETwySfwr3+pNCAiAglNBM89Bxs36vqAiAgkNBFUVUG7dnDyycWORESk+GJNBGY23MwWmdliM7suy/gDzWymmb1kZvPN7Mw440mZMQPKy6Fbt0KsTUSkdYstEZhZCXAncAZwJDDKzI7MmOwG4EF3HwicD/wqrnhS1q2D559XtZCISEqcJYIhwGJ3f8vdNwOTgZEZ0ziwR9TdDXgvxngAmDULamuVCEREUuJMBAcA76b1L42GpRsHfNXMlgLTgW9lW5CZjTGzajOrXrFiRbOCqqqCjh1h6NBmLUZEZJdR7IvFo4D73L03cCZwv5ntFJO7V7p7ubuX9+zZs1krnDEjJIFOnZq1GBGRXUaciWAZ0Cetv3c0LN03gAcB3P1ZoBToEVdAH34Ic+eqWkhEJF2ciWA2cIiZ9TOzDoSLwdMypnkHOA3AzI4gJILm1f3UY+bM8K1EICKyXWyJwN1rgSuAx4BXCXcHLTCz8WY2Iprsv4Fvmtk84A/AaHf3uGKaMQO6dg23jjbGpElQVhaePSgrC/0iIruK9nEu3N2nEy4Cpw/7YVr3QuCEOGNIV1UVHiJr34hfPWkSjBkDGzaE/iVLQj9ARUXLxygiUmjFvlhcMO++C2+80fhqoeuv354EUjZsCMNFRHYFiUkEM2aE78Y2NPfOO40bLiLS1iQmEXTrBmefHZqebowDD2zccBGRtiYxieALX4BHHgkXfBtjwgTo3HnHYZ07h+EiIruCxCSCpqqogMpK6NsXzMJ3ZaUuFIvIriPWu4Z2FRUVOvCLyK5LJQIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRrMBGY2X9me2uYiIjsGvI5wH8FeMPMfm5mh8cdkIiIFFaDicDdvwoMBN4E7jOzZ6OXyXeNPToREYldXlU+7r4G+CMwGegFnAPMMbNvxRibiIgUQD7XCEaY2Z+BJ4DdgCHufgbQn/CqSRERacPyaXTui8Ct7j4rfaC7bzCzb8QTloiIFEo+iWAcsDzVY2adgH3dvcbdq+IKTERECiOfawQPAdvS+rdGw0REZBeQTyJo7+6bUz1Rd4f4QhIRkULKJxGsMLMRqR4zGwl8GF9IIiJSSPlcI7gUmGRmdwAGvAt8LdaoRESkYBpMBO7+JvBpM+sS9a+LPSoRESmYvN5ZbGZnAUcBpWYGgLuPjzEuEREpkHweKPs1ob2hbxGqhr4E9M1n4WY23MwWmdliM7suy/hbzWxu9HndzFY1Mn4REWmmfEoEQ939GDOb7+4/NrNfAI82NJOZlQB3Ap8FlgKzzWyauy9MTePuV6dN/y1Cm0YiIlJA+dw1tCn63mBm+wNbCO0NNWQIsNjd34puOZ0MjKxn+lHAH/JYroiItKB8EsEjZrYncDMwB6gBfp/HfAcQ7jBKWRoN24mZ9QX6ATNyjB9jZtVmVr1ixYo8Vi0iIvmqt2ooeiFNlbuvAh42s78Cpe6+uoXjOB/4o7tvzTbS3SuBSoDy8nJv4XWLiCRavSUCd99GqOdP9X/SiCSwDOiT1t87GpbN+ahaSESkKPKpGqoysy9a6r7R/M0GDjGzfmbWgXCwn5Y5UfTWs72AZxu5fBERaQH5JIJLCI3MfWJma8xsrZmtaWgmd68FrgAeA14FHnT3BWY2Pr3JCkKCmOzuqvIRESkCa2vH3/Lycq+uri52GCIibYqZveju5dnGNfgcgZmdlG145otqRESkbcrngbLvpHWXEp4PeBE4NZaIRESkoPJpdO4/0/vNrA9wW2wRiYhIQeVzsTjTUuCIlg5ERESKI59rBP8LpK4otwMGEJ4wFhGRXUA+1wjSb9GpBf7g7k/HFI+IiBRYPongj8CmVPMPZlZiZp3dfUO8oYmISCHk9WQx0CmtvxPweDzhiIhIoeWTCErTX08ZdXeOLyQRESmkfBLBejMblOoxs8HAxvhCEhGRQsrnGsFVwENm9h7hVZX7EV5dKSIiu4B8HiibHbUQelg0aJG7b4k3LBERKZR8Xl5/ObC7u7/i7q8AXczssvhDExGRQsjnGsE3ozeUAeDuHwPfjC8kEREppHwSQUn6S2nMrAToEF9IIiJSSPlcLP47MMXM/i/qvwR4NL6QRESkkPJJBN8FxgCXRv3zCXcOiYjILqDBqqHoBfbPAzWEdxGcSnj1pIiI7AJylgjM7FBgVPT5EJgC4O6nFCY0EREphPqqhl4DngLOdvfFAGZ2dUGiEhGRgqmvauhcYDkw08x+Y2anEZ4sFhGRXUjORODuU939fOBwYCahqYl9zOwuM/tcoQIUEZF45XOxeL27/z56d3Fv4CXCnUQiIrILaNQ7i939Y3evdPfT4gpIREQKqykvrxcRkV1IrInAzIab2SIzW2xm1+WY5stmttDMFpjZ7+OMR0REdpbPk8VNErVJdCfwWWApMNvMprn7wrRpDgG+B5zg7h+b2T5xxSMiItnFWSIYAix297fcfTMwGRiZMc03gTujFk1x93/HGI+IiGQRZyI4AHg3rX9pNCzdocChZva0mT1nZsNjjEdERLKIrWqoEes/BBhGuDV1lpkdnf7+AwAzG0No+I4DDzyw0DGKiOzS4iwRLAP6pPX3joalWwpMc/ct7v428DohMewgumW13N3Le/bsGVvAIiJJFGcimA0cYmb9zKwDcD4wLWOaqYTSAGbWg1BV9FaMMYmISIbYEoG71wJXAI8Rmq1+0N0XmNl4MxsRTfYYsNLMFhKasfiOu6+MKyYREdmZuXuxY2iU8vJyr66uLnYYIiJtipm96O7l2cbpyWIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhARSbhYE4GZDTezRWa22MyuyzJ+tJmtMLO50ee/4oxHRER21j6uBZtZCXAn8FlgKTDbzKa5+8KMSae4+xVxxSEiIvWLs0QwBFjs7m+5+2ZgMjAyxvWJiEgTxJkIDgDeTetfGg3L9EUzm29mfzSzPjHGIyIiWRT7YvEjQJm7HwP8E5iYbSIzG2Nm1WZWvWLFioIGKCKyq4szESwD0s/we0fD6rj7Snf/JOr9LTA424LcvdLdy929vGfPnrEEKyKSVHEmgtnAIWbWz8w6AOcD09InMLNeab0jgFdjjEdERLKI7a4hd681syuAx4AS4B53X2Bm44Fqd58GXGlmI4Ba4CNgdFzxiIhIdubuxY6hUcrLy726urrYYYiItClm9qK7l2cbF1uJQER2PVu2bGHp0qVs2rSp2KFIDqWlpfTu3Zvddtst73mUCEQkb0uXLqVr166UlZVhZsUORzK4OytXrmTp0qX069cv7/mKffuoiLQhmzZtonv37koCrZSZ0b1790aX2JQIRKRRlARat6b8fZQIREQSTolARGIzaRKUlUG7duF70qTmLW/lypUMGDCAAQMGsN9++3HAAQfU9W/evLneeaurq7nyyisbXMfQoUObF2QbpIvFIhKLSZNgzBjYsCH0L1kS+gEqKpq2zO7duzN37lwAxo0bR5cuXbjmmmvqxtfW1tK+ffbDWnl5OeXlWe+e3MEzzzzTtODaMJUIRCQW11+/PQmkbNgQhrek0aNHc+mll3Lcccdx7bXX8sILL3D88cczcOBAhg4dyqJFiwB44oknOPvss4GQRC6++GKGDRvGQQcdxO233163vC5dutRNP2zYMM477zwOP/xwKioqSD13NX36dA4//HAGDx7MlVdeWbfcdDU1NZx44okMGjSIQYMG7ZBgbrrpJo4++mj69+/PddeFV7UsXryY008/nf79+zNo0CDefPPNlt1Q9VCJQERi8c47jRveHEuXLuWZZ56hpKSENWvW8NRTT9G+fXsef/xxvv/97/Pwww/vNM9rr73GzJkzWbt2LYcddhhjx47d6d77l156iQULFrD//vtzwgkn8PTTT1NeXs4ll1zCrFmz6NevH6NGjcoa0z777MM///lPSktLeeONNxg1ahTV1dU8+uij/OUvf+H555+nc+fOfPTRRwBUVFRw3XXXcc4557Bp0ya2bdvW8hsqByUCEYnFgQeG6qBsw1val770JUpKSgBYvXo1F110EW+88QZmxpYtW7LOc9ZZZ9GxY0c6duzIPvvswwcffEDv3r13mGbIkCF1wwYMGEBNTQ1dunThoIMOqrtPf9SoUVRWVu60/C1btnDFFVcwd+5cSkpKeP311wF4/PHH+frXv07nzp0B2HvvvVm7di3Lli3jnHPOAcJDYYWkqiERicWECRAd6+p07hyGt7Tdd9+9rvsHP/gBp5xyCq+88gqPPPJIznvqO3bsWNddUlJCbW1tk6bJ5dZbb2Xfffdl3rx5VFdXN3gxu5iUCEQkFhUVUFkJffuCWfiurGz6heJ8rV69mgMOCO/Auu+++1p8+YcddhhvvfUWNTU1AEyZMiVnHL169aJdu3bcf//9bN26FYDPfvaz3HvvvWyILqB89NFHdO3ald69ezN16lQAPvnkk7rxhaBEICKxqaiAmhrYti18x50EAK699lq+973vMXDgwEadweerU6dO/OpXv2L48OEMHjyYrl270q1bt52mu+yyy5g4cSL9+/fntddeqyu1DB8+nBEjRlBeXs6AAQO45ZZbALj//vu5/fbbOeaYYxg6dCjvv/9+i8eei1ofFZG8vfrqqxxxxBHFDqPo1q1bR5cuXXB3Lr/8cg455BCuvvrqYodVJ9vfqb7WR1UiEBFppN/85jcMGDCAo446itWrV3PJJZcUO6Rm0V1DIiKNdPXVV7eqEkBzqUQgIpJwSgQiIgmnRCAiknBKBCIiCadEICJtximnnMJjjz22w7DbbruNsWPH5pxn2LBhpG45P/PMM1m1atVO04wbN67ufv5cpk6dysKFC+v6f/jDH/L44483JvxWS4lARNqMUaNGMXny5B2GTZ48OWfDb5mmT5/Onnvu2aR1ZyaC8ePHc/rppzdpWa2Nbh8VkSa56iqIXg3QYgYMgNtuyz3+vPPO44YbbmDz5s106NCBmpoa3nvvPU488UTGjh3L7Nmz2bhxI+eddx4//vGPd5q/rKyM6upqevTowYQJE5g4cSL77LMPffr0YfDgwUB4RqCyspLNmzdz8MEHc//99zN37lymTZvGk08+yU9+8hMefvhhbrzxRs4++2zOO+88qqqquOaaa6itreXYY4/lrrvuomPHjpSVlXHRRRfxyCOPsGXLFh566CEOP/zwHWKqqanhwgsvZP369QDccccddS/Huemmm3jggQdo164dZ5xxBj/72c9YvHgxl156KStWrKCkpISHHnqIT33qU83a7ioRiEibsffeezNkyBAeffd4OxsAAApISURBVPRRIJQGvvzlL2NmTJgwgerqaubPn8+TTz7J/Pnzcy7nxRdfZPLkycydO5fp06cze/bsunHnnnsus2fPZt68eRxxxBHcfffdDB06lBEjRnDzzTczd+7cHQ68mzZtYvTo0UyZMoWXX36Z2tpa7rrrrrrxPXr0YM6cOYwdOzZr9VOqueo5c+YwZcqUureopTdXPW/ePK699logNFd9+eWXM2/ePJ555hl69erVvI2KSgQi0kT1nbnHKVU9NHLkSCZPnszdd98NwIMPPkhlZSW1tbUsX76chQsXcswxx2RdxlNPPcU555xT1xT0iBEj6sa98sor3HDDDaxatYp169bx+c9/vt54Fi1aRL9+/Tj00EMBuOiii7jzzju56qqrgJBYAAYPHsyf/vSnneZvDc1VJ6JE0NLvTRWR4hk5ciRVVVXMmTOHDRs2MHjwYN5++21uueUWqqqqmD9/PmeddVbO5qcbMnr0aO644w5efvllfvSjHzV5OSmppqxzNWPdGpqrjjURmNlwM1tkZovN7Lp6pvuimbmZNfxC0UZKvTd1yRJw3/7eVCUDkbapS5cunHLKKVx88cV1F4nXrFnD7rvvTrdu3fjggw/qqo5yOemkk5g6dSobN25k7dq1PPLII3Xj1q5dS69evdiyZQuT0g4UXbt2Ze3atTst67DDDqOmpobFixcDoRXRk08+Oe/f0xqaq44tEZhZCXAncAZwJDDKzI7MMl1X4NvA83HEUaj3popI4YwaNYp58+bVJYL+/fszcOBADj/8cC644AJOOOGEeucfNGgQX/nKV+jfvz9nnHEGxx57bN24G2+8keOOO44TTjhhhwu7559/PjfffDMDBw7c4X3CpaWl3HvvvXzpS1/i6KOPpl27dlx66aV5/5bW0Fx1bM1Qm9nxwDh3/3zU/z0Ad/9pxnS3Af8EvgNc4+71tjHd2Gao27ULJYGd4wttpItI/tQMddvQmpqhPgB4N61/aTQsPbBBQB93/1tcQeR6P2oc700VEWmLinax2MzaAb8E/juPaceYWbWZVa9YsaJR6ynke1NFRNqiOBPBMqBPWn/vaFhKV+A/gCfMrAb4NDAt2wVjd69093J3L+/Zs2ejgijWe1NFdlVt7a2GSdOUv0+czxHMBg4xs36EBHA+cEFqpLuvBnqk+s3sCfK4RtAUFRU68Iu0hNLSUlauXEn37t0xs2KHIxncnZUrVzb6+YLYEoG715rZFcBjQAlwj7svMLPxQLW7T4tr3SISj969e7N06VIaW0UrhVNaWkrv3r0bNY9eXi8ikgB6eb2IiOSkRCAiknBKBCIiCdfmrhGY2QpgSbHjyKEH8GGxg6iH4mue1h4ftP4YFV/zNCe+vu6e9f77NpcIWjMzq851MaY1UHzN09rjg9Yfo+JrnrjiU9WQiEjCKRGIiCScEkHLqix2AA1QfM3T2uOD1h+j4mueWOLTNQIRkYRTiUBEJOGUCEREEk6JoJHMrI+ZzTSzhWa2wMy+nWWaYWa22szmRp8fFjjGGjN7OVr3Tg0zWXB79C7p+dELggoV22Fp22Wuma0xs6sypin49jOze8zs32b2Stqwvc3sn2b2RvS9V455L4qmecPMLipQbDeb2WvR3+/PZrZnjnnr3RdijnGcmS1L+zuemWPevN5tHkN8U9JiqzGzuTnmjXUb5jqmFHT/c3d9GvEBegGDou6uwOvAkRnTDAP+WsQYa4Ae9Yw/E3gUMMJ7IJ4vUpwlwPuEB12Kuv2Ak4BBwCtpw34OXBd1XwfclGW+vYG3ou+9ou69ChDb54D2UfdN2WLLZ1+IOcZxhKblG9oH3gQOAjoA8zL/n+KKL2P8L4AfFmMb5jqmFHL/U4mgkdx9ubvPibrXAq+S8QrONmAk8DsPngP2NLNeRYjjNOBNdy/6k+LuPgv4KGPwSGBi1D0R+EKWWT8P/NPdP3L3jwnv3x4ed2zu/g93r416nyO8+Klocmy/fAwBFrv7W+6+GZhM2O4tqr74LLxY4cvAH1p6vfmo55hSsP1PiaAZzKwMGAg8n2X08WY2z8weNbOjChoYOPAPM3vRzMZkGd/g+6QL5Hxy//MVc/ul7Ovuy6Pu94F9s0zTGrblxYQSXjYN7QtxuyKqvronR9VGa9h+JwIfuPsbOcYXbBtmHFMKtv8pETSRmXUBHgaucvc1GaPnEKo7+gP/C0wtcHifcfdBwBnA5WZ2UoHX3yAz6wCMAB7KMrrY228nHsrhre5eazO7HqgFJuWYpJj7wl3Ap4ABwHJC9UtrNIr6SwMF2Yb1HVPi3v+UCJrAzHYj/MEmufufMse7+xp3Xxd1Twd2M7MemdPFxd2XRd//Bv5MKH6na+h90oVwBjDH3T/IHFHs7Zfmg1SVWfT97yzTFG1bmtlo4GygIjpQ7CSPfSE27v6Bu291923Ab3Ksu6j7opm1B84FpuSaphDbMMcxpWD7nxJBI0X1iXcDr7r7L3NMs180HWY2hLCdVxYovt3NrGuqm3BR8ZWMyaYBX4vuHvo0sDqtCFooOc/Cirn9MkwDUndhXAT8Jcs0jwGfM7O9oqqPz0XDYmVmw4FrgRHuviHHNPnsC3HGmH7d6Zwc6657t3lUSjyfsN0L5XTgNXdfmm1kIbZhPceUwu1/cV0J31U/wGcIRbT5wNzocyZwKXBpNM0VwALCHRDPAUMLGN9B0XrnRTFcHw1Pj8+AOwl3a7wMlBd4G+5OOLB3SxtW1O1HSErLgS2EetZvAN2BKuAN4HFg72jacuC3afNeDCyOPl8vUGyLCXXDqX3w19G0+wPT69sXCrj97o/2r/mEg1qvzBij/jMJd8q8GVeM2eKLht+X2u/Spi3oNqznmFKw/U9NTIiIJJyqhkREEk6JQEQk4ZQIREQSTolARCThlAhERBJOiUAkYmZbbceWUVusJUwzK0tv+VKkNWlf7ABEWpGN7j6g2EGIFJpKBCINiNqj/3nUJv0LZnZwNLzMzGZEjapVmdmB0fB9LbwjYF70GRotqsTMfhO1Of8PM+sUTX9l1Bb9fDObXKSfKQmmRCCyXaeMqqGvpI1b7e5HA3cAt0XD/heY6O7HEBp9uz0afjvwpIdG8wYRnkgFOAS4092PAlYBX4yGXwcMjJZzaVw/TiQXPVksEjGzde7eJcvwGuBUd38rahzsfXfvbmYfEppN2BINX+7uPcxsBdDb3T9JW0YZod34Q6L+7wK7uftPzOzvwDpCK6tTPWpwT6RQVCIQyY/n6G6MT9K6t7L9Gt1ZhLafBgGzoxYxRQpGiUAkP19J+3426n6G0FomQAXwVNRdBYwFMLMSM+uWa6Fm1g7o4+4zge8C3YCdSiUicdKZh8h2nWzHF5j/3d1Tt5DuZWbzCWf1o6Jh3wLuNbPvACuAr0fDvw1Umtk3CGf+YwktX2ZTAjwQJQsDbnf3VS32i0TyoGsEIg2IrhGUu/uHxY5FJA6qGhIRSTiVCEREEk4lAhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYT7//5Kb2KSzywvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model definition\n",
        "num_tokens = len(voc) + 2\n",
        "embedding_dim = 8\n",
        "\n",
        "model3 = tf.keras.Sequential([\n",
        "  vectorize_layer,\n",
        "  layers.Embedding(num_tokens, embedding_dim, mask_zero=True, name='Embedding_1'), # mask_zero set to True\n",
        "  layers.LSTM(6, return_sequences=True, name='LSTM_1'),\n",
        "  layers.LSTM(4, name='LSTM_2'),\n",
        "  layers.Dense(8, activation='relu', name='Dense_1'),\n",
        "  layers.Dropout(0.2, name='Dropout_1'),\n",
        "  layers.Dense(7, activation='softmax', name='Dense_2')])\n",
        "\n",
        "model3._name = 'Modelwith2LSTMLayers'\n",
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bx3BRx2auBMM",
        "outputId": "0cc5c350-dde7-45b9-88b9-348a0448ee63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Modelwith2LSTMLayers\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " text_vectorization (TextVec  (None, 65)               0         \n",
            " torization)                                                     \n",
            "                                                                 \n",
            " Embedding_1 (Embedding)     (None, 65, 8)             75520     \n",
            "                                                                 \n",
            " LSTM_1 (LSTM)               (None, 65, 6)             360       \n",
            "                                                                 \n",
            " LSTM_2 (LSTM)               (None, 4)                 176       \n",
            "                                                                 \n",
            " Dense_1 (Dense)             (None, 8)                 40        \n",
            "                                                                 \n",
            " Dropout_1 (Dropout)         (None, 8)                 0         \n",
            "                                                                 \n",
            " Dense_2 (Dense)             (None, 7)                 63        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 76,159\n",
            "Trainable params: 76,159\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model compiling\n",
        "model3.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics='accuracy')\n",
        "\n",
        "# model training\n",
        "epochs = 25\n",
        "%time history = model3.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmeRyjNhxnGU",
        "outputId": "14b8c0dd-e900-4749-9169-529c14a50aeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "311/311 [==============================] - 38s 96ms/step - loss: 1.4827 - accuracy: 0.4409 - val_loss: 0.8495 - val_accuracy: 0.7605\n",
            "Epoch 2/25\n",
            "311/311 [==============================] - 25s 80ms/step - loss: 0.7100 - accuracy: 0.7872 - val_loss: 0.4238 - val_accuracy: 0.9353\n",
            "Epoch 3/25\n",
            "311/311 [==============================] - 27s 87ms/step - loss: 0.4814 - accuracy: 0.8637 - val_loss: 0.2984 - val_accuracy: 0.9504\n",
            "Epoch 4/25\n",
            "311/311 [==============================] - 25s 81ms/step - loss: 0.3815 - accuracy: 0.8803 - val_loss: 0.2641 - val_accuracy: 0.9487\n",
            "Epoch 5/25\n",
            "311/311 [==============================] - 24s 79ms/step - loss: 0.3332 - accuracy: 0.8941 - val_loss: 0.2458 - val_accuracy: 0.9477\n",
            "Epoch 6/25\n",
            "311/311 [==============================] - 24s 78ms/step - loss: 0.3001 - accuracy: 0.9006 - val_loss: 0.2126 - val_accuracy: 0.9558\n",
            "Epoch 7/25\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.2816 - accuracy: 0.9092 - val_loss: 0.2274 - val_accuracy: 0.9514\n",
            "Epoch 8/25\n",
            "311/311 [==============================] - 25s 80ms/step - loss: 0.2637 - accuracy: 0.9117 - val_loss: 0.2144 - val_accuracy: 0.9501\n",
            "Epoch 9/25\n",
            "311/311 [==============================] - 25s 81ms/step - loss: 0.2476 - accuracy: 0.9138 - val_loss: 0.2143 - val_accuracy: 0.9511\n",
            "Epoch 10/25\n",
            "311/311 [==============================] - 27s 85ms/step - loss: 0.2309 - accuracy: 0.9157 - val_loss: 0.2145 - val_accuracy: 0.9508\n",
            "Epoch 11/25\n",
            "311/311 [==============================] - 25s 82ms/step - loss: 0.2150 - accuracy: 0.9222 - val_loss: 0.2239 - val_accuracy: 0.9497\n",
            "Epoch 12/25\n",
            "311/311 [==============================] - 25s 80ms/step - loss: 0.2066 - accuracy: 0.9241 - val_loss: 0.2237 - val_accuracy: 0.9511\n",
            "Epoch 13/25\n",
            "311/311 [==============================] - 28s 91ms/step - loss: 0.1918 - accuracy: 0.9241 - val_loss: 0.2371 - val_accuracy: 0.9487\n",
            "Epoch 14/25\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.1815 - accuracy: 0.9338 - val_loss: 0.2353 - val_accuracy: 0.9524\n",
            "Epoch 15/25\n",
            "311/311 [==============================] - 24s 78ms/step - loss: 0.1783 - accuracy: 0.9332 - val_loss: 0.2605 - val_accuracy: 0.9481\n",
            "Epoch 16/25\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.1725 - accuracy: 0.9355 - val_loss: 0.2586 - val_accuracy: 0.9511\n",
            "Epoch 17/25\n",
            "311/311 [==============================] - 24s 79ms/step - loss: 0.1661 - accuracy: 0.9390 - val_loss: 0.2782 - val_accuracy: 0.9444\n",
            "Epoch 18/25\n",
            "311/311 [==============================] - 24s 79ms/step - loss: 0.1550 - accuracy: 0.9393 - val_loss: 0.2809 - val_accuracy: 0.9471\n",
            "Epoch 19/25\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.1558 - accuracy: 0.9383 - val_loss: 0.2944 - val_accuracy: 0.9454\n",
            "Epoch 20/25\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.1584 - accuracy: 0.9351 - val_loss: 0.3098 - val_accuracy: 0.9457\n",
            "Epoch 21/25\n",
            "311/311 [==============================] - 25s 79ms/step - loss: 0.1451 - accuracy: 0.9421 - val_loss: 0.3120 - val_accuracy: 0.9474\n",
            "Epoch 22/25\n",
            "311/311 [==============================] - 25s 81ms/step - loss: 0.1490 - accuracy: 0.9415 - val_loss: 0.3370 - val_accuracy: 0.9430\n",
            "Epoch 23/25\n",
            "311/311 [==============================] - 28s 91ms/step - loss: 0.1346 - accuracy: 0.9487 - val_loss: 0.3332 - val_accuracy: 0.9441\n",
            "Epoch 24/25\n",
            "311/311 [==============================] - 25s 81ms/step - loss: 0.1356 - accuracy: 0.9486 - val_loss: 0.3367 - val_accuracy: 0.9447\n",
            "Epoch 25/25\n",
            "311/311 [==============================] - 25s 80ms/step - loss: 0.1240 - accuracy: 0.9549 - val_loss: 0.3509 - val_accuracy: 0.9447\n",
            "CPU times: user 16min 58s, sys: 53.8 s, total: 17min 52s\n",
            "Wall time: 11min 29s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the graphs\n",
        "history_dict = history.history # dictionary of result values\n",
        "epochs = range(1, len(history_dict['accuracy']) + 1) # number of epochs\n",
        "\n",
        "# plot for loss\n",
        "plt.plot(epochs, history_dict['loss'], 'bo', label='Training loss')\n",
        "plt.plot(epochs, history_dict['val_loss'], 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# plot for accuracy\n",
        "plt.plot(epochs, history_dict['accuracy'], 'bo', label='Training acc')\n",
        "plt.plot(epochs, history_dict['val_accuracy'], 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "mFobMkTPxrV7",
        "outputId": "f3ea73b1-8bdc-4fcf-f3f3-a103bb0f3414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluate Model**"
      ],
      "metadata": {
        "id": "Lqi80zysdVyY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate model on test dataset\n",
        "x_test = np.array([np.array(val) for val in testdf['text']])\n",
        "y_test = np.array([np.array(val) for val in testdf['intent']])\n",
        "# model 1 evaluation\n",
        "print('Model 1 Evaluation on Test Set:')\n",
        "loss, accuracy = model1.evaluate(x_test, y_test)\n",
        "print(\"Loss: \", loss)\n",
        "print(\"Accuracy: \", accuracy)\n",
        "# model 2 evaluation\n",
        "print('\\nModel 2 Evaluation on Test Set:')\n",
        "loss, accuracy = model2.evaluate(x_test, y_test)\n",
        "print(\"Loss: \", loss)\n",
        "print(\"Accuracy: \", accuracy)\n",
        "# model 3 evaluation\n",
        "print('\\nModel 3 Evaluation on Test Set:')\n",
        "loss, accuracy = model3.evaluate(x_test, y_test)\n",
        "print(\"Loss: \", loss)\n",
        "print(\"Accuracy: \", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyNR3LFEdZWx",
        "outputId": "102a915c-2566-43fc-d44a-8edee01b33ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 1 Evaluation on Test Set:\n",
            "40/40 [==============================] - 0s 10ms/step - loss: 0.3600 - accuracy: 0.9617\n",
            "Loss:  0.3599610924720764\n",
            "Accuracy:  0.961718738079071\n",
            "\n",
            "Model 2 Evaluation on Test Set:\n",
            "40/40 [==============================] - 1s 13ms/step - loss: 0.2154 - accuracy: 0.9633\n",
            "Loss:  0.2153855860233307\n",
            "Accuracy:  0.9632812738418579\n",
            "\n",
            "Model 3 Evaluation on Test Set:\n",
            "40/40 [==============================] - 1s 15ms/step - loss: 0.3423 - accuracy: 0.9500\n",
            "Loss:  0.34234166145324707\n",
            "Accuracy:  0.949999988079071\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Export Best Model**"
      ],
      "metadata": {
        "id": "OOCtwhBJI-ri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model\n",
        "export_model = tf.keras.Sequential([\n",
        "  tf.keras.Input(shape=(1,), dtype=\"string\"),\n",
        "  model2 ]) # saving model 2\n",
        "\n",
        "export_model.save('saved_model/model2_lstm', save_format='tf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UU3Z3GZ03Jp1",
        "outputId": "b152c5c0-6489-4776-b98b-9ecb727aa052"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            "WARNING:absl:Found untraced functions such as lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_3_layer_call_fn, lstm_cell_3_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion:**\n",
        "\n",
        "**Model 1:** Vanilla LSTM\n",
        "\n",
        "INPUT => EMBEDDING(int encoding) => LSTM => FC => RELU => DO => FC => SOFTMAX\n",
        "1.   Trainable Parameters: 76,695\n",
        "1.   Epochs: 20\n",
        "2.   Loss and Accuracy:\n",
        "    *   Training Loss: 0.0901 - Training Accuracy: 0.9677\n",
        "    *   Validation Loss: 0.3097 - Validation Accuracy: 0.9658\n",
        "    *   Testing Loss: 0.3599 - Testing Accuracy: 0.9617\n",
        "3.   Wall Time: 5min 31s\n",
        "**Model 2:** BidirectionalLSTM\n",
        "\n",
        "INPUT => EMBEDDING(int encoding) => BiLSTM => FC => RELU => DO => FC => SOFTMAX\n",
        "1.   Trainable Parameters: 77,799\n",
        "1.   Epochs: 20\n",
        "2.   Loss and Accuracy:\n",
        "    *   Training Loss: 0.1004 - Training Accuracy: 0.9654\n",
        "    *   Validation Loss: 0.1602 - Validation Accuracy: 0.9672\n",
        "    *   Testing Loss: 0.2153 - Testing Accuracy: 0.9632\n",
        "3.   Wall Time: 7min 29s\n",
        "**Model 3:** Stacked LSTM\n",
        "\n",
        "INPUT => EMBEDDING(int encoding) => LSTM => LSTM => FC => RELU => DO => FC => SOFTMAX\n",
        "1.   Trainable Parameters: 76,159\n",
        "1.   Epochs: 25\n",
        "2.   Loss and Accuracy:\n",
        "    *   Training Loss: 0.1240 - Training Accuracy: 0.9549\n",
        "    *   Validation Loss: 0.3509 - Validation Accuracy: 0.9447\n",
        "    *   Testing Loss: 0.3423 - Testing Accuracy: 0.9499\n",
        "3.   Wall Time: 11min 29s\n",
        "\n",
        "**ANALYSIS:**\n",
        "1.   Model 3(stacked LSTM) **with less parameters** can achieve approximately similar or even better performance compared to Model 1(vanilla LSTM), but requires more training epochs to achieve the same.\n",
        "2.   Model 2(BidirectionalLSTM) achieved better performance compared to other two models."
      ],
      "metadata": {
        "id": "dZC6HxQXR2Pv"
      }
    }
  ]
}