Movie_review_text_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Movie review text classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVowZ4mfMhDD"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn29DRMMPGqh",
        "outputId": "f271886c-5382-41f3-f45a-c71b4cee2c42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSENu5bHUCtq",
        "outputId": "8da10f30-90d7-4b37-bf52-ff4ca53c6422",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "with open('/content/drive/My Drive/REVIEW CLASSIFIER/train.csv') as train:\n",
        "  print(train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<_io.TextIOWrapper name='/content/drive/My Drive/REVIEW CLASSIFIER/train.csv' mode='r' encoding='UTF-8'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEG-YnDqUbEH"
      },
      "source": [
        "import io\n",
        "#train=pd.read_csv(io.StringIO(uploaded['train.csv'].decode('utf8')))\n",
        "train=pd.read_csv('/content/drive/My Drive/REVIEW CLASSIFIER/train.csv')\n",
        "test=pd.read_csv(\"/content/drive/My Drive/REVIEW CLASSIFIER/test.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TehMy04gY4sp",
        "outputId": "0678d737-9176-41ec-ab73-f99dc50dc008",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head( )\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Review</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A series of escapades demonstrating the adage ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>A series</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>of</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>escapades demonstrating the adage that what is...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id                                             Review  Sentiment\n",
              "0   0  A series of escapades demonstrating the adage ...          1\n",
              "1   2                                           A series          2\n",
              "2   3                                                  A          2\n",
              "3   6                                                 of          2\n",
              "4   7  escapades demonstrating the adage that what is...          2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRBx0HnOr-rd",
        "outputId": "5cac4f57-7198-4d65-9a11-28ead108e1fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Review</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A series of escapades demonstrating the adage ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>series</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>of escapades demonstrating the adage that what...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>the adage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>adage</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id                                             Review\n",
              "0   1  A series of escapades demonstrating the adage ...\n",
              "1   4                                             series\n",
              "2   5  of escapades demonstrating the adage that what...\n",
              "3  12                                          the adage\n",
              "4  14                                              adage"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bH7TUZvrXCMJ",
        "outputId": "ac9bbd94-5ef5-47a9-d8bc-f571bf9b2537",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "def show_info(data):\n",
        "  print(\"Shape of dataset::\",data.shape)\n",
        "  print(\"columns are: \",data.columns)\n",
        "  print(\"Value analysis: \",data.describe())\n",
        "\n",
        "  print(\"DATA :::\\n\",data.head(4))\n",
        "  \n",
        "  print(\"\\nNull Values analysis::\\n\",data.isna().sum())\n",
        "  \n",
        "\n",
        "show_info(train)\n",
        "print(\"*******************TEST DATA************************\")\n",
        "show_info(test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of dataset:: (109170, 3)\n",
            "columns are:  Index(['Id', 'Review', 'Sentiment'], dtype='object')\n",
            "Value analysis:                    Id      Sentiment\n",
            "count  109170.000000  109170.000000\n",
            "mean    78090.855574       2.064523\n",
            "std     45021.513255       0.892050\n",
            "min         0.000000       0.000000\n",
            "25%     39051.250000       2.000000\n",
            "50%     78134.500000       2.000000\n",
            "75%    116994.750000       3.000000\n",
            "max    156059.000000       4.000000\n",
            "DATA :::\n",
            "    Id                                             Review  Sentiment\n",
            "0   0  A series of escapades demonstrating the adage ...          1\n",
            "1   2                                           A series          2\n",
            "2   3                                                  A          2\n",
            "3   6                                                 of          2\n",
            "\n",
            "Null Values analysis::\n",
            " Id           0\n",
            "Review       0\n",
            "Sentiment    0\n",
            "dtype: int64\n",
            "*******************TEST DATA************************\n",
            "Shape of dataset:: (46890, 2)\n",
            "columns are:  Index(['Id', 'Review'], dtype='object')\n",
            "Value analysis:                    Id\n",
            "count   46890.000000\n",
            "mean    77886.651034\n",
            "std     45119.022368\n",
            "min         1.000000\n",
            "25%     38890.500000\n",
            "50%     77797.500000\n",
            "75%    117124.750000\n",
            "max    156058.000000\n",
            "DATA :::\n",
            "    Id                                             Review\n",
            "0   1  A series of escapades demonstrating the adage ...\n",
            "1   4                                             series\n",
            "2   5  of escapades demonstrating the adage that what...\n",
            "3  12                                          the adage\n",
            "\n",
            "Null Values analysis::\n",
            " Id        0\n",
            "Review    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzU3t2CkZpu9",
        "outputId": "78f40a9a-4a17-4bcb-a0c5-10f546f1cf1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.countplot(train['Sentiment'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd09b3962b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REiPXkBMaQVD"
      },
      "source": [
        "# data preprocessing\n",
        "import nltk\n",
        "from bs4 import BeautifulSoup #web scrapping\n",
        "import re # expression matching operation\n",
        "from tqdm import tqdm\n",
        "from nltk.corpus import stopwords #natural language toolkit\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer=WordNetLemmatizer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1U1svOUdUtv"
      },
      "source": [
        "def data_preprocessing(df):\n",
        "    reviews = []\n",
        "    for raw in tqdm(df['Review']):\n",
        "        # remove html tag\n",
        "        text = BeautifulSoup(raw, 'lxml').get_text()\n",
        "        # remove non-letters\n",
        "        letters_only = re.sub('[^a-zA-Z]', ' ', text)\n",
        "        # split(lowercase)\n",
        "        words = word_tokenize(letters_only.lower())\n",
        "        # get stoplist words\n",
        "        stops = set(stopwords.words('english'))\n",
        "        # remove stopwords / get non-stopwords list\n",
        "        non_stopwords = [word for word in words if not word in stops]\n",
        "        # lemmatize word to its lemma\n",
        "        lemma_words = [lemmatizer.lemmatize(word) for word in non_stopwords]    \n",
        "        reviews.append(lemma_words)\n",
        "    return reviews\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nWoakk2iO_Y",
        "outputId": "e52f837c-386c-4757-b194-627eb8bb8d28",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdWU8OY0hy5Q",
        "outputId": "8fa5dd90-c9e2-4e12-d40e-612300e303fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "train_sentence=data_preprocessing(train)\n",
        "test_sentence=data_preprocessing(test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 1/109170 [00:01<46:34:32,  1.54s/it]/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:273: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
            "  ' Beautiful Soup.' % markup)\n",
            " 66%|██████▋   | 72458/109170 [00:46<00:22, 1616.69it/s]/usr/local/lib/python3.6/dist-packages/bs4/__init__.py:273: UserWarning: \"b'drive'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
            "  ' Beautiful Soup.' % markup)\n",
            "100%|██████████| 109170/109170 [01:09<00:00, 1564.55it/s]\n",
            "100%|██████████| 46890/46890 [00:29<00:00, 1576.85it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c82pulwjjRuy"
      },
      "source": [
        "# split train_validation ,tokenize\n",
        "\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing import sequence"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLeS1uLdj3Yb",
        "outputId": "a802f27c-e5b0-42be-8306-97a85f4edd82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "target=train.Sentiment.values\n",
        "y_target=to_categorical(target)#one hot encode\n",
        "num_of_class=y_target.shape[1]\n",
        "print(target[2],y_target[2],num_of_class)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2 [0. 0. 1. 0. 0.] 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8snw3T4Hklvl"
      },
      "source": [
        "Xtrain,Xval,ytrain,yval=train_test_split(train_sentence,y_target,test_size=0.2,stratify=y_target)\n",
        "Xtrain2,Xval2,ytrain2,yval2=train_test_split(train_sentence,y_target,test_size=0.3,stratify=y_target)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFUEHOpZm8Vv",
        "outputId": "62e7ad6b-bed5-4ba3-a715-d774898df588",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "unique_words=set()\n",
        "\n",
        "lenM=0\n",
        "for sent in tqdm(Xtrain):\n",
        "  unique_words.update(sent)\n",
        "  if lenM<len(sent):\n",
        "    lenM=len(sent)\n",
        "len(list(unique_words)) ,lenM"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 87336/87336 [00:00<00:00, 640463.49it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13571, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eeiuzBIRn6ur",
        "outputId": "e603e69c-b498-407c-d06e-0ccd5b0a9e66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# tokenizing and padding\n",
        "\n",
        "tokenizer=Tokenizer(num_words=len(list(unique_words)))\n",
        "tokenizer.fit_on_texts(list(Xtrain))\n",
        "word_index=tokenizer.word_index\n",
        "\n",
        "Xtrain=tokenizer.texts_to_sequences(Xtrain)\n",
        "Xval=tokenizer.texts_to_sequences(Xval)\n",
        "Xtest=tokenizer.texts_to_sequences(test_sentence)\n",
        "\n",
        "Xtrain=sequence.pad_sequences(Xtrain,maxlen=lenM)\n",
        "Xval=sequence.pad_sequences(Xval,maxlen=lenM)\n",
        "Xtest=sequence.pad_sequences(Xtest,maxlen=lenM)\n",
        "Xtrain.shape,Xval.shape,Xtest.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((87336, 28), (21834, 28), (46890, 28))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlbCXqNlKmbu"
      },
      "source": [
        "# defining the model along with using early stopping\n",
        "\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM,Dropout,Dense,Embedding\n",
        "from keras.optimizers import Adam\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cx7RKLhWpOqS",
        "outputId": "ba589ac9-efa5-457c-95f3-b8224fe376f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "!unzip \"/content/drive/My Drive/PRETRAINED EMBEDDING/crawl-300d-2M.vec.zip\" -d \"/content/drive/My Drive/PRETRAINED EMBEDDING\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/PRETRAINED EMBEDDING/crawl-300d-2M.vec.zip\n",
            "replace /content/drive/My Drive/PRETRAINED EMBEDDING/crawl-300d-2M.vec? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X968KrjqrMmz"
      },
      "source": [
        "embd_path='/content/drive/My Drive/PRETRAINED EMBEDDING/crawl-300d-2M.vec'\n",
        "embd_size=300\n",
        "max_feature=30000\n",
        "def get_coef(word,arr): return word,np.asarray(arr,dtype='float32')\n",
        "emb_index=dict(get_coef(o.split(\" \")[0],o.split(' ')[1:]) for o in open(embd_path))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jmuyy54ZvNBq"
      },
      "source": [
        "len(word_index)\n",
        "\n",
        "embd_matrix=np.zeros((len(word_index)+1,embd_size))\n",
        "for word, i in word_index.items():\n",
        "  if i>=max_feature:continue\n",
        "  embd_vector=emb_index.get(word)\n",
        "  if embd_vector is not None:embd_matrix[i]=embd_vector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbDE7n4pLQ-r"
      },
      "source": [
        "early_stop=EarlyStopping(min_delta=0.001,mode='max',patience=2,monitor='val_accuracy')\n",
        "callaback=[early_stop]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMlZBwE6L1Ne",
        "outputId": "6c1b33ca-aa35-4c9c-fd7d-4f4073faa4fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#definig the model\n",
        "\n",
        "model=Sequential()\n",
        "model.add(Embedding(len(list(unique_words))+1,300,weights=[embd_matrix],trainable=False,input_length=lenM))\n",
        "model.add(LSTM(128,dropout=0.5,recurrent_dropout=0.5,return_sequences=True))\n",
        "model.add(LSTM(64,dropout=0.5,recurrent_dropout=0.5,return_sequences=False))\n",
        "model.add(Dense(100,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_of_class,activation='softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.005),metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_5 (Embedding)      (None, 28, 300)           4071600   \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 28, 128)           219648    \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 64)                49408     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 100)               6500      \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 5)                 505       \n",
            "=================================================================\n",
            "Total params: 4,347,661\n",
            "Trainable params: 276,061\n",
            "Non-trainable params: 4,071,600\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Hi88VrWPIXG",
        "outputId": "065512a5-5a94-44de-807e-32dcef2753c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "#training with early stoping\n",
        "hist=model.fit(Xtrain,ytrain,validation_data=(Xval,yval),epochs=10,batch_size=256,verbose=1,callbacks=callaback)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "342/342 [==============================] - 194s 566ms/step - loss: 0.9953 - accuracy: 0.5928 - val_loss: 0.9013 - val_accuracy: 0.6233\n",
            "Epoch 2/10\n",
            "342/342 [==============================] - 195s 570ms/step - loss: 0.9258 - accuracy: 0.6195 - val_loss: 0.8796 - val_accuracy: 0.6361\n",
            "Epoch 3/10\n",
            "342/342 [==============================] - 194s 568ms/step - loss: 0.9000 - accuracy: 0.6299 - val_loss: 0.8529 - val_accuracy: 0.6486\n",
            "Epoch 4/10\n",
            "342/342 [==============================] - 194s 567ms/step - loss: 0.8837 - accuracy: 0.6357 - val_loss: 0.8424 - val_accuracy: 0.6483\n",
            "Epoch 5/10\n",
            "342/342 [==============================] - 193s 565ms/step - loss: 0.8712 - accuracy: 0.6413 - val_loss: 0.8412 - val_accuracy: 0.6501\n",
            "Epoch 6/10\n",
            "342/342 [==============================] - 193s 563ms/step - loss: 0.8639 - accuracy: 0.6440 - val_loss: 0.8280 - val_accuracy: 0.6561\n",
            "Epoch 7/10\n",
            "342/342 [==============================] - 192s 563ms/step - loss: 0.8526 - accuracy: 0.6485 - val_loss: 0.8289 - val_accuracy: 0.6544\n",
            "Epoch 8/10\n",
            "342/342 [==============================] - 194s 567ms/step - loss: 0.8480 - accuracy: 0.6508 - val_loss: 0.8297 - val_accuracy: 0.6547\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Q-kPTtyCBQI",
        "outputId": "26e9dc87-0806-45d5-a1a1-e266d3a7a462",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "ep=range(1,9)\n",
        "plt.plot(ep,hist.history['val_accuracy'],'r--')\n",
        "plt.plot(ep,hist.history['accuracy'],'b--')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Validation Accuracy','Training Accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff305843898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pq3PosU8RT07",
        "outputId": "5cd1a1a9-02d1-40d6-880a-6e3fb9f9caa4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "plt.plot(ep,hist.history['val_loss'],'y--')\n",
        "plt.plot(ep,hist.history['accuracy'],'g--')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend(['Validation Loss','Training Loss'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ff305834b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8ddnJnsC2SMhgAkKQfYlRMEF0Eq5ilJFLKhVaosr+pNrrdpbK7VuvVe72FpbcW2vFXcurQoqSrFiKwFkCRJkE8OahAQI2SYzn98fczJOYAIBMkwCn+fjMQ9mzvecM5+J7Xmf71m+R1QVY4wx5kCuSBdgjDGmfbKAMMYYE5IFhDHGmJAsIIwxxoRkAWGMMSakqEgX0FYyMjI0Nzc30mUYY0yHsnTp0nJVzQzVdsIERG5uLkVFRZEuwxhjOhQR+aqlNjvEZIwxJiQLCGOMMSFZQBhjjAnphDkHYYw5fjweD6WlpdTV1UW6FNNKcXFxdOvWjejo6FYvYwFhjDlipaWldOrUidzcXEQk0uWYw1BVKioqKC0tJS8vr9XL2SEmY8wRq6urIz093cKhgxAR0tPTj7jHF9aAEJFxIlIiIutF5J4Q7aeKyAIRWSkiC0WkW1DbdSLypfO6Lpx1GmOOnIVDx3I0/73CFhAi4gaeBP4D6AtMEZG+B8z2GPBnVR0IPAA84iybBtwPnAkUAveLSGq4am1oKGP37vn4fA3h+gpjjOlwwtmDKATWq+pGVW0AZgMTDpinL/Ch8/6joPZvA++r6m5VrQTeB8aFq9CystdZuXIcn3ySxZo111BW9iZe7/5wfZ0x5hiNGTOG+fPnN5v2m9/8hptvvrnFZUaPHh24mfaiiy6iqqrqoHlmzpzJY489dsjvnjNnDmvWrAl8/tnPfsYHH3xwJOWHtHDhQsaPH3/M62lL4QyIHODroM+lzrRgK4DLnfeXAZ1EJL2VyyIiN4hIkYgUlZWVHXWhXbp8nwED/k5m5kR2755HcfFEPvnkFBob9wD+EzzGmPZjypQpzJ49u9m02bNnM2XKlFYt/84775CSknJU331gQDzwwAN861vfOqp1tXeRPkn9I2CUiCwHRgFbAW9rF1bVp1W1QFULMjNDDiXSKm53HOnpF9Onz7OMHLmDQYM+JDd3JlFRyQCsXn0ZK1aMY9u2p2lo2HnU32OMaRtXXHEFb7/9Ng0N/sPCmzdvZtu2bZx77rncfPPNFBQU0K9fP+6///6Qy+fm5lJeXg7AQw89RO/evTnnnHMoKSkJzDNr1iyGDx/OoEGDmDhxIjU1NSxevJi5c+dy1113MXjwYDZs2MDUqVN5/fXXAViwYAFDhgxhwIABXH/99dTX1we+7/7772fo0KEMGDCAtWvXtvq3vvzyywwYMID+/ftz9913A+D1epk6dSr9+/dnwIAB/PrXvwbgiSeeoG/fvgwcOJDJkycf4V/1YOG8zHUr0D3oczdnWoCqbsPpQYhIEjBRVatEZCsw+oBlF4ax1gCXK4rU1DGkpo4JTEtKGsCuXbNZt+5G1q27ieTkc8jJuZWsrO8ej5KMafeWLx990LSsrCvJybkFr7eGlSsvOqi9S5epZGdPpaGhnOLiK5q1DRmy8JDfl5aWRmFhIe+++y4TJkxg9uzZXHnllYgIDz30EGlpaXi9Xi644AJWrlzJwIEDQ65n6dKlzJ49m88//5zGxkaGDh3KsGHDALj88suZNm0aAD/96U959tlnue2227j00ksZP348V1zRvOa6ujqmTp3KggUL6N27N9deey1PPfUUd9xxBwAZGRksW7aMP/zhDzz22GM888wzh/yNANu2bePuu+9m6dKlpKamMnbsWObMmUP37t3ZunUrq1evBggcLnv00UfZtGkTsbGxIQ+hHalw9iCWAL1EJE9EYoDJwNzgGUQkQ0SaargXeM55Px8YKyKpzsnpsc60iMjL+wWFhesoKFhJbu79NDbuobZ2AwBe736++uph9u//IlLlGXNSCj7MFHx46dVXX2Xo0KEMGTKE4uLiZoeDDvTxxx9z2WWXkZCQQOfOnbn00ksDbatXr+bcc89lwIABvPTSSxQXFx+ynpKSEvLy8ujduzcA1113HYsWLQq0X365/2j6sGHD2Lx5c6t+45IlSxg9ejSZmZlERUVx9dVXs2jRInr27MnGjRu57bbbmDdvHp07dwZg4MCBXH311fzv//4vUVHHvv8fth6EqjaKyHT8G3Y38JyqFovIA0CRqs7F30t4REQUWATc6iy7W0R+gT9kAB5Q1d3hqrU1RISkpAEkJQ0gN/d+VP1Hwvbs+ZRNm/6LTZv+i4SEPmRkXE5m5uUkJQ21ywDNSeNQe/xud8Ih22NiMg7bYwhlwoQJzJgxg2XLllFTU8OwYcPYtGkTjz32GEuWLCE1NZWpU6ce9d3eU6dOZc6cOQwaNIgXXniBhQuPvMZgsbGxALjdbhobG49pXampqaxYsYL58+fzxz/+kVdffZXnnnuOt99+m0WLFvG3v/2Nhx56iFWrVh1TUIT1HISqvqOqvVX1NFV9yJn2MyccUNXXVbWXM88PVbU+aNnnVPV05/V8OOs8Gv6reCEt7VuMGFFKr15PEhOTw5Ytv2Tp0gL2718JgMdTFQgTY0zbSUpKYsyYMVx//fWB3sPevXtJTEwkOTmZnTt38u677x5yHeeddx5z5syhtraWffv28be//S3Qtm/fPrKzs/F4PLz00kuB6Z06dWLfvn0HrSs/P5/Nmzezfv16AP7yl78watSoY/qNhYWF/OMf/6C8vByv18vLL7/MqFGjKC8vx+fzMXHiRB588EGWLVuGz+fj66+/ZsyYMfzyl79kz549VFdXH9P321AbbSA2NoecnFvIybkFj6eC3bvfJzHRf8xzw4Y7qaj4GxkZ3yEj43JSU8/H5YqJcMXGnBimTJnCZZddFjjUNGjQIIYMGUKfPn3o3r07Z5999iGXHzp0KN/97ncZNGgQWVlZDB8+PND2i1/8gjPPPJPMzEzOPPPMQChMnjyZadOm8cQTTwROToN/rKPnn3+eSZMm0djYyPDhw7npppuO6PcsWLCAbt0C9wvz2muv8eijjzJmzBhUlYsvvpgJEyawYsUKvv/97+Pz+QB45JFH8Hq9XHPNNezZswdV5fbbbz/qK7WayIlyCWdBQYG2xwcGlZf/jZ07X2L37rfxeqtxu5PJzv4Bp5/+eKRLM+aoffHFF5xxxhmRLsMcoVD/3URkqaoWhJrfehBhlpFxCRkZl+D11lFZ+QHl5W/idicCoOpj7drrSU29gPT0S4iOPra0N8aYtmQBcZy43XFkZIwnI+ObOyXr6rZQWfkBO3e+iEgUKSnnk5l5ORkZE4mJyYhgtcYYE/kb5U5q8fG5jBixhaFD/0W3bv9JXd1G1q27ierqpQDU12+jtnZThKs0xpysrAcRYSIuOnc+k86dz6Rnz0fZv381CQn5AGzd+iRbtjxMUtKQwOWzCQln2OWzxpjjwnoQ7UjTvRZNVzllZ/+Qnj3/B5crjs2b72PJkn4sXTrcxoYyxhwX1oNox+Lj8+jR40f06PEj6uu3UV4+B49nd6AH8eWXt5OcfA4ZGRNwuWIjXK0x5kRjPYgOIja2Kzk5t5Cb+1MAPJ4Kysv/jzVrvsunn3Zj/fo7bbgPc9KoqKhg8ODBDB48mC5dupCTkxP43DSAX0uKioq4/fbbD/sdI0eObJNa2+Mw3q1lPYgOKjo6nbPO2khl5Qds2zaLrVufoLT0V/Tr9xaZmd+JdHnGhFV6ejqff/454H+GQ1JSEj/60Y8C7Y2NjS0OMVFQUEBBQcjL/ptZvHhx2xTbgVkPogMTcZOW9m3693+dESO20rPn/5CaegEA27c/x7p1t7Bv37IIV2nM8TF16lRuuukmzjzzTH784x/z2WefMWLECIYMGcLIkSMDQ3kH79HPnDmT66+/ntGjR9OzZ0+eeOKJwPqSkpIC848ePZorrriCPn36cPXVVwfOA77zzjv06dOHYcOGcfvttx9RTyGSw3i3lvUgThAxMVn06PHNHlRd3RZ27HiebdueIilpKNnZ0zjllKuIiuocwSrNiWr0C6MPmnZlvyu5Zfgt1HhquOilg4f7njp4KlMHT6W8ppwrXm0+dPbCqQuPqo7S0lIWL16M2+1m7969fPzxx0RFRfHBBx/wk5/8hDfeeOOgZdauXctHH33Evn37yM/P5+abbyY6OrrZPMuXL6e4uJiuXbty9tln88knn1BQUMCNN97IokWLyMvLa/XDiiDyw3i3lvUgTlB5eTMZMWIbp5/+O1Qb+fLLmykunhTpsowJq0mTJuF2+wfS3LNnD5MmTaJ///7MmDGjxeG6L774YmJjY8nIyCArK4udOw9+KFhhYSHdunXD5XIxePBgNm/ezNq1a+nZsyd5eXkARxQQkR7Gu7WsB3ECi45OpVu36eTk3Mq+fUWBUWUbGnaxYsVYunS5llNOudbu2jbH7FB7/AnRCYdsz0jIOOoew4ESExMD7++77z7GjBnDW2+9xebNmxk9enTIZZqG4YaWh+JuzTxt4XgN491a1oM4CYgInTsPJzn5LAAaGnbgcsWxYcOdfPppDsXFk6msXICqL8KVGtN29uzZQ06O/1H2L7zwQpuvPz8/n40bNwYe/vPKK6+0etlID+PdWtaDOAklJQ1k2LB/UV29iu3bn2Xnzj9TVvYqZ521ibi4U1H18c2D/ozpmH784x9z3XXX8eCDD3LxxRe3+frj4+P5wx/+wLhx40hMTGw2VPiB2tsw3q1lw30bvN469u79JHAF1KpV3wGU7OxppKWNw+Wy/QjTnA337VddXU1SUhKqyq233kqvXr2YMWNGpMtq0ZEO9227iQa3Oy4QDqpKYmI/9u79N6tXX8K//pXLpk33UVu7ObJFGtMOzZo1i8GDB9OvXz/27NnDjTfeGOmS2pT1IExIPp+Hioq/s337LHbvnkde3oOceupP8Pk8gNpT8U5y1oPomOyBQaZNuFzRZGZeRmbmZdTVfY3bnQBAeflbfPnlrZxyynVkZ/+QxMQ+Ea7URIqq2sjCHcjRdAbsEJM5rLi47kRHpzvvc0lOPo+tW3/LkiVnsHz5uezY8efAJbTm5BAXF0dFRYWNLNxBqCoVFRXExcUd0XJ2iMkclYaGnezY8SLbtz+DiJvhw9cgItTXbyc2NjvS5Zkw83g8lJaWUldXF+lSTCvFxcXRrVu3g+4SP9QhJgsIc0xUlYaGHcTGZuP11rB4cVcSEnqRnf1DsrKm2NAexrRzEbuKSUTGiUiJiKwXkXtCtPcQkY9EZLmIrBSRi5zpuSJSKyKfO68/hrNOc/REpFmPIS/vF/h89axbdxOLF2ezdu33qalZB2A34hnTwYTtJLWIuIEngQuBUmCJiMxV1TVBs/0UeFVVnxKRvsA7QK7TtkFVB4erPtP23O4EunW7jZyc6ezbt4Tt22exa9dssrOnAb3ZtesVvvzyNhIS+jivfBIS+pCaej5ud+Jh12+MOb7CeRVTIbBeVTcCiMhsYAIQHBAKNB2DSAa2hbEec5z4h/YopHPnQnr3/qbzFxeXS2bmRGpqSqio+Ds7djwLwFlnfYXbncj27S9QUfF/JCT0IT4+PxAi0dGpkfopxpzUwhkQOcDXQZ9LgTMPmGcm8J6I3AYkAt8KassTkeXAXuCnqvrxgV8gIjcANwD06NGj7So3bcbfkfRLTh5BcvKIwGePp4qamrXExvqHIPD59jvh8TaqHmcuN+edV4PLFcOuXa9QX7810POIi8tttn5jTNuK9H0QU4AXVPVxERkB/EVE+gPbgR6qWiEiw4A5ItJPVfcGL6yqTwNPg/8k9fEu3hyb6OiUwACCADk5t5KTcys+XyN1dZuoqVlLQ8O2wE15ZWWvU1b2emB+kRiSk89l8OAPAKiq+gcuVzwJCflERSUf3x9jzAkonAGxFege9LmbMy3YD4BxAKr6qYjEARmquguod6YvFZENQG/ALlM6CbhcUSQk9CIhoVez6f36vYbHU0FNTQk1NWupqVmLyDd3dK9bdws1Nf4jmDEx2SQk5JOWdhE9etwFQH39dmJiTrGBCI1ppXAGxBKgl4jk4Q+GycBVB8yzBbgAeEFEzgDigDIRyQR2q6pXRHoCvYCNYazVdBDR0ekkJ48kOfngB8r37/8m+/d/QU3NWmpr/SFSX78F8F+O+9lnfVBtID6+d9AJ8gtJSTn3eP8MYzqEsAWEqjaKyHRgPuAGnlPVYhF5AChS1bnAncAsEZmB/4T1VFVVETkPeEBEPIAPuElVd4erVnNi8G/080O2qXo57bTHnJ5HCfv2LaOs7A1UG0lJOZfGxr189llfYmO74XLFIBKFSDRdu95IZubl1NdvY8OGuxCJxuWKRsT/ysqaTHLyCOrrt7F9+6zA9Kb5UlPHkpDQi4aGnVRVLXTaogLzJCUNJiYmA4+nirq6zc3WLRJFTEwWLlcsPp8H1UZnutuGuDDHRVjPQajqO/gvXQ2e9rOg92uAs0Ms9wZw8MNjjTlKLlcUXbtOazbN56vH56sHwOutITX1fBoadqDqwedrQLWmWfu+fZ85G2oPqg34fB46dRrmBMTXbN4886Dv7dv3FRISelFdvYo1aw5+2PyAAe+Qnv4fVFV9RHHx5Qe1Dx68iJSUc9m1azZr114bmO4PoFgGDfqQzp2HU1u7CY+njMTEAbjd8cfypzImwO6kNqaNqPqccGkKEQ9udyfc7ni83v3U1X3lTG8MzJOY2I/o6DTq67exd++/A8s19RjS08cTG9uF6urV7N79drN1e737yc39OdHRKWzaNJOvvvo54CYx8QySkoaSlDSErl1vwu0+svF3zMnFhtow5gRXX7+dvXv/RXX1MvbtW0Z19XIaG/dy7rl7EHGzefPP2b9/DZ06+YMjKWkIMTGZkS7btAM23LcxJ7jY2OzA8OxNPJ7dgftEfL469u37jLKyVwPtnTufzdCh/wRg797PiInpSmxsjp3fMAEWEMacoKKj0wLve/Z8hJ49H8Hj2U119edUVy/Hf+2IX3HxROrrS4mOzggcnkpLG0tq6vkRqNy0FxYQxpxEoqPTSE09v9mGX1Xp2/cVqquXBw5PlZb+Cq+3mtTU8/H5Glm5chxJSQOcw1NDSUjoY88qPwnYf2FjTnIictC9JT5fPV5vDQAeTzk+Xw3btv0Jn68WAJcrjl69fk929g9obKymtraExMT+uFyxEfkNJjwsIIwxB3G5YgMb+9jYLgwduhhVb+Aekurq5SQk9ANg795PWblyLCJRJCT0o1Mnfy8jM3MSsbFdIvkzQvJfmKOBO+q93hrnsuZGwOv86woMY19buxmfbz+q3sDL7U4gMbEv4D9/4/Xua9YeHZ0RGEamvHzuQe1xcbmkpfmHntu69Sl8vhqnrRFVL0lJg8jIuBSAjRvvdZ4F/83yKSmjycqahM/XQEnJNPLzn8Hlav4goLZgAWGMaRURN4mJfZ0N4zWB6Z06DaVv39eorvYHR0XFO+zY8QLJyecQG9uF8vK/sWvXy8TGdnceTevfEPbs+ShudyK7dr1ORcXfnY3jNxvpvn1fw+WKorT095SXv9VsWXAHTrBv2HBPoL1pebc7mcLC1QCsWXM15eVzmrXHxeVy1lmbAFi16lKqqhY0+62JiQMZPnyFs/x32bfvs2btnTuPZOjQTwCcZ56sadaemjqWQYPmA/Dll9Opr/+6WXtGxsRAQGza9BMaG6uatXfpMjUQEKWlv8MfaG7nogM3UVHJZGVNApSqqn84fxsLCGNMOxMdnU5W1hVkZV0BND1lcDvR0VkAeDy72LPnnzQ07HTuIo9CxE1u7kzc7kTq6jY4d5lHORvBKGdD6AWinJsS6522GFyu+GaHsuLiupOUNKTZsm73N08yTE29kJiY7KD1u4mK+uYEfteuN5GePr7Z8tHRGYH2vLwHaWysDGyc/e3pgfY+fZ7H56tr1h4VlRJoHzToQ8DXrD34+SdnnrkJEVfg+5vmaXLeedUt/u1drlhGjNjcqv9OR8PugzDGmJNYxB45aowxpuOygDDGGBOSBYQxxpiQLCCMMcaEZAFhjDEmJAsIY4wxIVlAGGOMCckCwhhjTEgWEMYYY0KygDDGGBOSBYQxxpiQLCCMMcaEZAFhjDEmpLAGhIiME5ESEVkvIveEaO8hIh+JyHIRWSkiFwW13essVyIi3w5nncYYYw4WtudBiH9A8yeBC4FSYImIzFXV4Cdr/BR4VVWfEpG+wDtArvN+MtAP6Ap8ICK91f9UDGOMMcdBOHsQhcB6Vd2oqg3AbGDCAfMo0PRkj2Rgm/N+AjBbVetVdROw3lmfMcaY4yScAZEDBD9nr9SZFmwmcI2IlOLvPdx2BMsiIjeISJGIFJWVlbVV3cYYY4j8SeopwAuq2g24CPiLND1JvBVU9WlVLVDVgszMzLAVaYwxJ6NwPpN6K9A96HM3Z1qwHwDjAFT1UxGJAzJauawxxpgwCmcPYgnQS0TyRCQG/0nnuQfMswW4AEBEzgDigDJnvskiEisieUAv4LMw1mqMMeYAYetBqGqjiEwH5gNu4DlVLRaRB4AiVZ0L3AnMEpEZ+E9YT1VVBYpF5FVgDdAI3GpXMBljzPEl/u1xx1dQUKBFRUWRLsMYYzoUEVmqqgWh2iJ9ktoYY0w7ZQFhjDEmJAsIY4wxIVlAGGOMCckCwhhjTEgWEMYYY0KygDDGGBOSBYQxxpiQLCCMMcaEZAFhjDEmJAsIY4wxIVlAGGOMCckCwhhjTEgWEMYYY0KygDDGGBOSBYQxxpiQLCCMMcaEZAFhjDEmJAsIY4wxIVlAGGOMCalVASEiiSLict73FpFLRSQ6vKUZY4yJpNb2IBYBcSKSA7wHfA94IVxFGWOMibzWBoSoag1wOfAHVZ0E9AtfWcYYYyKt1QEhIiOAq4G3nWnuViw0TkRKRGS9iNwTov3XIvK581onIlVBbd6gtrmtrNMYY0wbiWrlfHcA9wJvqWqxiPQEPjrUAiLiBp4ELgRKgSUiMldV1zTNo6ozgua/DRgStIpaVR3cyvqMMca0sVYFhKr+A/gHgHOyulxVbz/MYoXAelXd6Cw3G5gArGlh/inA/a2pxxhjTPi19iqmv4pIZxFJBFYDa0TkrsMslgN8HfS51JkWav2nAnnAh0GT40SkSET+JSLfaWG5G5x5isrKylrzU4wxxrRSa89B9FXVvcB3gHfxb8y/14Z1TAZeV1Vv0LRTVbUAuAr4jYicduBCqvq0qhaoakFmZmYblmOMMaa1ARHt3PfwHWCuqnoAPcwyW4HuQZ+7OdNCmQy8HDxBVbc6/24EFtL8/IQxxpgwa21A/AnYDCQCi5xDQnsPs8wSoJeI5IlIDP4QOOhqJBHpA6QCnwZNSxWRWOd9BnA2LZ+7MMYYEwatPUn9BPBE0KSvRGTMYZZpFJHpwHz8l8Q+51wB9QBQpKpNYTEZmK2qwT2SM4A/iYgPf4g9Gnz1kzHGmPCT5tvlFmYSScZ/hdF5zqR/AA+o6p4w1nZECgoKtKioKNJlGGNMhyIiS53zvQdp7SGm54B9wJXOay/wfNuUZ4wxpj1q7Y1yp6nqxKDPPxeRz8NRkDHGmPahtT2IWhE5p+mDiJwN1IanJGOMMe1Ba3sQNwF/ds5FAFQC14WnJGOMMe1Ba69iWgEMEpHOzue9InIHsDKcxRljjImcI3qinKrude6oBvjPMNRjjDGmnTiWR45Km1VhjDGm3TmWgDj8DRTGGGM6rEOegxCRfYQOAgHiw1KRMcaYduGQAaGqnY5XIcYYY9qXYznEZIwx5gRmAWGMMSYkCwhjjDEhWUAYY4wJyQLCGGNMSBYQxhhjQrKAMMYYE5IFhDHGmJAsIIwxxoRkAWGMMSYkCwhjjDEhWUAYY4wJyQLCGGNMSGENCBEZJyIlIrJeRO4J0f5rEfncea0TkaqgtutE5EvnZc+/NsaY46xVz6Q+GiLiBp4ELgRKgSUiMldV1zTNo6ozgua/DRjivE8D7gcK8D+PYqmzbGW46jXGGNNcOHsQhcB6Vd2oqg3AbGDCIeafArzsvP828L6q7nZC4X1gXBhrNcYYc4BwBkQO8HXQ51Jn2kFE5FQgD/jwSJYVkRtEpEhEisrKytqkaGOMMX7t5ST1ZOB1VfUeyUKq+rSqFqhqQWZmZphKM8aYk1M4A2Ir0D3oczdnWiiT+ebw0pEua4wxJgzCGRBLgF4ikiciMfhDYO6BM4lIHyAV+DRo8nxgrIikikgqMNaZZowx5jgJ21VMqtooItPxb9jdwHOqWiwiDwBFqtoUFpOB2aqqQcvuFpFf4A8ZgAdUdXe4ajXGGHMwCdoud2gFBQVaVFQU6TKMMaZDEZGlqloQqq29nKQ2xhjTzlhAGGOMCckCwhhjTEgWEMYYY0KygDDGGBOSBYQxxpiQLCCMMcaEZAFhjDEmJAsIY4wxIVlAGGOMCckCwhhjTEgWEMYYY0KygDDGGBOSBYQxxpiQLCCMMcaEZAFhjDEmJAsIY4wxIVlAGGOMCckCwhhjTEgWEMYYY0KygDDGGBOSBYQxxpiQLCCMMcaEFNaAEJFxIlIiIutF5J4W5rlSRNaISLGI/DVouldEPndec8NZpzHGmINFhWvFIuIGngQuBEqBJSIyV1XXBM3TC7gXOFtVK0UkK2gVtao6OFz1GWOMObSwBQRQCKxX1Y0AIjIbmACsCZpnGvCkqlYCqOquMNZjjDFhp6rUe+upa6yj1lNLanwqcVFxlNeU80XZF9Q21lLrqQ38O773eDITM1m2fRlvfvHmN21O++NjHyencw6zV8/m8U8fb7ZsbWMtxbcU07VT17D8lnAGRA7wddDnUuDMA+bpDSAinwBuYKaqznPa4kSkCGgEHlXVOQd+gYjcANwA0KNHj7at3hhzQvN4PVTVVREbFUvn2M5U1VUxf/18KusqqaqrosZTQ62nlkn9JlGYU0hJeQn3LLjnoA3042MfZ+xpY1mwcQGXvHwJdY11KBr4nvnXzGfsaWNZuHkhk16bdFAd//z+P8lMzGTVzlU88s9HiI+KJz46PvBvdUM1APFR8WQmZFZ+jtoAABI0SURBVH7T5rTHumPD9jcKZ0C09vt7AaOBbsAiERmgqlXAqaq6VUR6Ah+KyCpV3RC8sKo+DTwNUFBQoBhjThqqSm1jLT71kRSTRKOvkXnr51FZ69/AV9ZVUllbyQU9L2B87/HsrN7JhX+5MNDWtOH91dhfMWPEDHZU72DyG5ObfUd8VDz9s/pTmFOIx+dh/e71gQ1zWnwa8dHxJEYnAtAjuQe3Dr+VuKi4Zhv4Phl9ADi3x7m8/733mwVAXFRcYO//2kHXct3g61r8vRP6TGBCnwnh+FO2KJwBsRXoHvS5mzMtWCnwb1X1AJtEZB3+wFiiqlsBVHWjiCwEhgAbMMacMHzqo8HbQFxUHAAff/UxZTVl/o24s6E/I/MMrhpwFQDnPX8eZTVlVNZWUllXSYO3genDp/O7i36HqnLJy5c0W39STBLpCemM7z2exJhEeqb2JDU+ldQ4/yslLoXzTj0PgJ6pPSm+pTgwPS4qDhEJrKt/Vn9W3byqxd/SK70X/zP2f1psPyXpFE5JOqXF9uDvai/CGRBLgF4ikoc/GCYDVx0wzxxgCvC8iGTgP+S0UURSgRpVrXemnw38dxhrNca0QnVDNfvq91HjqQm8XOJieM5wAP6+7u9s2bMl0La/YT/ZnbK546w7APj+/32fVTtXBQ7jVNVVMe70cbx91dsATHljClv3fbMf6RIXVw+4OhAQmYmZdEnqQkpcin8jH59KYU4hANHuaJZMW0JKXErgFeX6ZhOXFJPEnMkHHakOiHHH0Dezb9v+wTq4sAWEqjaKyHRgPv7zC8+parGIPAAUqepcp22siKwBvMBdqlohIiOBP4mID/+luI8GX/1kjPmGqv/oqohQWVtJeU25f+Ps2R84jn5Jvn/P+oONH7Bs+7LAxrvGUwPAkxc/CcDPF/6ceRvmBdpqPDWkxaex+pbVAEx6bRLz1s9r9v19Mvrwxa1fAPDfn/w3H2/5ONAW645lZPeRgYBw4SIrMYv8jHxSYlNIjU+lX2a/wPxvfvdNYtwxgQDoFNsJl3xzNf4bV75xyL9FQdeCI/8DmhaF9RyEqr4DvHPAtJ8FvVfgP51X8DyLgQHhrM2Y9sinPqrqqti1fxdl+8soqylj1/5dfG/g90iMSWT26tnMWjYr0Na0Id999246x3bm4Y8f5rFPHztovZ77PES5onjzizd5qugpwH98PSE6gbT4tMB8Ua4oOsd2pktSFxKiE0iMTiQzITPQfnPBzUzInxBoS4hOIDU+NdD+2qTXEBESohOIj4rH7XI3q+PZCc8e8vc39QZM+yBNex8dXUFBgRYVFUW6DGOaUVWq6qqIj/afkNxUuYn3NrwX2PA3/fvHi/9Ir/RePPnZk0x/d/pB61l761ryM/J58fMXmbVsFpmJmWTEZ9ApthMJ0Qnce869JMYkUrStiJLyEhKiE5q9hmQPwSUu9jfsR0SIi4prtmduTl4islRVQ3a9LCCMOQJNG/ymjXvZfv8GfkzeGHqn9+bzHZ/zo/d+FNjwl9eU0+hr5N2r32Xc6eOYs3YOl71yGQDJsclkJmaSlZjFUxc/xcBTBrJq5yoWbFpAZoJ/emZiJpkJ/uPuB+6NG9MWDhUQkb7M1ZiIUlX21u8FIDkumeqGav666q/NDu+U1ZRxw9AbmNRvEit3rmTwnw6+wf+ZS56hd3pv3OKmxlNDbkouhV0LAwHQO703ABf2vJDSGaVkJGQQG3Xw9esDThnAgFPs6KppHywgzAmn1lPLrv27mh3CyUvJY1TuKOoa6/jO7O80a2vwNvBf5/4XD57/IHWNddz49xsB6BzbObAn71UvALkpufxq7K8CG/7MhMzAe/Bv4Bf/YHGLtSXGJJIYkxj+P4IxbcACwrR7Hq+H/Z79pMSlAPDmF2+yZc+WQAjs2r+LodlDmTl6JgBZj2UFboJqMnXwVEbljiLWHcve+r107dSVwV0Gk5WYRVZiFiO7jwQgLT6NLXdsITMxM3BtfrDkuGRmjJgR3h9sTDthAWGOO5/6qKytbLaBd4mLiX0nAnDn/DtZsm1JYC9/d+1uRp06ioVTFwLwkwU/oaSihChXVGAPP/j69V9+65fEumMDG/+mF/gvBT3UHr5LXHRP7t5iuzEnEwsI0+Z2Vu+kuKyYkvISvtz9JTuqdwDw14n+0dzH/3U8765/t9kyp6edHgiIqroq3C43A08ZGNi456fnB+Z973vv0SmmEylxKSHvPr1l+C3h+mnGnFQsIMxRqfHUsK5iHSXlJZRUlLChcgPPT3gel7i4d8G9PP/584D/WvuczjmcmnxqYNnrh1zPuNPHBfb+g/fw4fDXyvdItoEZjTkeLCBMi3zqY+verZRUlLC2fC3XDrqWzrGdeWzxY9z1/l3N5u2R3IPdtbvJSMhgeuF0rhpwFfnp+eR0zjnoevsr+l5xPH+GMeYoWUAYqhuqWVexjtyUXNLi0/hw04fc+d6drKtYFxiKAWBY9jBGdB/ByO4jeWD0A+Rn5JOfnk+v9F4kRCcE5huaPTQSP8MY08YsIE4SPvXh8XqIjYply54t/PKfvwz0DJoGR3v1ileZ1G8SnWI60SWpC6NOHUV+ej59MvqQn5FPdlI2ACO7jwxc9WOMOXFZQJyA6hrreH3N64HzAyUVJayrWMfD5z/MjBEz8Pq8vLTqJfIz8jk/73zy0/PJz8jnnB7nADA8ZzjvXv3uYb7FGHOis4DooDZWbqSk3N8DaAqB83PP575R9wFw7VvX4hIXeal59Mnow4U9LwyMdJmbkkvl3ZXtcvx5Y0z7YQHRQTR4G9hYuTHwdKoRz45g137/I7zT4tPIT8+nU2wnAOKi4iiZXsKpKacS4445aF0WDMaY1rCAaOdKykt4dvmzvLjiReKi4th4+0bcLjfPXvosqXGp5Gfkk5GQcdByvdJ7RaBaY8yJxAKinVq4eSH3L7yfRV8tIsoVxSW9L+GHQ38Y2Psf33t8hCs0xpzoLCDakRU7VpCZmEnXTl2p8dSwbd82Hr3gUa4bfB1dkrpEujxjzEnGAiLC9tbvDTwlrGhbEfeecy8PX/Aw404fx7rp6+x8gTEmYiwgIkRVmf7OdF5c8SL7Pfvpn9Wf3477LdcMvAbAnvZljIk4C4jjaHftbuavn8+UAVMQERq8DUzuP5lpQ6dRmFNovQVjTLtiARFmPvWxcPNCnln2DG9+8Sb13noKcwo5Le00Zl06K9LlGWNMiywgwmj59uVMem0SGyo3kBKXwrSh05g2bBqnpZ0W6dKMMeawLCDakNfnZf6G+bjFzbdP/zY9U3vSM7UnM0fPZOIZE4mPjo90icYY02phPRMqIuNEpERE1ovIPS3Mc6WIrBGRYhH5a9D060TkS+d1XTjrPFZfVX3F/R/dT+5vc7n4rxfzq3/9CvA/nvK9773HNQOvsXAwxnQ4YetBiIgbeBK4ECgFlojIXFVdEzRPL+Be4GxVrRSRLGd6GnA/UAAosNRZtjJc9R6tu967i8c/fRyAb5/+bX477rd2E5sx5oQQzkNMhcB6Vd0IICKzgQnAmqB5pgFPNm34VXWXM/3bwPuquttZ9n1gHPByGOttlaahL+4++27SE9IZ0X0E90Xfx/VDrufUlFMPvwJjjOkgwhkQOcDXQZ9LgTMPmKc3gIh8AriBmao6r4Vlcw78AhG5AbgBoEeP8D2GstZTy+trXmfWsll8vOVj3OLmnB7ncGn+pVx+xuVcfsblYftuY4yJlEifpI4CegGjgW7AIhEZ0NqFVfVp4GmAgoICDUeBlbWVnPbEaVTWVXJa6mk8csEjTB081Ya+MMac8MIZEFuB7kGfuznTgpUC/1ZVD7BJRNbhD4yt+EMjeNmFYas0SNPQF5urNvPwBQ+TGp/KnSPuZGT3kYzKHWV3OBtjThqiGpYdb0QkClgHXIB/g78EuEpVi4PmGQdMUdXrRCQDWA4MxjkxDTQ93HgZMKzpnEQoBQUFWlRUdFS1qir/3vpvZi2dxSvFr7Dfs58hXYbw7x/+m2h39FGt0xhjOgIRWaqqBaHawtaDUNVGEZkOzMd/fuE5VS0WkQeAIlWd67SNFZE1gBe4S1UrnKJ/gT9UAB44VDgcq99/9ntun3c7idGJNvSFMcY4wtaDON6OpQfx9Z6vmbd+HpP7Tw48lc0YY04GEelBdCTdk7szbdi0SJdhjDHtip1xNcYYE5IFhDHGmJAsIIwxxoRkAWGMMSYkCwhjjDEhWUAYY4wJyQLCGGNMSBYQxhhjQjph7qQWkTLgq2NYRQZQ3kblhFtHqhU6Vr0dqVboWPV2pFqhY9V7LLWeqqqZoRpOmIA4ViJS1NLt5u1NR6oVOla9HalW6Fj1dqRaoWPVG65a7RCTMcaYkCwgjDHGhGQB8Y2nI13AEehItULHqrcj1Qodq96OVCt0rHrDUqudgzDGGBOS9SCMMcaEZAFhjDEmpJM+IETkORHZJSKrI13L4YhIdxH5SETWiEixiPy/SNfUEhGJE5HPRGSFU+vPI13T4YiIW0SWi8jfI13L4YjIZhFZJSKfi8jRPUrxOBKRFBF5XUTWisgXIjIi0jWFIiL5zt+06bVXRO6IdF2HIiIznP+PrRaRl0Ukrs3WfbKfgxCR84Bq4M+q2j/S9RyKiGQD2aq6TEQ6AUuB76jqmgiXdhDxP9A7UVWrRSQa+Cfw/1T1XxEurUUi8p9AAdBZVcdHup5DEZHNQIGqdogbuUTkReBjVX1GRGKABFWtinRdhyIibmArcKaqHstNuGEjIjn4/7/VV1VrReRV4B1VfaEt1n/S9yBUdRGwO9J1tIaqblfVZc77fcAXQE5kqwpN/aqdj9HOq93ujYhIN+Bi4JlI13KiEZFk4DzgWQBVbWjv4eC4ANjQXsMhSBQQLyJRQAKwra1WfNIHREclIrnAEODfka2kZc4hm8+BXcD7qtpuawV+A/wY8EW6kFZS4D0RWSoiN0S6mMPIA8qA551DeM+ISGKki2qFycDLkS7iUFR1K/AYsAXYDuxR1ffaav0WEB2QiCQBbwB3qOreSNfTElX1qupgoBtQKCLt8hCeiIwHdqnq0kjXcgTOUdWhwH8AtzqHSturKGAo8JSqDgH2A/dEtqRDcw6DXQq8FulaDkVEUoEJ+EO4K5AoIte01fotIDoY53j+G8BLqvpmpOtpDedwwkfAuEjX0oKzgUud4/qzgfNF5H8jW9KhOXuOqOou4C2gMLIVHVIpUBrUg3wdf2C0Z/8BLFPVnZEu5DC+BWxS1TJV9QBvAiPbauUWEB2Ic+L3WeALVf1VpOs5FBHJFJEU5308cCGwNrJVhaaq96pqN1XNxX9Y4UNVbbO9sLYmIonORQo4h2rGAu32KjxV3QF8LSL5zqQLgHZ3YcUBptDODy85tgBniUiCs324AP+5yTZx0geEiLwMfArki0ipiPwg0jUdwtnA9/Dv4TZdhndRpItqQTbwkYisBJbgPwfR7i8f7SBOAf4pIiuAz4C3VXVehGs6nNuAl5z/PQwGHo5wPS1yQvdC/Hvj7ZrTK3sdWAaswr9Nb7NhN076y1yNMcaEdtL3IIwxxoRmAWGMMSYkCwhjjDEhWUAYY4wJyQLCGGNMSBYQxhyGiHgPGOGzze4CFpHcjjCSsDk5RUW6AGM6gFpnyBBjTirWgzDmKDnPZPhv57kMn4nI6c70XBH5UERWisgCEenhTD9FRN5ynpGxQkSahkRwi8gsZ0z/95w7zxGR251nf6wUkdkR+pnmJGYBYczhxR9wiOm7QW17VHUA8Hv8I8IC/A54UVUHAi8BTzjTnwD+oaqD8I9FVOxM7wU8qar9gCpgojP9HmCIs56bwvXjjGmJ3UltzGGISLWqJoWYvhk4X1U3OoMo7lDVdBEpx/9gJ48zfbuqZohIGdBNVeuD1pGLfxiSXs7nu4FoVX1QRObhf5jVHGBO0PM1jDkurAdhzLHRFt4fifqg916+OTd4MfAk/t7GEueBMMYcNxYQxhyb7wb9+6nzfjH+UWEBrgY+dt4vAG6GwMOUkltaqYi4gO6q+hFwN5AMHNSLMSacbI/EmMOLd56M12SeqjZd6prqjFBaj3+IaPCPXPq8iNyF/0lq33em/z/gaWfEYC/+sNjewne6gf91QkSAJzrIYzrNCcTOQRhzlJxzEAWqWh7pWowJBzvEZIwxJiTrQRhjjAnJehDGGGNCsoAwxhgTkgWEMcaYkCwgjDHGhGQBYYwxJqT/Dx3icwdzdXM9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZULT06H2EacZ"
      },
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.layers import Dense,Input,LSTM,Embedding,BatchNormalization,Conv1D,Dropout,GRU,Activation,Concatenate\n",
        "from keras.layers import Flatten,Bidirectional,GlobalAveragePooling1D,GlobalMaxPooling1D,MaxPool1D,SpatialDropout1D\n",
        "\n",
        "from keras.models import Model,load_model\n",
        "from keras import initializers ,regularizers,optimizers,callbacks\n",
        "from keras import backend as K\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping,ModelCheckpoint\n",
        "import tensorflow as tf\n",
        "from tensorflow.compat.v1.keras.layers import CuDNNGRU\n",
        "from tensorflow.compat.v1.keras.layers import CuDNNLSTM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVbPTqOVrw8h"
      },
      "source": [
        "#definig model 2\n",
        "def model_1(lr=0.0,lr_d=0.0,units=0,spatial_dr=0.0,kernel_size1=3,kernel_size2=2,dense_units=128,dr=0.1,conv_size=32):\n",
        "  saving_path='/content/best_model_1.hdf5'\n",
        "  file_path='/content/best_model_1.hdf5'\n",
        "  #defining callbacks\n",
        "  check_point=ModelCheckpoint(saving_path,monitor='val_loss',verbose=1,save_best_only=True,patience=3,mode=\"min\")\n",
        "  early_stop=EarlyStopping(monitor='val_loss',mode=\"min\")\n",
        "\n",
        "  #definig model structure\n",
        "  inp = Input(shape = (lenM,))\n",
        "  x=Embedding(len(list(unique_words))+1,300,weights=[embd_matrix],trainable=False,input_length=lenM)(inp)\n",
        "  x1 = SpatialDropout1D(spatial_dr)(x)\n",
        "\n",
        "  x_gru = Bidirectional(GRU(units, return_sequences = True))(x1)\n",
        "  x1 = Conv1D(conv_size, kernel_size=kernel_size1, padding='valid', kernel_initializer='he_uniform')(x_gru)\n",
        "  avg_pool1_gru = GlobalAveragePooling1D()(x1)\n",
        "  max_pool1_gru = GlobalMaxPooling1D()(x1)\n",
        "    \n",
        "  x3 = Conv1D(conv_size, kernel_size=kernel_size2, padding='valid', kernel_initializer='he_uniform')(x_gru)\n",
        "  avg_pool3_gru = GlobalAveragePooling1D()(x3)\n",
        "  max_pool3_gru = GlobalMaxPooling1D()(x3)\n",
        "    \n",
        "  x_lstm = Bidirectional(LSTM(units, return_sequences = True))(x1)\n",
        "  x1 = Conv1D(conv_size, kernel_size=kernel_size1, padding='valid', kernel_initializer='he_uniform')(x_lstm)\n",
        "  avg_pool1_lstm = GlobalAveragePooling1D()(x1)\n",
        "  max_pool1_lstm = GlobalMaxPooling1D()(x1)\n",
        "    \n",
        "  x3 = Conv1D(conv_size, kernel_size=kernel_size2, padding='valid', kernel_initializer='he_uniform')(x_lstm)\n",
        "  avg_pool3_lstm = GlobalAveragePooling1D()(x3)\n",
        "  max_pool3_lstm = GlobalMaxPooling1D()(x3)\n",
        "    \n",
        "    \n",
        "  x = Concatenate()([avg_pool1_gru, max_pool1_gru, avg_pool3_gru, max_pool3_gru,avg_pool1_lstm, max_pool1_lstm, avg_pool3_lstm, max_pool3_lstm])\n",
        "  x= BatchNormalization()(x)\n",
        "  x = Dropout(dr)(Dense(dense_units, activation='relu') (x))\n",
        "  x = BatchNormalization()(x)\n",
        "  x = Dropout(dr)(Dense(int(dense_units / 2), activation='relu') (x))\n",
        "  x = Dense(5, activation = \"softmax\")(x)\n",
        "  model = Model(inputs = inp, outputs = x)\n",
        "  model.compile(loss = \"categorical_crossentropy\", optimizer = Adam(lr = lr, decay = lr_d), metrics = [\"accuracy\"])\n",
        "  history = model.fit(Xtrain, ytrain, batch_size = 128, epochs = 20,validation_data=(Xval,yval), verbose = 1, callbacks = [check_point, early_stop])\n",
        " \n",
        "\n",
        "  model = load_model(file_path)\n",
        "  return model\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EgEV37hqsyC",
        "outputId": "ea2b23ce-b56a-430f-db93-da4320cc6fd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "model1=load_model(filepath='/content/best_model_1.hdf5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-77a1a87c8201>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/content/best_model_1.hdf5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/save.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0mfilepath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m       \u001b[0mloader_impl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model\u001b[0;34m(export_dir)\u001b[0m\n\u001b[1;32m    111\u001b[0m                   (export_dir,\n\u001b[1;32m    112\u001b[0m                    \u001b[0mconstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSAVED_MODEL_FILENAME_PBTXT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m                    constants.SAVED_MODEL_FILENAME_PB))\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: SavedModel file does not exist at: /content/best_model_1.hdf5/{saved_model.pbtxt|saved_model.pb}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sAmjLA7F8aU",
        "outputId": "9656ab7f-ba8e-461d-c10d-af08900b9357",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model1 =model_1(lr = 1e-3, lr_d = 1e-10, units = 64, spatial_dr = 0.3, kernel_size1=3, kernel_size2=2, dense_units=32, dr=0.1, conv_size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "683/683 [==============================] - ETA: 0s - loss: 1.0761 - accuracy: 0.5627\n",
            "Epoch 00001: val_loss improved from inf to 0.90475, saving model to /content/best_model_1.hdf5\n",
            "683/683 [==============================] - 139s 204ms/step - loss: 1.0761 - accuracy: 0.5627 - val_loss: 0.9047 - val_accuracy: 0.6266\n",
            "Epoch 2/20\n",
            "683/683 [==============================] - ETA: 0s - loss: 0.9510 - accuracy: 0.6097\n",
            "Epoch 00002: val_loss improved from 0.90475 to 0.90066, saving model to /content/best_model_1.hdf5\n",
            "683/683 [==============================] - 137s 201ms/step - loss: 0.9510 - accuracy: 0.6097 - val_loss: 0.9007 - val_accuracy: 0.6265\n",
            "Epoch 3/20\n",
            "683/683 [==============================] - ETA: 0s - loss: 0.9239 - accuracy: 0.6209\n",
            "Epoch 00003: val_loss improved from 0.90066 to 0.87019, saving model to /content/best_model_1.hdf5\n",
            "683/683 [==============================] - 137s 200ms/step - loss: 0.9239 - accuracy: 0.6209 - val_loss: 0.8702 - val_accuracy: 0.6366\n",
            "Epoch 4/20\n",
            "683/683 [==============================] - ETA: 0s - loss: 0.9027 - accuracy: 0.6307\n",
            "Epoch 00004: val_loss did not improve from 0.87019\n",
            "683/683 [==============================] - 137s 201ms/step - loss: 0.9027 - accuracy: 0.6307 - val_loss: 0.8727 - val_accuracy: 0.6382\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0M3wefBWVcD"
      },
      "source": [
        "##defining model 3\n",
        "def model_2(lr=0.0,lr_d=0.0,units=0,sp_dr=0.0,kernel_1=3,kernel_2=2,dense_units=128,dr=0.1,conv_size=32):\n",
        "  save_to='/content/drive/My Drive/REVIEW CLASSIFIER'\n",
        "  #callbacks\n",
        "  check=ModelCheckpoint(save_to,monitor='val_loss',verbose=1,mode='min',save_best_only=True)\n",
        "  early_stop=EarlyStopping(monitor='val_loss',mode='min',patience=2)\n",
        "  #model structure\n",
        "  inp = Input(shape = (lenM,))\n",
        "  x=Embedding(len(list(unique_words))+1,300,weights=[embd_matrix],trainable=False,input_length=lenM)(inp)\n",
        "  x1 = SpatialDropout1D(sp_dr)(x)\n",
        "  xGRU=Bidirectional(GRU(units,return_sequences=True))(x1)\n",
        "  xLSTM=Bidirectional(LSTM(units,return_sequences=True))(x1)\n",
        "  \n",
        "  xCONV1=Conv1D(conv_size,kernel_size=kernel_1,padding='valid',kernel_initializer='he_uniform')\n",
        "  xCONV2=Conv1D(conv_size,kernel_size=kernel_2,padding='valid',kernel_initializer='he_uniform')\n",
        "\n",
        "  GRU1=Conv1D(conv_size,kernel_size=kernel_1,padding='valid',kernel_initializer='he_uniform')(xGRU)\n",
        "  GRU2=Conv1D(conv_size,kernel_size=kernel_2,padding='valid',kernel_initializer='he_uniform')(xGRU)\n",
        "\n",
        "  LSTM1=Conv1D(conv_size,kernel_size=kernel_2,padding='valid',kernel_initializer='he_uniform')(xLSTM)\n",
        "  LSTM2=Conv1D(conv_size,kernel_size=kernel_2,padding='valid',kernel_initializer='he_uniform')(xLSTM)\n",
        "\n",
        "  avg_pool=GlobalAveragePooling1D()\n",
        "  max_pool= GlobalMaxPooling1D()\n",
        "\n",
        "  avg_gru_1=GlobalAveragePooling1D()(GRU1)\n",
        "  max_gru_1=GlobalMaxPooling1D()(GRU1)\n",
        "  avg_gru_2=GlobalAveragePooling1D()(xCONV2)(GRU2)\n",
        "  max_gru_2=GlobalMaxPooling1D()(xCONV2)(GRU2)\n",
        "\n",
        "  avg_lstm_1=GlobalAveragePooling1D()(LSTM1)\n",
        "  max_lstm_1=GlobalMaxPooling1D()(LSTM1)\n",
        "  avg_lstm_2=GlobalAveragePooling1D()(LSTM2)\n",
        "  max_lstm_2=GlobalMaxPooling1D()(LSTM2)\n",
        "\n",
        "  #concatinating lstms and grus\n",
        "\n",
        "  x = Concatenate()([avg_gru_1,avg_gru_2,avg_lstm_1,avg_lstm_2,max_gru_1,max_gru_2,max_lstm_1,max_lstm_2])\n",
        "  x= BatchNormalization()(x)\n",
        "  x= Dropout(dr)(Dense(dense_units,activation='relu')(x))\n",
        "  x= BatchNormalization()(x)\n",
        "  x = Dropout(dr)(Dense(int(dense_units/2),activation='relu')(x))\n",
        "  x=Dense(5,activation='sigmoid')(x)\n",
        "  model=Model(inputs=inp,outputs=x)\n",
        "  model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=lr,decay=lr_d),metrics=['accuracy'])\n",
        "  history=model.fit(Xtrain, ytrain, batch_size = 128, epochs = 20,validation_data=(Xval,yval), verbose = 1, callbacks = [check, early_stop])\n",
        "  model=load_model(save_to)\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntzJNISjc_RE"
      },
      "source": [
        "def build_model2(lr=0.0, lr_d=0.0, units=0, spatial_dr=0.0, kernel_size1=3, kernel_size2=2, dense_units=128, dr=0.1, conv_size=32):\n",
        "    file_path = \"best_model.hdf5\"\n",
        "    check_point = ModelCheckpoint(file_path, monitor = \"val_loss\", verbose = 1,\n",
        "                                  save_best_only = True, mode = \"min\")\n",
        "    early_stop = EarlyStopping(monitor = \"val_loss\", mode = \"min\", patience = 3)\n",
        "    inp = Input(shape = (lenM,))\n",
        "    x=Embedding(len(list(unique_words))+1,300,weights=[embd_matrix],trainable=False,input_length=lenM)(inp)\n",
        "    x1 = SpatialDropout1D(spatial_dr)(x)\n",
        "\n",
        "    x_gru = Bidirectional(GRU(units, return_sequences = True))(x1)\n",
        "    x_lstm = Bidirectional(LSTM(units, return_sequences = True))(x1)\n",
        "    \n",
        "    x_conv1 = Conv1D(conv_size, kernel_size=kernel_size1, padding='valid', kernel_initializer='he_uniform')(x_gru)\n",
        "    avg_pool1_gru = GlobalAveragePooling1D()(x_conv1)\n",
        "    max_pool1_gru = GlobalMaxPooling1D()(x_conv1)\n",
        "    \n",
        "    x_conv2 = Conv1D(conv_size, kernel_size=kernel_size2, padding='valid', kernel_initializer='he_uniform')(x_gru)\n",
        "    avg_pool2_gru = GlobalAveragePooling1D()(x_conv2)\n",
        "    max_pool2_gru = GlobalMaxPooling1D()(x_conv2)\n",
        "    x_conv3 = Conv1D(conv_size, kernel_size=kernel_size1, padding='valid', kernel_initializer='he_uniform')(x_lstm)\n",
        "    avg_pool1_lstm = GlobalAveragePooling1D()(x_conv3)\n",
        "    max_pool1_lstm = GlobalMaxPooling1D()(x_conv3)\n",
        "    \n",
        "    x_conv4 = Conv1D(conv_size, kernel_size=kernel_size2, padding='valid', kernel_initializer='he_uniform')(x_lstm)\n",
        "    avg_pool2_lstm = GlobalAveragePooling1D()(x_conv4)\n",
        "    max_pool2_lstm = GlobalMaxPooling1D()(x_conv4)\n",
        "    \n",
        "    \n",
        "    x = Concatenate()([avg_pool1_gru, max_pool1_gru, avg_pool2_gru, max_pool2_gru,\n",
        "                    avg_pool1_lstm, max_pool1_lstm, avg_pool2_lstm, max_pool2_lstm])\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Dropout(dr)(Dense(dense_units, activation='relu') (x))\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Dropout(dr)(Dense(int(dense_units / 2), activation='relu') (x))\n",
        "    x = Dense(5, activation = \"sigmoid\")(x)\n",
        "    model = Model(inputs = inp, outputs = x)\n",
        "    model.compile(loss = \"binary_crossentropy\", optimizer = Adam(lr = lr, decay = lr_d), metrics = [\"accuracy\"])\n",
        "    history = model.fit(Xtrain, ytrain, batch_size = 128, epochs = 20, validation_split=0.1, \n",
        "                        verbose = 1, callbacks = [check_point, early_stop])\n",
        "    model = load_model(file_path)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BSxzku0dKMs",
        "outputId": "21ee7279-54f4-42ed-9bf8-e4acea543b88",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model4 = build_model2(lr = 1e-3, lr_d = 0, units = 64, spatial_dr = 0.5, kernel_size1=3, kernel_size2=3, dense_units=64, dr=0.3, conv_size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3980 - accuracy: 0.5359\n",
            "Epoch 00001: val_loss improved from inf to 0.31812, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 153s 248ms/step - loss: 0.3980 - accuracy: 0.5359 - val_loss: 0.3181 - val_accuracy: 0.6152\n",
            "Epoch 2/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3425 - accuracy: 0.5894\n",
            "Epoch 00002: val_loss improved from 0.31812 to 0.31275, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 153s 248ms/step - loss: 0.3425 - accuracy: 0.5894 - val_loss: 0.3128 - val_accuracy: 0.6239\n",
            "Epoch 3/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3327 - accuracy: 0.6015\n",
            "Epoch 00003: val_loss improved from 0.31275 to 0.30529, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 152s 248ms/step - loss: 0.3327 - accuracy: 0.6015 - val_loss: 0.3053 - val_accuracy: 0.6344\n",
            "Epoch 4/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3266 - accuracy: 0.6099\n",
            "Epoch 00004: val_loss did not improve from 0.30529\n",
            "615/615 [==============================] - 154s 251ms/step - loss: 0.3266 - accuracy: 0.6099 - val_loss: 0.3093 - val_accuracy: 0.6324\n",
            "Epoch 5/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3216 - accuracy: 0.6155\n",
            "Epoch 00005: val_loss improved from 0.30529 to 0.29967, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 155s 252ms/step - loss: 0.3216 - accuracy: 0.6155 - val_loss: 0.2997 - val_accuracy: 0.6431\n",
            "Epoch 6/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3178 - accuracy: 0.6202\n",
            "Epoch 00006: val_loss did not improve from 0.29967\n",
            "615/615 [==============================] - 152s 247ms/step - loss: 0.3178 - accuracy: 0.6202 - val_loss: 0.3030 - val_accuracy: 0.6399\n",
            "Epoch 7/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3135 - accuracy: 0.6273\n",
            "Epoch 00007: val_loss improved from 0.29967 to 0.29873, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 153s 249ms/step - loss: 0.3135 - accuracy: 0.6273 - val_loss: 0.2987 - val_accuracy: 0.6501\n",
            "Epoch 8/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3096 - accuracy: 0.6312\n",
            "Epoch 00008: val_loss improved from 0.29873 to 0.29216, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 154s 251ms/step - loss: 0.3096 - accuracy: 0.6312 - val_loss: 0.2922 - val_accuracy: 0.6563\n",
            "Epoch 9/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3066 - accuracy: 0.6384\n",
            "Epoch 00009: val_loss improved from 0.29216 to 0.29198, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 153s 250ms/step - loss: 0.3066 - accuracy: 0.6384 - val_loss: 0.2920 - val_accuracy: 0.6523\n",
            "Epoch 10/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3034 - accuracy: 0.6419\n",
            "Epoch 00010: val_loss improved from 0.29198 to 0.28981, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.3034 - accuracy: 0.6419 - val_loss: 0.2898 - val_accuracy: 0.6564\n",
            "Epoch 11/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.3018 - accuracy: 0.6456\n",
            "Epoch 00011: val_loss did not improve from 0.28981\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.3018 - accuracy: 0.6456 - val_loss: 0.2915 - val_accuracy: 0.6611\n",
            "Epoch 12/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2974 - accuracy: 0.6511\n",
            "Epoch 00012: val_loss improved from 0.28981 to 0.28674, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 155s 253ms/step - loss: 0.2974 - accuracy: 0.6511 - val_loss: 0.2867 - val_accuracy: 0.6619\n",
            "Epoch 13/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2958 - accuracy: 0.6527\n",
            "Epoch 00013: val_loss improved from 0.28674 to 0.28624, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.2958 - accuracy: 0.6527 - val_loss: 0.2862 - val_accuracy: 0.6674\n",
            "Epoch 14/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2933 - accuracy: 0.6555\n",
            "Epoch 00014: val_loss did not improve from 0.28624\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.2933 - accuracy: 0.6555 - val_loss: 0.2863 - val_accuracy: 0.6619\n",
            "Epoch 15/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2907 - accuracy: 0.6597\n",
            "Epoch 00015: val_loss improved from 0.28624 to 0.28389, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 155s 251ms/step - loss: 0.2907 - accuracy: 0.6597 - val_loss: 0.2839 - val_accuracy: 0.6716\n",
            "Epoch 16/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2883 - accuracy: 0.6634\n",
            "Epoch 00016: val_loss did not improve from 0.28389\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.2883 - accuracy: 0.6634 - val_loss: 0.2852 - val_accuracy: 0.6688\n",
            "Epoch 17/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2874 - accuracy: 0.6657\n",
            "Epoch 00017: val_loss did not improve from 0.28389\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.2874 - accuracy: 0.6657 - val_loss: 0.2847 - val_accuracy: 0.6658\n",
            "Epoch 18/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2843 - accuracy: 0.6716\n",
            "Epoch 00018: val_loss improved from 0.28389 to 0.28286, saving model to best_model.hdf5\n",
            "615/615 [==============================] - 154s 250ms/step - loss: 0.2843 - accuracy: 0.6716 - val_loss: 0.2829 - val_accuracy: 0.6706\n",
            "Epoch 19/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2823 - accuracy: 0.6739\n",
            "Epoch 00019: val_loss did not improve from 0.28286\n",
            "615/615 [==============================] - 154s 251ms/step - loss: 0.2823 - accuracy: 0.6739 - val_loss: 0.2873 - val_accuracy: 0.6611\n",
            "Epoch 20/20\n",
            "615/615 [==============================] - ETA: 0s - loss: 0.2820 - accuracy: 0.6735\n",
            "Epoch 00020: val_loss did not improve from 0.28286\n",
            "615/615 [==============================] - 153s 248ms/step - loss: 0.2820 - accuracy: 0.6735 - val_loss: 0.2841 - val_accuracy: 0.6685\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLh87ID8gm1k",
        "outputId": "e44376ef-9748-47ee-8393-a520c755ba97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "model4 = model_2(lr = 1e-3, lr_d = 0, units = 64, sp_dr = 0.5, kernel_1=3, kernel_2=3, dense_units=64, dr=0.3, conv_size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-ec9792502671>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr_d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msp_dr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdense_units\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconv_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-56-5dbdc8fd8fc1>\u001b[0m in \u001b[0;36mmodel_2\u001b[0;34m(lr, lr_d, units, sp_dr, kernel_1, kernel_2, dense_units, dr, conv_size)\u001b[0m\n\u001b[1;32m     26\u001b[0m   \u001b[0mavg_gru_1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGlobalAveragePooling1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGRU1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m   \u001b[0mmax_gru_1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGlobalMaxPooling1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGRU1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m   \u001b[0mavg_gru_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGlobalAveragePooling1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxCONV2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGRU2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m   \u001b[0mmax_gru_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGlobalMaxPooling1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxCONV2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGRU2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    980\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope_v2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 982\u001b[0;31m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    983\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_auto_cast_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_maybe_build\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2616\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2617\u001b[0m       input_spec.assert_input_compatibility(\n\u001b[0;32m-> 2618\u001b[0;31m           self.input_spec, inputs, self.name)\n\u001b[0m\u001b[1;32m   2619\u001b[0m       \u001b[0minput_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2620\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0minput_list\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype_policy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_dtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    164\u001b[0m         \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin_ndim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         spec.max_ndim is not None):\n\u001b[0;32m--> 166\u001b[0;31m       \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndims\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    167\u001b[0m         raise ValueError('Input ' + str(input_index) + ' of layer ' +\n\u001b[1;32m    168\u001b[0m                          \u001b[0mlayer_name\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' is incompatible with the layer: '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Conv1D' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sKlNR5NNJF6",
        "outputId": "4cd3ee04-bb68-4450-8de0-f207961d5b87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        " \n",
        "PRED_1=model.predict(Xtest,batch_size=1024,verbose=1)\n",
        "PRED_2=model1.predict(Xtest,batch_size=1024,verbose=1)\n",
        "PRED_3=model4.predict(Xtest,batch_size=1024,verbose=1)\n",
        "#\n",
        "#"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "46/46 [==============================] - 18s 389ms/step\n",
            "46/46 [==============================] - 20s 433ms/step\n",
            "46/46 [==============================] - 29s 620ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bch9kyQzw0MM",
        "outputId": "2bc00dfb-6a22-4f83-a8d0-cd6d972aa71b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "pred=np.add(PRED_1,PRED_3)\n",
        "\n",
        "prediction=np.round(np.argmax(pred,axis=1)).astype(int)\n",
        "sub=pd.read_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv')\n",
        "sub['Sentiment']=prediction\n",
        "sub.to_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv', index=False)\n",
        "sub.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  Sentiment\n",
              "0   1          2\n",
              "1   4          2\n",
              "2   5          2\n",
              "3  12          2\n",
              "4  14          2\n",
              "5  15          3\n",
              "6  19          3\n",
              "7  20          2\n",
              "8  32          1\n",
              "9  33          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5PGNsy9uU8b",
        "outputId": "43761f25-b2d5-4c26-f0e2-6583ae3eb897",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "pred=np.add(PRED_1,PRED_2)\n",
        "pred=np.add(pred,PRED_3)\n",
        "prediction=np.round(np.argmax(pred,axis=1)).astype(int)\n",
        "sub=pd.read_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv')\n",
        "sub['Sentiment']=prediction\n",
        "sub.to_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv', index=False)\n",
        "sub.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>33</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  Sentiment\n",
              "0   1          2\n",
              "1   4          2\n",
              "2   5          2\n",
              "3  12          2\n",
              "4  14          2\n",
              "5  15          3\n",
              "6  19          3\n",
              "7  20          2\n",
              "8  32          2\n",
              "9  33          2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TegFyrTYkAZY",
        "outputId": "402cdbd0-96e3-4e29-ae44-0f7e855cd3f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "\n",
        "prediction=np.round(np.argmax(PRED_1,axis=1)).astype(int)\n",
        "sub=pd.read_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv')\n",
        "sub['Sentiment']=prediction\n",
        "sub.to_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv', index=False)\n",
        "sub.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  Sentiment\n",
              "0   1          2\n",
              "1   4          2\n",
              "2   5          2\n",
              "3  12          2\n",
              "4  14          2\n",
              "5  15          3\n",
              "6  19          3\n",
              "7  20          2\n",
              "8  32          1\n",
              "9  33          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EEvk8ROVwCq",
        "outputId": "0e50699f-d04a-4568-e35b-98b2a488ab5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "PRED_1.shape\n",
        "pred=np.add(PRED_1,PRED_2)\n",
        "pred.shape\n",
        "prediction=np.round(np.argmax(pred,axis=1)).astype(int)\n",
        "sub=pd.read_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv')\n",
        "sub['Sentiment']=prediction\n",
        "sub.to_csv('/content/drive/My Drive/REVIEW CLASSIFIER/sample_submission.csv', index=False)\n",
        "sub.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  Sentiment\n",
              "0   1          2\n",
              "1   4          2\n",
              "2   5          2\n",
              "3  12          2\n",
              "4  14          2\n",
              "5  15          3\n",
              "6  19          3\n",
              "7  20          2\n",
              "8  32          1\n",
              "9  33          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-UlyeqFfICCe"
      },
      "source": [
        "model1=build_model()\n",
        "model1.summary()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jins7FWtTbHS"
      },
      "source": [
        "hist2=model2.fit(Xtrain,ytrain,32,validation_data=(Xval,yval))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzt6luPgOQ8v"
      },
      "source": [
        "hist_2=model2.fit(Xtrain,ytrain,batch_size=32,epochs=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwJRhCpPqtzl"
      },
      "source": [
        "hist_2=model2.fit(Xtrain,ytrain,batch_size=256,verbose=1,epochs=5,validation_data=(Xval,yval))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nhLcqTArx_t3"
      },
      "source": [
        "ep=range(1,6)\n",
        "plt.plot(ep,hist_2.history['val_loss'],'b--')\n",
        "plt.plot(ep,hist_2.history['loss'],'r--')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}