Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4dcc023a-37c6-4f24-8282-fd8070664ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from random import shuffle\n",
    "import re\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13f83be2-55b9-4b1f-87e4-307a74666585",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"hin.txt\",sep=\"\\t\",header=None)\n",
    "data.columns = [\"english_sentence\",\"hindi_sentence\",\"extra\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6a1c4316-c186-4af5-9437-0b305214c189",
   "metadata": {},
   "outputs": [
    {
     "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>english_sentence</th>\n",
       "      <th>hindi_sentence</th>\n",
       "      <th>extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wow!</td>\n",
       "      <td>वाह!</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Help!</td>\n",
       "      <td>बचाओ!</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jump.</td>\n",
       "      <td>उछलो.</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jump.</td>\n",
       "      <td>कूदो.</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jump.</td>\n",
       "      <td>छलांग.</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hello!</td>\n",
       "      <td>नमस्ते।</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hello!</td>\n",
       "      <td>नमस्कार।</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cheers!</td>\n",
       "      <td>वाह-वाह!</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cheers!</td>\n",
       "      <td>चियर्स!</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Got it?</td>\n",
       "      <td>समझे कि नहीं?</td>\n",
       "      <td>CC-BY 2.0 (France) Attribution: tatoeba.org #4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  english_sentence hindi_sentence  \\\n",
       "0             Wow!           वाह!   \n",
       "1            Help!          बचाओ!   \n",
       "2            Jump.          उछलो.   \n",
       "3            Jump.          कूदो.   \n",
       "4            Jump.         छलांग.   \n",
       "5           Hello!        नमस्ते।   \n",
       "6           Hello!       नमस्कार।   \n",
       "7          Cheers!       वाह-वाह!   \n",
       "8          Cheers!        चियर्स!   \n",
       "9          Got it?  समझे कि नहीं?   \n",
       "\n",
       "                                               extra  \n",
       "0  CC-BY 2.0 (France) Attribution: tatoeba.org #5...  \n",
       "1  CC-BY 2.0 (France) Attribution: tatoeba.org #4...  \n",
       "2  CC-BY 2.0 (France) Attribution: tatoeba.org #6...  \n",
       "3  CC-BY 2.0 (France) Attribution: tatoeba.org #6...  \n",
       "4  CC-BY 2.0 (France) Attribution: tatoeba.org #6...  \n",
       "5  CC-BY 2.0 (France) Attribution: tatoeba.org #3...  \n",
       "6  CC-BY 2.0 (France) Attribution: tatoeba.org #3...  \n",
       "7  CC-BY 2.0 (France) Attribution: tatoeba.org #4...  \n",
       "8  CC-BY 2.0 (France) Attribution: tatoeba.org #4...  \n",
       "9  CC-BY 2.0 (France) Attribution: tatoeba.org #4...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "956d7b79-6a0e-482d-89b9-6a3c7cfc2476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                    वाह!\n",
       "1                                                   बचाओ!\n",
       "2                                                   उछलो.\n",
       "3                                                   कूदो.\n",
       "4                                                  छलांग.\n",
       "                              ...                        \n",
       "2910    उस सूपरमार्केट में तुम लगभग कोई भी रोजाने में ...\n",
       "2911    जिन यात्रियों को दुर्घटना मे चोट आई थी उन्हे अ...\n",
       "2912    लोकतंत्र सरकार का सबसे घिनौना रूप है, अगर बाकी...\n",
       "2913    अगर मेरा बेटा ट्रेफ़िक हादसे में नहीं मारा गया...\n",
       "2914    जब मैं बच्चा था, मुझे कीड़ों को छूने से कोई पर...\n",
       "Name: hindi_sentence, Length: 2915, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"english_sentence\"].apply(lambda x: x.lower())\n",
    "data[\"hindi_sentence\"].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "763d607c-c6bf-4bb3-a8ea-9d20e047591b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                    वाह!\n",
       "1                                                   बचाओ!\n",
       "2                                                   उछलो.\n",
       "3                                                   कूदो.\n",
       "4                                                  छलांग.\n",
       "                              ...                        \n",
       "2910    उस सूपरमार्केट में तुम लगभग कोई भी रोजाने में ...\n",
       "2911    जिन यात्रियों को दुर्घटना मे चोट आई थी उन्हे अ...\n",
       "2912    लोकतंत्र सरकार का सबसे घिनौना रूप है, अगर बाकी...\n",
       "2913    अगर मेरा बेटा ट्रेफ़िक हादसे में नहीं मारा गया...\n",
       "2914    जब मैं बच्चा था, मुझे कीड़ों को छूने से कोई पर...\n",
       "Name: hindi_sentence, Length: 2915, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"english_sentence\"].apply(lambda x: re.sub(\"''\",'',x))\n",
    "data[\"hindi_sentence\"].apply(lambda x: re.sub(\"''\",'',x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c357db92-4f03-42ef-8ad7-6e069d773a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "exclude = set(string.punctuation) # Set of all special characters\n",
    "# Remove all the special characters\n",
    "data['english_sentence']=data['english_sentence'].apply(lambda x: ''.join(ch for ch in x if ch not in exclude))\n",
    "data['hindi_sentence']=data['hindi_sentence'].apply(lambda x: ''.join(ch for ch in x if ch not in exclude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6aae30f3-046d-4d01-9794-9fff226deed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all numbers from text\n",
    "remove_digits = str.maketrans('', '', string.digits)\n",
    "data['english_sentence']=data['english_sentence'].apply(lambda x: x.translate(remove_digits))\n",
    "data['hindi_sentence']=data['hindi_sentence'].apply(lambda x: x.translate(remove_digits))\n",
    "\n",
    "data['hindi_sentence'] = data['hindi_sentence'].apply(lambda x: re.sub(\"[२३०८१५७९४६]\", \"\", x))\n",
    "\n",
    "# Remove extra spaces\n",
    "data['english_sentence']=data['english_sentence'].apply(lambda x: x.strip())\n",
    "data['hindi_sentence']=data['hindi_sentence'].apply(lambda x: x.strip())\n",
    "data['english_sentence']=data['english_sentence'].apply(lambda x: re.sub(\" +\", \" \", x))\n",
    "data['hindi_sentence']=data['hindi_sentence'].apply(lambda x: re.sub(\" +\", \" \", x))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a2af94f8-245c-4245-b089-521c5cc50639",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8b6f75e-5a38-4139-8986-0185027e4b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['hindi_sentence'] = data['hindi_sentence'].apply(lambda x : 'START_ '+ x + ' _END')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4367761-de57-462d-9d42-fc61c4244890",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get English and Hindi Vocabulary\n",
    "all_eng_words=set()\n",
    "for eng in data['english_sentence']:\n",
    "    for word in eng.split():\n",
    "        if word not in all_eng_words:\n",
    "            all_eng_words.add(word)\n",
    "\n",
    "all_hindi_words=set()\n",
    "for hin in data['hindi_sentence']:\n",
    "    for word in hin.split():\n",
    "        if word not in all_hindi_words:\n",
    "            all_hindi_words.add(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f49db38-2847-4019-8851-ccb75679c637",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vocab = len(all_eng_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a156709-9c3e-4c91-9df1-484c26f0aed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_vocab = len(all_hindi_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f3f53d28-12e9-40ee-9375-f6ccef3fd63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAX_ENG_LEN = 0\n",
    "for i in data[\"english_sentence\"]:\n",
    "    if len(i.split())>MAX_ENG_LEN:\n",
    "        MAX_ENG_LEN = len(i.split())\n",
    "MAX_ENG_LEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "76fe74b2-46e2-4996-ab44-66ecc27b9690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAX_HIN_LEN = 0\n",
    "for i in data[\"hindi_sentence\"]:\n",
    "    if len(i.split())>MAX_HIN_LEN:\n",
    "        MAX_HIN_LEN = len(i.split())\n",
    "MAX_HIN_LEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61c49208-bc97-4e52-b1c8-a8812168a3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2915, 22)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "x_tokenizer = Tokenizer(num_words=x_vocab)\n",
    "x_tokenizer.fit_on_texts(data[\"english_sentence\"].values)\n",
    "X = x_tokenizer.texts_to_sequences(data[\"english_sentence\"].values)\n",
    "X = pad_sequences(X, maxlen=MAX_ENG_LEN, padding=\"post\")\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f3d8ece-ea13-4c5b-a327-c2c84646c105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2915, 27)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_tokenizer = Tokenizer(num_words=y_vocab)\n",
    "y_tokenizer.fit_on_texts(data[\"hindi_sentence\"].values)\n",
    "Y = y_tokenizer.texts_to_sequences(data[\"hindi_sentence\"].values)\n",
    "Y = pad_sequences(Y,maxlen=MAX_HIN_LEN, padding=\"post\")\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4ab27f40-38ce-4d27-a49d-cb08705470b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(X, Y, train_size=0.1, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c240c8ea-bc93-468e-9ff1-4d7fae2ff9c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import LSTM, Embedding, Dense, TimeDistributed\n",
    "from tensorflow.keras import Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "#making encoder model\n",
    "embedded_dim = 100\n",
    "latent_dim = 300\n",
    "\n",
    "encoder_input = Input(shape=(MAX_ENG_LEN,))\n",
    "encoder_emb_layer = Embedding(x_vocab, embedded_dim)\n",
    "encoder_emb = encoder_emb_layer(encoder_input)\n",
    "encoder_lstm = LSTM(latent_dim, recurrent_dropout=0.4, dropout=0.4, return_sequences=True, return_state=True)\n",
    "encoder_output, state_h, state_c = encoder_lstm(encoder_emb)\n",
    "\n",
    "#decoding layer\n",
    "decoder_input = Input(shape=(None,))\n",
    "decoder_emb_layer = Embedding(y_vocab, embedded_dim)\n",
    "decoder_emb = decoder_emb_layer(decoder_input)\n",
    "decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)\n",
    "decoder_output, decoder_state_h, decoder_state_c = decoder_lstm(decoder_emb, initial_state=[state_h, state_c])\n",
    "\n",
    "decoder_dense = TimeDistributed(Dense(y_vocab, activation=\"softmax\"))\n",
    "decoder_output = decoder_dense(decoder_output)\n",
    "\n",
    "model = Model(inputs=[encoder_input, decoder_input], outputs=decoder_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d08dd258-f7cb-413b-89d2-0111c3918a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d7cf713f-e99e-4dac-97e6-331136a66a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.9325 - accuracy: 0.6895 - val_loss: 2.1385 - val_accuracy: 0.7063\n",
      "Epoch 2/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.9886 - accuracy: 0.6910 - val_loss: 2.0393 - val_accuracy: 0.7054\n",
      "Epoch 3/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8977 - accuracy: 0.6916 - val_loss: 2.0657 - val_accuracy: 0.7077\n",
      "Epoch 4/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8781 - accuracy: 0.6920 - val_loss: 2.0406 - val_accuracy: 0.7080\n",
      "Epoch 5/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8622 - accuracy: 0.6934 - val_loss: 2.0677 - val_accuracy: 0.7093\n",
      "Epoch 6/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8835 - accuracy: 0.6883 - val_loss: 2.0452 - val_accuracy: 0.7087\n",
      "Epoch 7/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8235 - accuracy: 0.6931 - val_loss: 2.0632 - val_accuracy: 0.7045\n",
      "Epoch 8/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8113 - accuracy: 0.6942 - val_loss: 2.0825 - val_accuracy: 0.7092\n",
      "Epoch 9/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8347 - accuracy: 0.6907 - val_loss: 2.1290 - val_accuracy: 0.7015\n",
      "Epoch 10/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8060 - accuracy: 0.6956 - val_loss: 2.0591 - val_accuracy: 0.7120\n",
      "Epoch 11/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8061 - accuracy: 0.6931 - val_loss: 2.2064 - val_accuracy: 0.6742\n",
      "Epoch 12/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.8225 - accuracy: 0.6886 - val_loss: 2.0660 - val_accuracy: 0.7115\n",
      "Epoch 13/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.7475 - accuracy: 0.6953 - val_loss: 2.0660 - val_accuracy: 0.7079\n",
      "Epoch 14/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.7337 - accuracy: 0.6975 - val_loss: 2.0634 - val_accuracy: 0.7118\n",
      "Epoch 15/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.7214 - accuracy: 0.7006 - val_loss: 2.1873 - val_accuracy: 0.6746\n",
      "Epoch 16/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.7860 - accuracy: 0.6905 - val_loss: 2.0707 - val_accuracy: 0.7130\n",
      "Epoch 17/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6922 - accuracy: 0.6985 - val_loss: 2.0623 - val_accuracy: 0.7117\n",
      "Epoch 18/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6676 - accuracy: 0.7042 - val_loss: 2.0643 - val_accuracy: 0.7143\n",
      "Epoch 19/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6766 - accuracy: 0.7008 - val_loss: 2.1703 - val_accuracy: 0.6789\n",
      "Epoch 20/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.7480 - accuracy: 0.6901 - val_loss: 2.0541 - val_accuracy: 0.7184\n",
      "Epoch 21/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6322 - accuracy: 0.7045 - val_loss: 2.0416 - val_accuracy: 0.7197\n",
      "Epoch 22/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6148 - accuracy: 0.7068 - val_loss: 2.0358 - val_accuracy: 0.7228\n",
      "Epoch 23/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6008 - accuracy: 0.7083 - val_loss: 2.0816 - val_accuracy: 0.7178\n",
      "Epoch 24/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6473 - accuracy: 0.6961 - val_loss: 2.1867 - val_accuracy: 0.6787\n",
      "Epoch 25/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.6841 - accuracy: 0.7008 - val_loss: 2.0340 - val_accuracy: 0.7241\n",
      "Epoch 26/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5798 - accuracy: 0.7199 - val_loss: 2.0378 - val_accuracy: 0.7267\n",
      "Epoch 27/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5659 - accuracy: 0.7219 - val_loss: 2.0417 - val_accuracy: 0.7266\n",
      "Epoch 28/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5638 - accuracy: 0.7227 - val_loss: 2.0548 - val_accuracy: 0.7277\n",
      "Epoch 29/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5516 - accuracy: 0.7240 - val_loss: 2.0444 - val_accuracy: 0.7280\n",
      "Epoch 30/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5441 - accuracy: 0.7240 - val_loss: 2.0830 - val_accuracy: 0.7282\n",
      "Epoch 31/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5513 - accuracy: 0.7232 - val_loss: 2.0603 - val_accuracy: 0.7222\n",
      "Epoch 32/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5578 - accuracy: 0.7230 - val_loss: 2.0571 - val_accuracy: 0.7299\n",
      "Epoch 33/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5240 - accuracy: 0.7265 - val_loss: 2.0594 - val_accuracy: 0.7302\n",
      "Epoch 34/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5208 - accuracy: 0.7284 - val_loss: 2.0940 - val_accuracy: 0.7292\n",
      "Epoch 35/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5315 - accuracy: 0.7209 - val_loss: 2.0596 - val_accuracy: 0.7307\n",
      "Epoch 36/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5134 - accuracy: 0.7306 - val_loss: 2.0883 - val_accuracy: 0.7327\n",
      "Epoch 37/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.5094 - accuracy: 0.7288 - val_loss: 2.0729 - val_accuracy: 0.7298\n",
      "Epoch 38/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4919 - accuracy: 0.7314 - val_loss: 2.1026 - val_accuracy: 0.7321\n",
      "Epoch 39/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4872 - accuracy: 0.7288 - val_loss: 2.0847 - val_accuracy: 0.7247\n",
      "Epoch 40/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4959 - accuracy: 0.7300 - val_loss: 2.1052 - val_accuracy: 0.7371\n",
      "Epoch 41/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4776 - accuracy: 0.7314 - val_loss: 2.0830 - val_accuracy: 0.7368\n",
      "Epoch 42/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4662 - accuracy: 0.7322 - val_loss: 2.1047 - val_accuracy: 0.7356\n",
      "Epoch 43/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4594 - accuracy: 0.7325 - val_loss: 2.1007 - val_accuracy: 0.7314\n",
      "Epoch 44/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4730 - accuracy: 0.7320 - val_loss: 2.1126 - val_accuracy: 0.7406\n",
      "Epoch 45/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4507 - accuracy: 0.7341 - val_loss: 2.0955 - val_accuracy: 0.7352\n",
      "Epoch 46/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4438 - accuracy: 0.7330 - val_loss: 2.1165 - val_accuracy: 0.7392\n",
      "Epoch 47/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4406 - accuracy: 0.7339 - val_loss: 2.1019 - val_accuracy: 0.7382\n",
      "Epoch 48/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4273 - accuracy: 0.7349 - val_loss: 2.1234 - val_accuracy: 0.7400\n",
      "Epoch 49/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4360 - accuracy: 0.7335 - val_loss: 2.1303 - val_accuracy: 0.7324\n",
      "Epoch 50/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4367 - accuracy: 0.7365 - val_loss: 2.1231 - val_accuracy: 0.7418\n",
      "Epoch 51/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4133 - accuracy: 0.7343 - val_loss: 2.1152 - val_accuracy: 0.7399\n",
      "Epoch 52/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4050 - accuracy: 0.7349 - val_loss: 2.1278 - val_accuracy: 0.7416\n",
      "Epoch 53/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3982 - accuracy: 0.7343 - val_loss: 2.1369 - val_accuracy: 0.7384\n",
      "Epoch 54/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.4043 - accuracy: 0.7374 - val_loss: 2.1428 - val_accuracy: 0.7425\n",
      "Epoch 55/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3992 - accuracy: 0.7355 - val_loss: 2.1280 - val_accuracy: 0.7417\n",
      "Epoch 56/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3779 - accuracy: 0.7365 - val_loss: 2.1380 - val_accuracy: 0.7421\n",
      "Epoch 57/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3689 - accuracy: 0.7378 - val_loss: 2.1387 - val_accuracy: 0.7398\n",
      "Epoch 58/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3715 - accuracy: 0.7358 - val_loss: 2.1545 - val_accuracy: 0.7431\n",
      "Epoch 59/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3716 - accuracy: 0.7371 - val_loss: 2.1507 - val_accuracy: 0.7361\n",
      "Epoch 60/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3664 - accuracy: 0.7371 - val_loss: 2.1529 - val_accuracy: 0.7424\n",
      "Epoch 61/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3500 - accuracy: 0.7382 - val_loss: 2.1092 - val_accuracy: 0.7415\n",
      "Epoch 62/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3480 - accuracy: 0.7375 - val_loss: 2.1503 - val_accuracy: 0.7412\n",
      "Epoch 63/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3378 - accuracy: 0.7375 - val_loss: 2.1567 - val_accuracy: 0.7436\n",
      "Epoch 64/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3425 - accuracy: 0.7372 - val_loss: 2.1580 - val_accuracy: 0.7400\n",
      "Epoch 65/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3256 - accuracy: 0.7404 - val_loss: 2.1589 - val_accuracy: 0.7432\n",
      "Epoch 66/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3224 - accuracy: 0.7399 - val_loss: 2.1473 - val_accuracy: 0.7370\n",
      "Epoch 67/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3140 - accuracy: 0.7407 - val_loss: 2.1571 - val_accuracy: 0.7409\n",
      "Epoch 68/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3006 - accuracy: 0.7398 - val_loss: 2.1554 - val_accuracy: 0.7445\n",
      "Epoch 69/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.3123 - accuracy: 0.7384 - val_loss: 2.1595 - val_accuracy: 0.7405\n",
      "Epoch 70/500\n",
      "3/3 [==============================] - 6s 2s/step - loss: 1.2886 - accuracy: 0.7431 - val_loss: 2.1762 - val_accuracy: 0.7424\n",
      "Epoch 71/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2892 - accuracy: 0.7400 - val_loss: 2.1643 - val_accuracy: 0.7397\n",
      "Epoch 72/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2800 - accuracy: 0.7409 - val_loss: 2.1658 - val_accuracy: 0.7432\n",
      "Epoch 73/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2676 - accuracy: 0.7432 - val_loss: 2.1893 - val_accuracy: 0.7408\n",
      "Epoch 74/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2655 - accuracy: 0.7445 - val_loss: 2.1778 - val_accuracy: 0.7424\n",
      "Epoch 75/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2535 - accuracy: 0.7454 - val_loss: 2.1683 - val_accuracy: 0.7401\n",
      "Epoch 76/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2404 - accuracy: 0.7450 - val_loss: 2.1900 - val_accuracy: 0.7399\n",
      "Epoch 77/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2466 - accuracy: 0.7441 - val_loss: 2.1707 - val_accuracy: 0.7423\n",
      "Epoch 78/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2380 - accuracy: 0.7421 - val_loss: 2.1789 - val_accuracy: 0.7379\n",
      "Epoch 79/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2286 - accuracy: 0.7460 - val_loss: 2.1771 - val_accuracy: 0.7434\n",
      "Epoch 80/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2228 - accuracy: 0.7477 - val_loss: 2.1398 - val_accuracy: 0.7429\n",
      "Epoch 81/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2362 - accuracy: 0.7483 - val_loss: 2.1804 - val_accuracy: 0.7404\n",
      "Epoch 82/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.2015 - accuracy: 0.7494 - val_loss: 2.1860 - val_accuracy: 0.7420\n",
      "Epoch 83/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1904 - accuracy: 0.7480 - val_loss: 2.1929 - val_accuracy: 0.7403\n",
      "Epoch 84/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1835 - accuracy: 0.7511 - val_loss: 2.1774 - val_accuracy: 0.7412\n",
      "Epoch 85/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1843 - accuracy: 0.7509 - val_loss: 2.2028 - val_accuracy: 0.7408\n",
      "Epoch 86/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1803 - accuracy: 0.7501 - val_loss: 2.1845 - val_accuracy: 0.7434\n",
      "Epoch 87/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1628 - accuracy: 0.7523 - val_loss: 2.1996 - val_accuracy: 0.7389\n",
      "Epoch 88/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1545 - accuracy: 0.7528 - val_loss: 2.1861 - val_accuracy: 0.7431\n",
      "Epoch 89/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1599 - accuracy: 0.7526 - val_loss: 2.2072 - val_accuracy: 0.7414\n",
      "Epoch 90/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1474 - accuracy: 0.7530 - val_loss: 2.1980 - val_accuracy: 0.7403\n",
      "Epoch 91/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1325 - accuracy: 0.7542 - val_loss: 2.2021 - val_accuracy: 0.7419\n",
      "Epoch 92/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1231 - accuracy: 0.7542 - val_loss: 2.2026 - val_accuracy: 0.7409\n",
      "Epoch 93/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1179 - accuracy: 0.7551 - val_loss: 2.2071 - val_accuracy: 0.7427\n",
      "Epoch 94/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1154 - accuracy: 0.7556 - val_loss: 2.2361 - val_accuracy: 0.7388\n",
      "Epoch 95/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1231 - accuracy: 0.7542 - val_loss: 2.1987 - val_accuracy: 0.7420\n",
      "Epoch 96/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.1006 - accuracy: 0.7571 - val_loss: 2.2166 - val_accuracy: 0.7416\n",
      "Epoch 97/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0863 - accuracy: 0.7593 - val_loss: 2.2022 - val_accuracy: 0.7421\n",
      "Epoch 98/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0965 - accuracy: 0.7585 - val_loss: 2.2205 - val_accuracy: 0.7403\n",
      "Epoch 99/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0719 - accuracy: 0.7646 - val_loss: 2.2209 - val_accuracy: 0.7417\n",
      "Epoch 100/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0659 - accuracy: 0.7647 - val_loss: 2.2241 - val_accuracy: 0.7398\n",
      "Epoch 101/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0654 - accuracy: 0.7637 - val_loss: 2.2289 - val_accuracy: 0.7429\n",
      "Epoch 102/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0584 - accuracy: 0.7628 - val_loss: 2.2477 - val_accuracy: 0.7407\n",
      "Epoch 103/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0658 - accuracy: 0.7604 - val_loss: 2.2286 - val_accuracy: 0.7411\n",
      "Epoch 104/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0527 - accuracy: 0.7632 - val_loss: 2.2352 - val_accuracy: 0.7419\n",
      "Epoch 105/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0265 - accuracy: 0.7692 - val_loss: 2.2345 - val_accuracy: 0.7421\n",
      "Epoch 106/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0190 - accuracy: 0.7708 - val_loss: 2.2600 - val_accuracy: 0.7407\n",
      "Epoch 107/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0319 - accuracy: 0.7667 - val_loss: 2.2407 - val_accuracy: 0.7422\n",
      "Epoch 108/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0118 - accuracy: 0.7703 - val_loss: 2.2477 - val_accuracy: 0.7405\n",
      "Epoch 109/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 1.0024 - accuracy: 0.7731 - val_loss: 2.2579 - val_accuracy: 0.7422\n",
      "Epoch 110/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9979 - accuracy: 0.7719 - val_loss: 2.2495 - val_accuracy: 0.7410\n",
      "Epoch 111/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9973 - accuracy: 0.7752 - val_loss: 2.2706 - val_accuracy: 0.7406\n",
      "Epoch 112/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9913 - accuracy: 0.7756 - val_loss: 2.2574 - val_accuracy: 0.7417\n",
      "Epoch 113/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9720 - accuracy: 0.7815 - val_loss: 2.2658 - val_accuracy: 0.7406\n",
      "Epoch 114/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9652 - accuracy: 0.7822 - val_loss: 2.2623 - val_accuracy: 0.7414\n",
      "Epoch 115/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9721 - accuracy: 0.7772 - val_loss: 2.2835 - val_accuracy: 0.7387\n",
      "Epoch 116/500\n",
      "3/3 [==============================] - 5s 2s/step - loss: 0.9575 - accuracy: 0.7843 - val_loss: 2.2691 - val_accuracy: 0.7406\n",
      "Epoch 117/500\n",
      "1/3 [=========>....................] - ETA: 1s - loss: 0.9297 - accuracy: 0.7870"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-4e68fc632377>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx_tr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_tr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_tr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_tr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_tr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx_val\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1181\u001b[0m                 _r=1):\n\u001b[0;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1183\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1184\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    887\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    915\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3021\u001b[0m       (graph_function,\n\u001b[0;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 3023\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   3024\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   3025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1958\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1959\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1960\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1961\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    589\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 591\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    592\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\naman mistry\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     57\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     60\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit([x_tr,y_tr[:,:-1]],y_tr.reshape(y_tr.shape[0],y_tr.shape[1],1)[:,1:], epochs=500, batch_size=128, validation_data=([x_val,y_val[:,:-1]],y_val.reshape(y_val.shape[0],y_val.shape[1],1)[:,1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c8366c60-4aa1-4ded-9b25-ddbb3b809577",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making inference model for prediction\n",
    "\n",
    "#for encoder model\n",
    "encoder_model = Model(inputs=[encoder_input], outputs=[encoder_output,state_h,state_c])\n",
    "\n",
    "decoder_internal_state_h = Input(shape=(latent_dim,))\n",
    "decoder_internal_state_c = Input(shape=(latent_dim,))\n",
    "decoder_internal_states = [decoder_internal_state_h, decoder_internal_state_c]\n",
    "\n",
    "\n",
    "dec_emb_2 = decoder_emb_layer(decoder_input)\n",
    "decoder_output, state_h2, state_c2 = decoder_lstm(dec_emb_2, initial_state=[decoder_internal_state_h, decoder_internal_state_c])\n",
    "decoder_output = decoder_dense(decoder_output)\n",
    "#for decoder model\n",
    "decoder_model = Model(inputs=[decoder_input,decoder_internal_states], outputs=[decoder_output,state_h2,state_c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1b9d5a5-07ce-400d-a3e6-b43c1c9592bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "reverse_target_word_index=y_tokenizer.index_word\n",
    "reverse_source_word_index=x_tokenizer.index_word\n",
    "target_word_index=y_tokenizer.word_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "549a8641-eba2-435d-82c0-64925488405f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "64f63269-fe64-4bf2-89a9-dbc19ae8dc6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_sequence(input_seq):\n",
    "    e_out, e_h, e_c = encoder_model.predict(input_seq)\n",
    "    \n",
    "    target_seq = np.zeros((1,1))\n",
    "    \n",
    "    target_seq[0,0] = target_word_index['start']\n",
    "    \n",
    "    stop_condition = False\n",
    "    decoded_seq = ''\n",
    "    count = 0\n",
    "    while not stop_condition:\n",
    "        \n",
    "        output_tokens,state_h2,state_c2 = decoder_model.predict([target_seq,e_h,e_c])\n",
    "        \n",
    "        sampled_token_index = np.argmax(output_tokens)\n",
    "        sampled_token = reverse_target_word_index[sampled_token_index]\n",
    "        \n",
    "        if sampled_token!='end':\n",
    "            decoded_seq+=' '+sampled_token\n",
    "            \n",
    "        if sampled_token == 'end' or len(decoded_seq.split()) >= MAX_HIN_LEN:\n",
    "            stop_condition = True\n",
    "        \n",
    "        target_seq = np.zeros((1,1))\n",
    "        target_seq[0,0] = sampled_token_index\n",
    "        \n",
    "        e_h, e_c = state_h2, state_c2\n",
    "    return decoded_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d7f15f8a-b19f-45cf-a320-bd24661339d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def seq2text(input_seq):\n",
    "    newString=''\n",
    "    for i in input_seq:\n",
    "        if(i!=0):\n",
    "            newString=newString+reverse_source_word_index[i]+' '\n",
    "    return newString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92b00d76-ea7f-427e-b9c7-88b9d174da3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def seq2hindi(input_seq):\n",
    "    newString=''\n",
    "    for i in input_seq:\n",
    "        if(i!=0):\n",
    "            newString=newString+reverse_target_word_index[i]+' '\n",
    "    return newString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "94b32473-967e-403c-b028-7f88e2327e09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input English sentence: how long have you been abroad \n",
      "real hindi translation:  start आप विदेश में कबसे हैं end \n",
      "Predicted summary:  मैं अपने क्लब को एक पाँच में रह हुआ सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: will you sing me a beatles song \n",
      "real hindi translation:  start तुम मेरे लिए एक बीटल्स का गाना गा सकते हो क्या end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: take this medicine when you have a cold \n",
      "real hindi translation:  start ज़ुकाम हो तो यह दवाई ले लेना। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: is mary your daughter \n",
      "real hindi translation:  start क्या मैरी तुम्हारी बेटी है end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: the dog kept barking all night \n",
      "real hindi translation:  start कुत्ता रातभर भौंकता रहा। end \n",
      "Predicted summary:  मैं ने को तोहफ़ा नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: you have to go \n",
      "real hindi translation:  start तुम्हें जाना होगा। end \n",
      "Predicted summary:  मैं अपने क्लब को एक पाँच में रह में बना हुआ था।\n",
      "\n",
      "\n",
      "Input English sentence: she got off at the next station \n",
      "real hindi translation:  start वह अगले स्टॉप पर उतर गई। end \n",
      "Predicted summary:  मैं ने को एक गाँव में कैफ़े हुआ था।\n",
      "\n",
      "\n",
      "Input English sentence: thats because youre a girl \n",
      "real hindi translation:  start इसलिए क्योंकि तुम लड़की हो। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच सकता दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: didnt you go out \n",
      "real hindi translation:  start तुम बाहर नहीं गए थे क्या end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: id just like to ask tom one question \n",
      "real hindi translation:  start मैं टॉम से एक सवाल पूछना चाहूँगी। end \n",
      "Predicted summary:  मैं अपने बारे को एक पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: have you ever been to canada \n",
      "real hindi translation:  start तुम कभी कनाडा गए हो क्या end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: his failure has nothing to do with me \n",
      "real hindi translation:  start उसकी नाकामयाबी से मेरा कोई लेनादेना नहीं है। end \n",
      "Predicted summary:  मैं अपने क्लब को एक पाँच में रह में बना हुआ था।\n",
      "\n",
      "\n",
      "Input English sentence: i dont like summer \n",
      "real hindi translation:  start मुझे गर्मी का मौसम पसंद नहीं है। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: he cant be under thirty \n",
      "real hindi translation:  start वह कमसेकम तीस साल का तो होगा। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: sit down please \n",
      "real hindi translation:  start बैठिए। end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: school begins at am \n",
      "real hindi translation:  start स्कूल साढ़ेआठ बजे शुरू होता है। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: there is a desk in this room \n",
      "real hindi translation:  start इस कमरे में मेज़ है। end \n",
      "Predicted summary:  मैं ने को तोहफ़ा नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: his main object in life was to become rich \n",
      "real hindi translation:  start उसका ज़िन्दगी में एक की उद्देश्य था और वह था अमीर बन जाना। end \n",
      "Predicted summary:  मैं अपने क्लब को एक पाँच में रह में बना हुआ था।\n",
      "\n",
      "\n",
      "Input English sentence: you had plenty of time \n",
      "real hindi translation:  start तुम्हारे पास काफ़ी समय था। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: it is easy to add to \n",
      "real hindi translation:  start पाँच से दस को जोड़ना बहुत आसान है। end \n",
      "Predicted summary:  मैं अपनी क्लब के सदस्य पाँच काट हुआ करता था।\n",
      "\n",
      "\n",
      "Input English sentence: there is nothing on earth that is not affected by the sun \n",
      "real hindi translation:  start दुनिया में ऐसी कोई भी चीज़ नहीं है जिसपर सूरज का असर न पड़ता हो। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: i have a bad pain in my back \n",
      "real hindi translation:  start मुझे पीठ में बुरी तरह से दर्द हो रहा है। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: are there enough chairs to go around \n",
      "real hindi translation:  start कुरसियाँ काफ़ी हैं क्या end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: where is the bar \n",
      "real hindi translation:  start शराबख़ाना कहाँ है end \n",
      "Predicted summary:  वह बहुत लेलूँगा।\n",
      "\n",
      "\n",
      "Input English sentence: if my boy had not been killed in the traffic accident he would be a college student now \n",
      "real hindi translation:  start अगर मेरा बेटा ट्रेफ़िक हादसे में नहीं मारा गया होता तो वह अभी कॉलेज जा रहा होता। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच सकता दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: he was afraid to go there \n",
      "real hindi translation:  start वह वहाँ जाने से डरता था। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच काट करता है।\n",
      "\n",
      "\n",
      "Input English sentence: the baby fell asleep in the cradle \n",
      "real hindi translation:  start बच्चा पालनी में सो गया। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: im satisfied with my current income \n",
      "real hindi translation:  start मैं अपनी कमाई से संतुष्ट हूँ। end \n",
      "Predicted summary:  मैं अपने क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: were against war \n",
      "real hindi translation:  start हम युद्ध का विरोध करते हैं। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: dont play with that key \n",
      "real hindi translation:  start उस चाबी के साथतानी मत करो end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: i cant walk any further \n",
      "real hindi translation:  start मुझसे और नहीं चला जाएगा। end \n",
      "Predicted summary:  वह अफ़वाह सच हैं।\n",
      "\n",
      "\n",
      "Input English sentence: iron is harder than gold \n",
      "real hindi translation:  start लोहा सोने से सख्त होता है। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: my wife has just cleared the table \n",
      "real hindi translation:  start मेरी बीवी ने अभीअभी मेज़ को साफ़ किया है। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: she complained to me of his rudeness \n",
      "real hindi translation:  start उसने मुझसे उसकी असभ्यता के बारे में शिक़ायत करी। end \n",
      "Predicted summary:  मैं अपने बारे को एक पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: the girl did nothing but cry \n",
      "real hindi translation:  start उस लड़की ने रोने के अलावा और कुछ नहीं किया। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: put your hands up \n",
      "real hindi translation:  start हाथ ऊपर करो end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: we drink our tea with sugar \n",
      "real hindi translation:  start हम चाय चीनी डालकर पीते हैं। end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: they have demanded that all copies of the book be destroyed \n",
      "real hindi translation:  start उन्होंने मांग करी है कि इस किताब की सारी कॉपियाँ नष्ट कर दीं जाएं। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच सकता दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: the house is said to be haunted \n",
      "real hindi translation:  start लोग कहते हैं कि इस घर में भूत हैं। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच सकता दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: they heard a gun go off in the distance \n",
      "real hindi translation:  start उनको दूर से बंदूक की आवाज़ सुनाई दी। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: at last we got to the lake \n",
      "real hindi translation:  start हम आखिरकार झील तक पहुँच गए। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: the two languages have a lot in common \n",
      "real hindi translation:  start इन दो भाषाओं में बहुत सारी समानताएं हैं। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: mind your own business \n",
      "real hindi translation:  start अपने काम से काम रखो end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: i am interested in english \n",
      "real hindi translation:  start मुझे अंग्रेज़ी में दिलचस्पी है। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: the top of that mountain is flat \n",
      "real hindi translation:  start उस पहाड़ की चोटी चपटी है। end \n",
      "Predicted summary:  मैं अपने बारे को एक पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: he took advantage of every opportunity he had \n",
      "real hindi translation:  start उसने किसी भी मौके को हाथ से जाने नहीं दिया। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच काट करता है।\n",
      "\n",
      "\n",
      "Input English sentence: its your move \n",
      "real hindi translation:  start तुम्हारी चाल है। end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: let me know your address \n",
      "real hindi translation:  start मुझे अपना पता बतादेना। end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: he turned traitor \n",
      "real hindi translation:  start वह देशद्रोही बन गया। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: the thieves pulled open all the drawers of the desk in search of money \n",
      "real hindi translation:  start चोरों ने पैसे ढूँढने के लिए मेज़ की सारी दराज़ें खोल डालीं। end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: she reproached me for not answering the letter \n",
      "real hindi translation:  start उसने मुझे चिट्ठी का जवाब न देने के लिए डाँटा। end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: i get up at six every day \n",
      "real hindi translation:  start मैं रोज़ छः बजे उठता हूँ। end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: come on in \n",
      "real hindi translation:  start अंदर आ जाओ। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: ill be back at ten \n",
      "real hindi translation:  start मैं दस बजे वापस आऊँगा। end \n",
      "Predicted summary:  वह बहुत बकबक नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: covered with dust the doll stood in the corner of the room \n",
      "real hindi translation:  start वह धूल से लिपटी हुई गुड़िया कमरे के कोने में खड़ी हुई थी। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: i really dont need any help \n",
      "real hindi translation:  start मुझे सच में किसी की भी मदद की ज़रूरत नहीं है। end \n",
      "Predicted summary:  यह अभी बड़ा आता है।\n",
      "\n",
      "\n",
      "Input English sentence: we were in a hurry \n",
      "real hindi translation:  start हम जल्दी में थे। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: i live in boston too \n",
      "real hindi translation:  start मैं भी बॉस्टन में ही रहता हूँ। end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: you didnt have to come so early \n",
      "real hindi translation:  start तुम्हे इतना जल्दी आने की ज़रूरत नहीं थी। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: thank you very much doctor \n",
      "real hindi translation:  start डॉक्टर साहब आपका बहुत बहुत धन्यवाद। end \n",
      "Predicted summary:  वह सब में बड़ा बजे उड़ान हैं।\n",
      "\n",
      "\n",
      "Input English sentence: congratulations \n",
      "real hindi translation:  start मुबारक हो end \n",
      "Predicted summary:  वह बहुत बकबक नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: your hair is too long \n",
      "real hindi translation:  start तुम्हारे बाल कुछ ज़्यादा ही लम्बे हैं। end \n",
      "Predicted summary:  मैं अपने क्लब को एक पाँच में रह हुआ सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: there are few if any such men \n",
      "real hindi translation:  start ऐसे लोग कम ही होंगे। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: ive lost my glasses \n",
      "real hindi translation:  start मेरा चश्मा खो गया है। end \n",
      "Predicted summary:  वह ने में तोहफ़ा नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: he married for money \n",
      "real hindi translation:  start उसने पैसों के लिए शादी की। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच काट करता है।\n",
      "\n",
      "\n",
      "Input English sentence: hell get well soon \n",
      "real hindi translation:  start वह जल्द ही ठीक हो जाएगा। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: the water was cut off yesterday \n",
      "real hindi translation:  start पानी कल बंद हो गया था। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: parents are responsible for the safety of their children \n",
      "real hindi translation:  start मातापिता अपने बच्चों की हिफ़ाज़त के लिए ज़िम्मेदार होते हैं। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: what time do you get up every day \n",
      "real hindi translation:  start तुम रोज़ कितने बजे उठा करते हो end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: what is in this box \n",
      "real hindi translation:  start इस डब्बे में क्या है end \n",
      "Predicted summary:  मैं ने को किताब में बंदर पकड़ा है।\n",
      "\n",
      "\n",
      "Input English sentence: im thinking about you \n",
      "real hindi translation:  start मैं तुम्हारे बारे में सोच रहा हूँ। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: i found the glass empty \n",
      "real hindi translation:  start मुझे गिलास खाली मिला। end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: dont forget to return the book to the library \n",
      "real hindi translation:  start उस किताब को पुस्तकालय को लौटाना मत भूलना। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार उठाने सकता है।\n",
      "\n",
      "\n",
      "Input English sentence: how was the weather yesterday \n",
      "real hindi translation:  start कल का मौसम कैसा था end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: that town is two miles away \n",
      "real hindi translation:  start वह नगर दो मील दूर है। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: this box contains five apples \n",
      "real hindi translation:  start इस डब्बे में पाँच सेव हैं। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: today is independence day \n",
      "real hindi translation:  start आज स्वतंत्रता दिवस है। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: im sorry my father is out \n",
      "real hindi translation:  start माफ़ कीजिएगा मेरे पापा अभी घर पर नहीं हैं। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: i am able to read english \n",
      "real hindi translation:  start मैं अंग्रेज़ी पढ़ सकता हूँ। end \n",
      "Predicted summary:  वह ने झूठ है\n",
      "\n",
      "\n",
      "Input English sentence: cover up the injured man with this blanket \n",
      "real hindi translation:  start घायल आदमी को इस कंबल से ढक दो। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: the foods not ready yet \n",
      "real hindi translation:  start खाना अभी तक तैयार नहीं हुआ है। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: the girl who works at the bakery is cute \n",
      "real hindi translation:  start बेकरी में जो लड़की काम करती है वह बहुत क्यूट है। end \n",
      "Predicted summary:  मैं अपनी क्लब के सदस्य पाँच काट हुआ करता था।\n",
      "\n",
      "\n",
      "Input English sentence: this is a good newspaper isnt it \n",
      "real hindi translation:  start यह एक अच्छा अखबार है ना end \n",
      "Predicted summary:  वह अफ़वाह सच हैं।\n",
      "\n",
      "\n",
      "Input English sentence: the ship is bound for finland \n",
      "real hindi translation:  start यह जहाज़ फ़िनलैंड के लिए रवाना है। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: you arent supposed to play baseball here \n",
      "real hindi translation:  start यहाँ बेसबॉल खेलना मना है। end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: he was arrested because he stole the money \n",
      "real hindi translation:  start उसे पैसे चुराने के लिए गिरफ़्तार कर लिया गया। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच सकता दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: she always wears black \n",
      "real hindi translation:  start वह हमेशा काले कपड़े पहनती है। end \n",
      "Predicted summary:  वह ने में तोहफ़ा नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: im looking for an old man \n",
      "real hindi translation:  start मैं एक बूढ़े आदमी की तलाश मे हूँ। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: i stayed at home all day instead of going to work \n",
      "real hindi translation:  start मैं काम पर जाने के बजाय सारे दिन घर पर रहा। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच काट करता है।\n",
      "\n",
      "\n",
      "Input English sentence: have you been abroad \n",
      "real hindi translation:  start आप विदेश गए हुए हैं क्या end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: she is well known in both india and china \n",
      "real hindi translation:  start वह भारत और चीन दोनो में बहुत मशहूर है। end \n",
      "Predicted summary:  मैं अपने बारे के लिए में रह हुआ था।\n",
      "\n",
      "\n",
      "Input English sentence: is it necessary for me to attend the party \n",
      "real hindi translation:  start मेरा पार्टी में आना ज़रूरी है क्या end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: she is poor but happy \n",
      "real hindi translation:  start वह गरीब है पर खुश है। end \n",
      "Predicted summary:  वह बहुत बकबक नहीं हैं।\n",
      "\n",
      "\n",
      "Input English sentence: im afraid that tom might come \n",
      "real hindi translation:  start मुझे डर है की टॉम आ जाएगा। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए पहुँच पहुँच दिलाया।\n",
      "\n",
      "\n",
      "Input English sentence: id like a map of the city \n",
      "real hindi translation:  start मैं शहर का नक्शा खरीदना चाहता हूँ। end \n",
      "Predicted summary:  मैं ने को किताब में कैफ़े हुआ हैं।\n",
      "\n",
      "\n",
      "Input English sentence: i cant let you leave \n",
      "real hindi translation:  start आपको जाने नहीं दे सकता हूँ। end \n",
      "Predicted summary:  मैं अपनी क्लब को सदस्य पाँच काट करता है।\n",
      "\n",
      "\n",
      "Input English sentence: i worked on the farm all day \n",
      "real hindi translation:  start मैंने पूरे दिन खेत में काम किया। end \n",
      "Predicted summary:  मैं अपने बारे को एक गाँव में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: we are good friends \n",
      "real hindi translation:  start हम अच्छे दोस्त हैं। end \n",
      "Predicted summary:  वह अफ़वाह सच हैं।\n",
      "\n",
      "\n",
      "Input English sentence: i know that its not a good idea \n",
      "real hindi translation:  start मुझे पता है कि यह एक अच्छा विचार नहीं है। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार में लिए उठाने पहुँच सकता था।\n",
      "\n",
      "\n",
      "Input English sentence: tom did what he could to calm mary down \n",
      "real hindi translation:  start टॉम ने वह सब किया जो वह मैरी को शांत करने के लिए कर सकता था। end \n",
      "Predicted summary:  मैं अपने बारे को एक सारे में बार उठाने सकता है।\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,100):\n",
    "    print('Input English sentence:', seq2text(x_val[i]))\n",
    "    print(\"real hindi translation: \",seq2hindi(y_val[i]))\n",
    "    print(\"Predicted summary:\",decode_sequence(x_val[i].reshape(1,MAX_ENG_LEN)))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "489e6d7b-d7d8-4cc6-a498-8f00d50c09f5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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