Exp_4_ANN_Text_Predictor-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.424621Z",
     "start_time": "2023-07-24T12:36:23.869767Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.437642Z",
     "start_time": "2023-07-24T12:36:36.426617Z"
    }
   },
   "outputs": [],
   "source": [
    "data = [\n",
    "    'Today is a sunny day',\n",
    "    'today is a Windy Day',\n",
    "    'Is it sunny Today',\n",
    "    'I really enjoyed walking in the snow today'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.499612Z",
     "start_time": "2023-07-24T12:36:36.439615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Today is a sunny day',\n",
       " 'today is a Windy Day',\n",
       " 'Is it sunny Today',\n",
       " 'I really enjoyed walking in the snow today']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.509616Z",
     "start_time": "2023-07-24T12:36:36.454617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'today': 1, 'is': 2, 'a': 3, 'sunny': 4, 'day': 5, 'windy': 6, 'it': 7, 'i': 8, 'really': 9, 'enjoyed': 10, 'walking': 11, 'in': 12, 'the': 13, 'snow': 14}\n"
     ]
    }
   ],
   "source": [
    "tokenizer = Tokenizer(100)\n",
    "tokenizer.fit_on_texts(data)\n",
    "text_to_numbers = tokenizer.texts_to_sequences(data)\n",
    "word_ids = tokenizer.word_index\n",
    "print(word_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.510651Z",
     "start_time": "2023-07-24T12:36:36.469614Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3, 4, 5], [1, 2, 3, 6, 5], [2, 7, 4, 1], [8, 9, 10, 11, 12, 13, 14, 1]]\n"
     ]
    }
   ],
   "source": [
    "print(text_to_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.510651Z",
     "start_time": "2023-07-24T12:36:36.486640Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "lengths_of_strings = [len(s) for s in text_to_numbers]\n",
    "print(max(lengths_of_strings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.516611Z",
     "start_time": "2023-07-24T12:36:36.502612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4  5  0  0  0]\n",
      " [ 1  2  3  6  5  0  0  0]\n",
      " [ 2  7  4  1  0  0  0  0]\n",
      " [ 8  9 10 11 12 13 14  1]]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "padded_num_text_data = pad_sequences(text_to_numbers, padding = 'post', maxlen = max(lengths_of_strings), truncating = 'post')\n",
    "print(padded_num_text_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the vocabulary size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.609614Z",
     "start_time": "2023-07-24T12:36:36.516611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = len(tokenizer.word_index) + 1\n",
    "print(vocabulary_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare input and output data for training\n",
    "### Today is a sunny[input] ___? (day)[output]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.610613Z",
     "start_time": "2023-07-24T12:36:36.534614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  0,  0],\n",
       "       [ 1,  2,  3,  6,  5,  0,  0],\n",
       "       [ 2,  7,  4,  1,  0,  0,  0],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = padded_num_text_data[:,:-1]\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.626614Z",
     "start_time": "2023-07-24T12:36:36.550612Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "y_train = padded_num_text_data[:,-1]\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.627612Z",
     "start_time": "2023-07-24T12:36:36.562613Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  0  1  2  3  4  5]\n",
      " [ 0  0  0  1  2  3  6  5]\n",
      " [ 0  0  0  0  2  7  4  1]\n",
      " [ 8  9 10 11 12 13 14  1]]\n"
     ]
    }
   ],
   "source": [
    "padded_num_text_data = pad_sequences(text_to_numbers, maxlen = max(lengths_of_strings))\n",
    "print(padded_num_text_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.627612Z",
     "start_time": "2023-07-24T12:36:36.579613Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = len(tokenizer.word_index) + 1\n",
    "print(vocabulary_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.629617Z",
     "start_time": "2023-07-24T12:36:36.595616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  1,  2,  3,  4],\n",
       "       [ 0,  0,  0,  1,  2,  3,  6],\n",
       "       [ 0,  0,  0,  0,  2,  7,  4],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = padded_num_text_data[:,:-1]\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.630620Z",
     "start_time": "2023-07-24T12:36:36.613613Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 5 1 1]\n"
     ]
    }
   ],
   "source": [
    "y_train = padded_num_text_data[:,-1]\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.746217Z",
     "start_time": "2023-07-24T12:36:36.629617Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "from tensorflow.keras.layers import LSTM, GRU, SimpleRNN\n",
    "from tensorflow.keras.layers import Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.747217Z",
     "start_time": "2023-07-24T12:36:36.644656Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "y_train = to_categorical(y_train, num_classes=vocabulary_size)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.845089Z",
     "start_time": "2023-07-24T12:36:36.661612Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(vocabulary_size,10,\n",
    "                    input_length=max(lengths_of_strings)-1))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(vocabulary_size, activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.914132Z",
     "start_time": "2023-07-24T12:36:36.845089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 7, 10)             150       \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 70)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 15)                1065      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,215\n",
      "Trainable params: 1,215\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:36.984093Z",
     "start_time": "2023-07-24T12:36:36.884091Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:41.526655Z",
     "start_time": "2023-07-24T12:36:36.988090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "1/1 [==============================] - 1s 578ms/step - loss: 2.7139 - accuracy: 0.0000e+00\n",
      "Epoch 2/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.7046 - accuracy: 0.0000e+00\n",
      "Epoch 3/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.6953 - accuracy: 0.0000e+00\n",
      "Epoch 4/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.6860 - accuracy: 0.0000e+00\n",
      "Epoch 5/500\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.6767 - accuracy: 0.0000e+00\n",
      "Epoch 6/500\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.6674 - accuracy: 0.2500\n",
      "Epoch 7/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.6581 - accuracy: 0.2500\n",
      "Epoch 8/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.6488 - accuracy: 0.2500\n",
      "Epoch 9/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.6394 - accuracy: 0.7500\n",
      "Epoch 10/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.6300 - accuracy: 0.7500\n",
      "Epoch 11/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.6206 - accuracy: 0.7500\n",
      "Epoch 12/500\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 2.6112 - accuracy: 0.7500\n",
      "Epoch 13/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.6016 - accuracy: 0.7500\n",
      "Epoch 14/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.5921 - accuracy: 0.7500\n",
      "Epoch 15/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.5824 - accuracy: 0.7500\n",
      "Epoch 16/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.5726 - accuracy: 0.7500\n",
      "Epoch 17/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.5628 - accuracy: 0.7500\n",
      "Epoch 18/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.5529 - accuracy: 0.7500\n",
      "Epoch 19/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.5428 - accuracy: 0.7500\n",
      "Epoch 20/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.5326 - accuracy: 0.7500\n",
      "Epoch 21/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.5224 - accuracy: 0.7500\n",
      "Epoch 22/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.5119 - accuracy: 0.7500\n",
      "Epoch 23/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.5014 - accuracy: 0.7500\n",
      "Epoch 24/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4906 - accuracy: 0.7500\n",
      "Epoch 25/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4798 - accuracy: 0.7500\n",
      "Epoch 26/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.4687 - accuracy: 0.7500\n",
      "Epoch 27/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.4575 - accuracy: 0.7500\n",
      "Epoch 28/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4461 - accuracy: 0.7500\n",
      "Epoch 29/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4346 - accuracy: 0.7500\n",
      "Epoch 30/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4228 - accuracy: 0.7500\n",
      "Epoch 31/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.4108 - accuracy: 0.7500\n",
      "Epoch 32/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.3987 - accuracy: 0.7500\n",
      "Epoch 33/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.3863 - accuracy: 0.7500\n",
      "Epoch 34/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.3737 - accuracy: 0.7500\n",
      "Epoch 35/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.3608 - accuracy: 0.7500\n",
      "Epoch 36/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.3478 - accuracy: 0.7500\n",
      "Epoch 37/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.3344 - accuracy: 0.7500\n",
      "Epoch 38/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.3209 - accuracy: 0.7500\n",
      "Epoch 39/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.3071 - accuracy: 0.7500\n",
      "Epoch 40/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.2930 - accuracy: 1.0000\n",
      "Epoch 41/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.2787 - accuracy: 1.0000\n",
      "Epoch 42/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.2641 - accuracy: 1.0000\n",
      "Epoch 43/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.2493 - accuracy: 1.0000\n",
      "Epoch 44/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.2341 - accuracy: 1.0000\n",
      "Epoch 45/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.2187 - accuracy: 1.0000\n",
      "Epoch 46/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.2031 - accuracy: 1.0000\n",
      "Epoch 47/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1871 - accuracy: 1.0000\n",
      "Epoch 48/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1708 - accuracy: 1.0000\n",
      "Epoch 49/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1543 - accuracy: 1.0000\n",
      "Epoch 50/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1375 - accuracy: 1.0000\n",
      "Epoch 51/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1204 - accuracy: 1.0000\n",
      "Epoch 52/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1030 - accuracy: 1.0000\n",
      "Epoch 53/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.0854 - accuracy: 1.0000\n",
      "Epoch 54/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.0674 - accuracy: 1.0000\n",
      "Epoch 55/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.0492 - accuracy: 1.0000\n",
      "Epoch 56/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.0307 - accuracy: 1.0000\n",
      "Epoch 57/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.0119 - accuracy: 1.0000\n",
      "Epoch 58/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.9928 - accuracy: 1.0000\n",
      "Epoch 59/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.9735 - accuracy: 1.0000\n",
      "Epoch 60/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.9539 - accuracy: 1.0000\n",
      "Epoch 61/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.9340 - accuracy: 1.0000\n",
      "Epoch 62/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.9139 - accuracy: 1.0000\n",
      "Epoch 63/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.8935 - accuracy: 1.0000\n",
      "Epoch 64/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.8729 - accuracy: 1.0000\n",
      "Epoch 65/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.8520 - accuracy: 1.0000\n",
      "Epoch 66/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.8309 - accuracy: 1.0000\n",
      "Epoch 67/500\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 0.0287 - accuracy: 1.0000\n",
      "Epoch 429/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0285 - accuracy: 1.0000\n",
      "Epoch 430/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0283 - accuracy: 1.0000\n",
      "Epoch 431/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0282 - accuracy: 1.0000\n",
      "Epoch 432/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0280 - accuracy: 1.0000\n",
      "Epoch 433/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0279 - accuracy: 1.0000\n",
      "Epoch 434/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0277 - accuracy: 1.0000\n",
      "Epoch 435/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0276 - accuracy: 1.0000\n",
      "Epoch 436/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0274 - accuracy: 1.0000\n",
      "Epoch 437/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0273 - accuracy: 1.0000\n",
      "Epoch 438/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0271 - accuracy: 1.0000\n",
      "Epoch 439/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0270 - accuracy: 1.0000\n",
      "Epoch 440/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0268 - accuracy: 1.0000\n",
      "Epoch 441/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0267 - accuracy: 1.0000\n",
      "Epoch 442/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0266 - accuracy: 1.0000\n",
      "Epoch 443/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0264 - accuracy: 1.0000\n",
      "Epoch 444/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0263 - accuracy: 1.0000\n",
      "Epoch 445/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0261 - accuracy: 1.0000\n",
      "Epoch 446/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0260 - accuracy: 1.0000\n",
      "Epoch 447/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0259 - accuracy: 1.0000\n",
      "Epoch 448/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0257 - accuracy: 1.0000\n",
      "Epoch 449/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0256 - accuracy: 1.0000\n",
      "Epoch 450/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0255 - accuracy: 1.0000\n",
      "Epoch 451/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0253 - accuracy: 1.0000\n",
      "Epoch 452/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0252 - accuracy: 1.0000\n",
      "Epoch 453/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0251 - accuracy: 1.0000\n",
      "Epoch 454/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0249 - accuracy: 1.0000\n",
      "Epoch 455/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0248 - accuracy: 1.0000\n",
      "Epoch 456/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0247 - accuracy: 1.0000\n",
      "Epoch 457/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0246 - accuracy: 1.0000\n",
      "Epoch 458/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0244 - accuracy: 1.0000\n",
      "Epoch 459/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0243 - accuracy: 1.0000\n",
      "Epoch 460/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0242 - accuracy: 1.0000\n",
      "Epoch 461/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0241 - accuracy: 1.0000\n",
      "Epoch 462/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0239 - accuracy: 1.0000\n",
      "Epoch 463/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0238 - accuracy: 1.0000\n",
      "Epoch 464/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0237 - accuracy: 1.0000\n",
      "Epoch 465/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0236 - accuracy: 1.0000\n",
      "Epoch 466/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0235 - accuracy: 1.0000\n",
      "Epoch 467/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0234 - accuracy: 1.0000\n",
      "Epoch 468/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0232 - accuracy: 1.0000\n",
      "Epoch 469/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0231 - accuracy: 1.0000\n",
      "Epoch 470/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0230 - accuracy: 1.0000\n",
      "Epoch 471/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0229 - accuracy: 1.0000\n",
      "Epoch 472/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0228 - accuracy: 1.0000\n",
      "Epoch 473/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0227 - accuracy: 1.0000\n",
      "Epoch 474/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0226 - accuracy: 1.0000\n",
      "Epoch 475/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0224 - accuracy: 1.0000\n",
      "Epoch 476/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0223 - accuracy: 1.0000\n",
      "Epoch 477/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0222 - accuracy: 1.0000\n",
      "Epoch 478/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0221 - accuracy: 1.0000\n",
      "Epoch 479/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0220 - accuracy: 1.0000\n",
      "Epoch 480/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0219 - accuracy: 1.0000\n",
      "Epoch 481/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0218 - accuracy: 1.0000\n",
      "Epoch 482/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0217 - accuracy: 1.0000\n",
      "Epoch 483/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0216 - accuracy: 1.0000\n",
      "Epoch 484/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0215 - accuracy: 1.0000\n",
      "Epoch 485/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0214 - accuracy: 1.0000\n",
      "Epoch 486/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0213 - accuracy: 1.0000\n",
      "Epoch 487/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0212 - accuracy: 1.0000\n",
      "Epoch 488/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0211 - accuracy: 1.0000\n",
      "Epoch 489/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0210 - accuracy: 1.0000\n",
      "Epoch 490/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0209 - accuracy: 1.0000\n",
      "Epoch 491/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0208 - accuracy: 1.0000\n",
      "Epoch 492/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0207 - accuracy: 1.0000\n",
      "Epoch 493/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0206 - accuracy: 1.0000\n",
      "Epoch 494/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0205 - accuracy: 1.0000\n",
      "Epoch 495/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0204 - accuracy: 1.0000\n",
      "Epoch 496/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0203 - accuracy: 1.0000\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0202 - accuracy: 1.0000\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.0201 - accuracy: 1.0000\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0200 - accuracy: 1.0000\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0199 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x233867d4df0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:36:41.562654Z",
     "start_time": "2023-07-24T12:36:41.526655Z"
    }
   },
   "outputs": [],
   "source": [
    "max_sequence_length = max(lengths_of_strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-24T12:37:55.652546Z",
     "start_time": "2023-07-24T12:37:55.573546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 20ms/step\n",
      "Predicted word: day\n"
     ]
    }
   ],
   "source": [
    "# Example prediction\n",
    "import numpy as np\n",
    "input_sequence = [\"today is a sunny\"]\n",
    "input_sequence = tokenizer.texts_to_sequences(input_sequence)\n",
    "input_sequence = pad_sequences(input_sequence, maxlen=max_sequence_length-1)\n",
    "\n",
    "predicted_probs = model.predict(input_sequence)[0]\n",
    "predicted_class_index = np.argmax(predicted_probs)\n",
    "\n",
    "# Convert predicted class index back to word\n",
    "predicted_word = tokenizer.index_word[predicted_class_index]\n",
    "\n",
    "print(\"Predicted word:\", predicted_word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict on the following Text\n",
    "## data3 = \"\"\" Jack and Jill went up the hill\\n\n",
    "##\t\tTo fetch a pail of water\\n\n",
    "##\t\tJack fell down and broke his crown\\n\n",
    "##\t\tAnd Jill came tumbling after\\n \"\"\""
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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