TASK 8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LETSGROW MORE INTERNSHIP\n",
    "\n",
    "## NAME :- HARSHAWARDHAN CHITNIS\n",
    "\n",
    "## POSITION :- Data Science Intern\n",
    "\n",
    "## TASK LEVEL : ADVANCED LEVEL TASK - 2\n",
    "\n",
    "## PROJECT NAME : NEXT WORD PREDICTION\n",
    "\n",
    "# STARTING WITH THE PROJECT\n",
    "\n",
    "### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 2732,
     "status": "ok",
     "timestamp": 1630315551031,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "3tZm2YwxLR-k"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(42)\n",
    " \n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.layers import LSTM, Dropout\n",
    "from tensorflow.keras.layers import TimeDistributed\n",
    "from tensorflow.keras.layers import Dense, Activation, Dropout, RepeatVector\n",
    "from tensorflow.keras.optimizers import  RMSprop\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import heapq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gid_6mnnLR-l"
   },
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1630315551032,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "9YyV4g4JVTB-",
    "outputId": "735454d2-96fc-4b0b-db08-60694fd8947f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "corpus length: 581888\n"
     ]
    }
   ],
   "source": [
    "path = 'sample_text.txt'\n",
    "text = open(path,encoding = \"utf8\").read().lower()\n",
    "print('corpus length:', len(text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hVxcxmxQLR-m"
   },
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1630315551033,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "WVpaZCSdLR-m",
    "outputId": "19ef4d53-0362-43dc-f753-b97fcc1724e2",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique chars: 73\n"
     ]
    }
   ],
   "source": [
    "character = sorted(list(set(text)))\n",
    "char_indices = dict((c, i) for i, c in enumerate(character))\n",
    "indices_char = dict((i, c) for i, c in enumerate(character))\n",
    " \n",
    "print(f'unique chars: {len(character)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1630315551034,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "Nf1LvBPILR-n",
    "outputId": "2e5eef48-5ace-417a-a14c-91e5aed2e256"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num training examples: 193950\n"
     ]
    }
   ],
   "source": [
    "#chunk 40 characters with 3 sequences\n",
    "seq_len = 40\n",
    "step = 3\n",
    "sentences = []\n",
    "next_chars = []\n",
    "for i in range(0, len(text) - seq_len, step):\n",
    "    sentences.append(text[i: i + seq_len ])\n",
    "    next_chars.append(text[i + seq_len])\n",
    "print(f'num training examples: {len(sentences)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 3263,
     "status": "ok",
     "timestamp": 1630315554285,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "u0xjISbGLR-n"
   },
   "outputs": [],
   "source": [
    "# generating our features and labels\n",
    "#one hot encoding\n",
    "X = np.zeros((len(sentences), seq_len, len(character)), dtype=np.bool)\n",
    "y = np.zeros((len(sentences), len(character)), dtype=np.bool)\n",
    "for i, sentence in enumerate(sentences):\n",
    "    for t, char in enumerate(sentence):\n",
    "        X[i, t, char_indices[char]] = 1\n",
    "    y[i, char_indices[next_chars[i]]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 37,
     "status": "ok",
     "timestamp": 1630315554292,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "a_LZ0kSRLR-n",
    "outputId": "ccd8f2ad-7617-4009-dee6-c390f68cb894"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'\\nauthor: arthur conan doyle\\n\\nrelease dat'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences[124]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1630315554293,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "g5w8agWgLR-o",
    "outputId": "c6f9307a-afe9-4a18-8119-fdc0f6dd6aea"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'e'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next_chars[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1630315554293,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "9uZlhQHILR-o",
    "outputId": "e97350f1-68eb-490e-b432-cf1da3cc4816"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "        True])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one hot encoded data\n",
    "X[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1630315554294,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "8G3NgBhGLR-o",
    "outputId": "bfb8f1a8-35d2-4724-c499-d1891b6b3f96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one hot encoded data\n",
    "y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1630315554294,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "P_pIYBcNLR-p",
    "outputId": "d8abb5e7-90e8-4359-f90f-5cce1825150e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193950, 40, 73)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1630315554295,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "1QCu1gCpLR-p",
    "outputId": "7d1b36a2-e8bb-4a6a-c9c2-44dee48b164b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193950, 73)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DfZiaqGOLR-p"
   },
   "source": [
    "# Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 946,
     "status": "ok",
     "timestamp": 1630315555226,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "PD3zlFwbLR-p"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(128, input_shape=(seq_len, len(character))))\n",
    "model.add(Dense(len(character)))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 656
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1630315555227,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "6vPB0EmMY-8O",
    "outputId": "94a55844-6d6c-4827-eb4f-ce4e5bc800af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 128)               103424    \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 73)                9417      \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 73)                0         \n",
      "=================================================================\n",
      "Total params: 112,841\n",
      "Trainable params: 112,841\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model, show_shapes=True, to_file='model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ijhm50ry9nBp"
   },
   "source": [
    "# Training our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VYqNDXsCx81S"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 556113,
     "status": "ok",
     "timestamp": 1630316111334,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "SijcUwz8cVwU",
    "outputId": "414c0cee-7bbf-4634-910e-83f72558da01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1501/1501 [==============================] - 23s 14ms/step - loss: 1.9425 - accuracy: 0.4286 - val_loss: 2.2035 - val_accuracy: 0.3918\n",
      "Epoch 2/30\n",
      "1501/1501 [==============================] - 19s 12ms/step - loss: 1.6090 - accuracy: 0.5171 - val_loss: 2.0659 - val_accuracy: 0.4474\n",
      "Epoch 3/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.5157 - accuracy: 0.5433 - val_loss: 1.9985 - val_accuracy: 0.4582\n",
      "Epoch 4/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.4672 - accuracy: 0.5563 - val_loss: 2.0102 - val_accuracy: 0.4567\n",
      "Epoch 5/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.4352 - accuracy: 0.5660 - val_loss: 1.9749 - val_accuracy: 0.4794\n",
      "Epoch 6/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.4096 - accuracy: 0.5712 - val_loss: 2.0004 - val_accuracy: 0.4778\n",
      "Epoch 7/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3939 - accuracy: 0.5770 - val_loss: 1.9970 - val_accuracy: 0.4727\n",
      "Epoch 8/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3777 - accuracy: 0.5806 - val_loss: 1.9891 - val_accuracy: 0.4716\n",
      "Epoch 9/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3649 - accuracy: 0.5848 - val_loss: 1.9355 - val_accuracy: 0.5052\n",
      "Epoch 10/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3558 - accuracy: 0.5869 - val_loss: 1.9872 - val_accuracy: 0.4753\n",
      "Epoch 11/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3460 - accuracy: 0.5894 - val_loss: 1.9744 - val_accuracy: 0.4840\n",
      "Epoch 12/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3369 - accuracy: 0.5917 - val_loss: 1.9839 - val_accuracy: 0.4928\n",
      "Epoch 13/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3331 - accuracy: 0.5919 - val_loss: 2.0311 - val_accuracy: 0.4814\n",
      "Epoch 14/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3260 - accuracy: 0.5936 - val_loss: 2.0093 - val_accuracy: 0.4918\n",
      "Epoch 15/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3198 - accuracy: 0.5956 - val_loss: 2.0079 - val_accuracy: 0.4814\n",
      "Epoch 16/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3135 - accuracy: 0.5965 - val_loss: 2.0272 - val_accuracy: 0.4851\n",
      "Epoch 17/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3084 - accuracy: 0.5981 - val_loss: 2.0315 - val_accuracy: 0.4948\n",
      "Epoch 18/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3043 - accuracy: 0.5998 - val_loss: 2.0498 - val_accuracy: 0.4902\n",
      "Epoch 19/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.3001 - accuracy: 0.6009 - val_loss: 2.0637 - val_accuracy: 0.4907\n",
      "Epoch 20/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2961 - accuracy: 0.6020 - val_loss: 2.0727 - val_accuracy: 0.4866\n",
      "Epoch 21/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2915 - accuracy: 0.6025 - val_loss: 2.1107 - val_accuracy: 0.4680\n",
      "Epoch 22/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2868 - accuracy: 0.6046 - val_loss: 2.0766 - val_accuracy: 0.4799\n",
      "Epoch 23/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2834 - accuracy: 0.6058 - val_loss: 1.9943 - val_accuracy: 0.4830\n",
      "Epoch 24/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2808 - accuracy: 0.6061 - val_loss: 2.0472 - val_accuracy: 0.4845\n",
      "Epoch 25/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2778 - accuracy: 0.6068 - val_loss: 2.0277 - val_accuracy: 0.4959\n",
      "Epoch 26/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2741 - accuracy: 0.6078 - val_loss: 2.0831 - val_accuracy: 0.4866\n",
      "Epoch 27/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2711 - accuracy: 0.6085 - val_loss: 2.1093 - val_accuracy: 0.4691\n",
      "Epoch 28/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2678 - accuracy: 0.6092 - val_loss: 2.0353 - val_accuracy: 0.4887\n",
      "Epoch 29/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2655 - accuracy: 0.6095 - val_loss: 2.1841 - val_accuracy: 0.4603\n",
      "Epoch 30/30\n",
      "1501/1501 [==============================] - 18s 12ms/step - loss: 1.2632 - accuracy: 0.6106 - val_loss: 2.0872 - val_accuracy: 0.4902\n"
     ]
    }
   ],
   "source": [
    "optimizer = RMSprop(learning_rate=0.01)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "history = model.fit(X, y,\n",
    "                    validation_split=0.01, \n",
    "                    batch_size=128,\n",
    "                    epochs=30, \n",
    "                    shuffle=True).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1630316111335,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "pvM3K0TQLR-q"
   },
   "outputs": [],
   "source": [
    "#saving our model\n",
    "model.save('keras_model.h5')\n",
    "pickle.dump(history, open(\"history.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1630316111335,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "Q3sQvQopLR-q"
   },
   "outputs": [],
   "source": [
    "#loading our model\n",
    "model = load_model('keras_model.h5')\n",
    "history = pickle.load(open(\"history.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cd88xYIE-BVu"
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36700,
     "status": "ok",
     "timestamp": 1630316148027,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "9HpwRNYuLR-r",
    "outputId": "6cf835bc-75e3-4761-f4b6-0f412210cc0b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6061/6061 [==============================] - 36s 6ms/step - loss: 1.2202 - accuracy: 0.6240\n",
      "Test Loss 1.2202041149139404\n",
      "Test Accuracy 0.6239752769470215\n"
     ]
    }
   ],
   "source": [
    "loss_and_acc=model.evaluate(X,y)\n",
    "print(\"Test Loss\", loss_and_acc[0])\n",
    "print(\"Test Accuracy\", loss_and_acc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 353
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1630316148029,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "J_gGLdRdUcxy",
    "outputId": "e04c4ca5-558c-4630-9ea4-5f3afb61291a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history['accuracy']\n",
    "val_acc = history['val_accuracy']\n",
    "loss = history['loss']\n",
    "val_loss = history['val_loss']\n",
    "epochs = range(len(acc))\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20, 6))\n",
    "ax[0].plot(epochs, acc, 'r', label='Training accuracy')\n",
    "ax[0].plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
    "ax[0].legend(loc=0)\n",
    "ax[1].plot(epochs, loss, 'r', label='Training loss')\n",
    "ax[1].plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "ax[1].legend(loc=0)\n",
    "\n",
    "plt.suptitle('Training and validation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1630316148030,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "CdqDoC2aLR-r"
   },
   "outputs": [],
   "source": [
    "def prepare_input(text):\n",
    "    x = np.zeros((1, seq_len, len(character)))\n",
    "    for t, char in enumerate(text):\n",
    "        x[0, t, char_indices[char]] = 1.\n",
    "        \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1630316148030,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "SGmWjw-7LR-r",
    "outputId": "11256290-4cd5-4184-928c-383e375c1884"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_input(\"This is an example of input of our model\".lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BHLDX1Tk-lLU"
   },
   "source": [
    "#### functions to get next probable characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1630316148031,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "FRN3rkbvLR-r"
   },
   "outputs": [],
   "source": [
    "def sample(preds, top_n=3):\n",
    "    preds = np.asarray(preds).astype('float64')\n",
    "    preds = np.log(preds)\n",
    "    exp_preds = np.exp(preds)\n",
    "    preds = exp_preds / np.sum(exp_preds)\n",
    "    \n",
    "    return heapq.nlargest(top_n, range(len(preds)), preds.take)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1630316148032,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "bF8DF6JrLR-s"
   },
   "outputs": [],
   "source": [
    "def predict_completion(text):\n",
    "    original_text = text\n",
    "    generated = text\n",
    "    completion = ''\n",
    "    while True:\n",
    "        x = prepare_input(text)\n",
    "        preds = model.predict(x, verbose=0)[0]\n",
    "        next_index = sample(preds, top_n=1)[0]\n",
    "        next_char = indices_char[next_index]\n",
    "        text = text[1:] + next_char\n",
    "        completion += next_char\n",
    "        \n",
    "        if len(original_text + completion) + 2 > len(original_text) and next_char == ' ':\n",
    "            return completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1630316148032,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "XtpcQBhILR-s"
   },
   "outputs": [],
   "source": [
    "def predict_completions(text, n=3):\n",
    "    x = prepare_input(text)\n",
    "    preds = model.predict(x, verbose=0)[0]\n",
    "    next_indices = sample(preds, n)\n",
    "    return [indices_char[idx] + predict_completion(text[1:] + indices_char[idx]) for idx in next_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uFvQZkhOWMwq"
   },
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1630316148033,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "R7MZd_1xLR-s"
   },
   "outputs": [],
   "source": [
    "quotes = [   \n",
    "    \"Deep learning is subset of machine learning,which is essentially a neural network with three or more layers.\",\n",
    "    \"Machine learning  is the study of computer algorithms that improve automatically through experience and by the use of data.\",\n",
    "    \"It is not a lack of love, but a lack of friendship that makes unhappy marriages.\",\n",
    "    \"Recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data.\",\n",
    "    \"Thanks for spending your precious time to view my work.\"  \n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4649,
     "status": "ok",
     "timestamp": 1630316152662,
     "user": {
      "displayName": "Saravana Vel",
      "photoUrl": "",
      "userId": "06114257416441264518"
     },
     "user_tz": -330
    },
    "id": "_TSIOy0OLR-s",
    "outputId": "d12950f5-23ac-4769-d181-e7383d9dfbcb",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deep learning is subset of machine learn\n",
      "['ed ', 'ing ', 'y ', ' to ', 'ales ']\n",
      "\n",
      "machine learning  is the study of comput\n",
      "['ion ', 'e ', 'on.”\\n\\n“well, ', 'tence ', 'ant ']\n",
      "\n",
      "it is not a lack of love, but a lack of \n",
      "['the ', 'considerable ', 'her ', 'my ', 'a ']\n",
      "\n",
      "recurrent neural network (rnn) is a type\n",
      "['ctient ', 'ssion ', 'mplication ', 'writter ', 'ar ']\n",
      "\n",
      "thanks for spending your precious time t\n",
      "['hat ', 'o ', 'rain ', 'ell ', 'ime ']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for q in quotes:\n",
    "    seq = q[:40].lower()\n",
    "    print(seq)\n",
    "    print(predict_completions(seq, 5))\n",
    "    print()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THANK YOU!!"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "v.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
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}