train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17d1d51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "import numpy as np \n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3187e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('intents.json') as file:\n",
    "    data = json.load(file)\n",
    "    \n",
    "training_sentences = []\n",
    "training_labels = []\n",
    "labels = []\n",
    "responses = []\n",
    "\n",
    "\n",
    "for intent in data['intents']:\n",
    "    for pattern in intent['patterns']:\n",
    "        training_sentences.append(pattern)\n",
    "        training_labels.append(intent['tag'])\n",
    "    responses.append(intent['responses'])\n",
    "    \n",
    "    if intent['tag'] not in labels:\n",
    "        labels.append(intent['tag'])\n",
    "        \n",
    "num_classes = len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1eff6ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl_encoder = LabelEncoder()\n",
    "lbl_encoder.fit(training_labels)\n",
    "training_labels = lbl_encoder.transform(training_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bedd959",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 1000\n",
    "embedding_dim = 16\n",
    "max_len = 20\n",
    "oov_token = \"<OOV>\"\n",
    "\n",
    "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)\n",
    "tokenizer.fit_on_texts(training_sentences)\n",
    "word_index = tokenizer.word_index\n",
    "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
    "padded_sequences = pad_sequences(sequences, truncating='post', maxlen=max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9109bb9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 20, 16)            16000     \n",
      "                                                                 \n",
      " global_average_pooling1d (G  (None, 16)               0         \n",
      " lobalAveragePooling1D)                                          \n",
      "                                                                 \n",
      " dense (Dense)               (None, 16)                272       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 16)                272       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 8)                 136       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16,680\n",
      "Trainable params: 16,680\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(vocab_size, embedding_dim, input_length=max_len))\n",
    "model.add(GlobalAveragePooling1D())\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy', \n",
    "              optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33343e7c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "1/1 [==============================] - 1s 537ms/step - loss: 2.0789 - accuracy: 0.0345\n",
      "Epoch 2/500\n",
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      "Epoch 3/500\n",
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      "Epoch 4/500\n",
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      "Epoch 5/500\n",
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      "Epoch 6/500\n",
      "1/1 [==============================] - 0s 0s/step - loss: 2.0747 - accuracy: 0.2414\n",
      "Epoch 7/500\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 2.0739 - accuracy: 0.2414\n",
      "Epoch 8/500\n",
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      "Epoch 9/500\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 2.0721 - accuracy: 0.2414\n",
      "Epoch 10/500\n",
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      "Epoch 11/500\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 2.0704 - accuracy: 0.2414\n",
      "Epoch 12/500\n",
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      "Epoch 13/500\n",
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      "Epoch 14/500\n",
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      "Epoch 15/500\n",
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      "Epoch 16/500\n",
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      "Epoch 30/500\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 2.0504 - accuracy: 0.2414\n",
      "Epoch 31/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.0491 - accuracy: 0.2414\n",
      "Epoch 32/500\n",
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      "Epoch 80/500\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 1.9622 - accuracy: 0.2414\n",
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      "1/1 [==============================] - 0s 0s/step - loss: 1.9538 - accuracy: 0.2414\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/500\n",
      "1/1 [==============================] - 0s 0s/step - loss: 1.9517 - accuracy: 0.2414\n",
      "Epoch 86/500\n",
      "1/1 [==============================] - 0s 0s/step - loss: 1.9495 - accuracy: 0.2414\n",
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      "Epoch 91/500\n",
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    },
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      "Epoch 496/500\n",
      "1/1 [==============================] - 0s 0s/step - loss: 0.0545 - accuracy: 1.0000\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.0539 - accuracy: 1.0000\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 0s/step - loss: 0.0534 - accuracy: 1.0000\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 0s/step - loss: 0.0529 - accuracy: 1.0000\n",
      "Epoch 500/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - ETA: 0s - loss: 0.0523 - accuracy: 1.0000\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 0s/step - loss: 0.0523 - accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "epochs = 500\n",
    "history = model.fit(padded_sequences, np.array(training_labels), epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43e192bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: chat_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: chat_model\\assets\n"
     ]
    }
   ],
   "source": [
    "# to save the trained model\n",
    "model.save(\"chat_model\")\n",
    "\n",
    "import pickle\n",
    "\n",
    "# to save the fitted tokenizer\n",
    "with open('tokenizer.pickle', 'wb') as handle:\n",
    "    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "# to save the fitted label encoder\n",
    "with open('label_encoder.pickle', 'wb') as ecn_file:\n",
    "    pickle.dump(lbl_encoder, ecn_file, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  }
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