Model_Training_2.ipynb

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  "metadata": {
    "colab": {
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Projects-/blob/master/Model_Training_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1-Import library**"
      ],
      "metadata": {
        "id": "3HLNoZfkBUGc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "import json\n",
        "import pickle\n",
        "\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout\n",
        "from keras.optimizers import SGD\n",
        "import random"
      ],
      "metadata": {
        "id": "UkuJaCW9bT6u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "id": "TbN0QbsScujH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**2-Dataset Loading**"
      ],
      "metadata": {
        "id": "CpC21eawvwSY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R60NmwbGDNp2",
        "outputId": "ce2de1b9-b58b-4b34-eba2-0bcffae5ed86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words=[]\n",
        "classes = []\n",
        "documents = []\n",
        "ignore_words = ['?', '!']\n",
        "data_file = open('/content/drive/MyDrive/Research /Research Proposal /Dataset/intentsnew (6).json').read()\n",
        "intents = json.loads(data_file)"
      ],
      "metadata": {
        "id": "CfFYn2f_b5u9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3-Data Preprocessing**"
      ],
      "metadata": {
        "id": "5Yl5GZnxZnI3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for intent in intents['intents']:\n",
        "    for pattern in intent['patterns']:\n",
        "\n",
        "        #tokenize each word\n",
        "        w = nltk.word_tokenize(pattern)\n",
        "        words.extend(w)\n",
        "        # a dd documents in the corpus\n",
        "        documents.append((w, intent['tag']))\n",
        "\n",
        "        # add to our classes list\n",
        "        if intent['tag'] not in classes:\n",
        "            classes.append(intent['tag'])"
      ],
      "metadata": {
        "id": "ICA7KJTRD768"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lemmaztize and lower each word and remove duplicates\n",
        "nltk.download('wordnet')\n",
        "words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]\n",
        "words = sorted(list(set(words)))\n",
        "# sort classes\n",
        "classes = sorted(list(set(classes)))\n",
        "# documents = combination between patterns and intents\n",
        "print (len(documents), \"documents\")\n",
        "# classes = intents\n",
        "print (len(classes), \"classes\", classes)\n",
        "# words = all words, vocabulary\n",
        "print (len(words), \"unique lemmatized words\", words)\n",
        "pickle.dump(words,open('texts.pkl','wb'))\n",
        "pickle.dump(classes,open('labels.pkl','wb'))\n"
      ],
      "metadata": {
        "id": "-azsX0L8c7hX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create our training data\n",
        "training = []\n",
        "# create an empty array for our output\n",
        "output_empty = [0] * len(classes)\n",
        "# training set, bag of words for each sentence\n",
        "for doc in documents:\n",
        "    # initialize our bag of words\n",
        "    bag = []\n",
        "    # list of tokenized words for the pattern\n",
        "    pattern_words = doc[0]\n",
        "    # lemmatize each word - create base word, in attempt to represent related words\n",
        "    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]\n",
        "    # create our bag of words array with 1, if word match found in current pattern\n",
        "    for w in words:\n",
        "        bag.append(1) if w in pattern_words else bag.append(0)\n",
        "\n",
        "    # output is a '0' for each tag and '1' for current tag (for each pattern)\n",
        "    output_row = list(output_empty)\n",
        "    output_row[classes.index(doc[1])] = 1\n",
        "\n",
        "    training.append([bag, output_row])\n",
        "# shuffle our features and turn into np.array\n",
        "random.shuffle(training)\n",
        "training = np.array(training)\n",
        "# create train and test lists. X - patterns, Y - intents\n",
        "train_x = list(training[:,0])\n",
        "train_y = list(training[:,1])\n",
        "print(\"Training data created\")\n"
      ],
      "metadata": {
        "id": "ZpeeTqAQdIUO",
        "outputId": "5c811704-a703-4873-ec88-81a6fbb1cac0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data created\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-14f4db91aa1f>:24: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  training = np.array(training)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4-Model Training**"
      ],
      "metadata": {
        "id": "iiA5mfO_bf5G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4.1-Neural Netowrk**"
      ],
      "metadata": {
        "id": "P-gDV4j8l0L0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model - 3 layers. First layer 128 neurons, second layer 64 neurons and 3rd output layer contains number of neurons\n",
        "# equal to number of intents to predict output intent with softmax\n",
        "model = Sequential()\n",
        "model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(len(train_y[0]), activation='softmax'))\n",
        "\n",
        "# Compile model. Stochastic gradient descent with Nesterov accelerated gradient gives good results for this model\n",
        "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
        "\n",
        "#fitting and saving the model\n",
        "hist = model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)\n"
      ],
      "metadata": {
        "id": "pyIo9YkVdORl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.1.1 Model Saving**"
      ],
      "metadata": {
        "id": "Em4aCw6ImU3R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('chat_model', hist)\n",
        "print(\"model created\")"
      ],
      "metadata": {
        "id": "GwFda9VsmgQC",
        "outputId": "ccf8d6e4-6bef-4d81-92fd-245e2fc38a2f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model created\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4.2- LSTM**"
      ],
      "metadata": {
        "id": "5HvFngP6nzWA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense"
      ],
      "metadata": {
        "id": "1nVA9smHqt4v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the LSTM network\n",
        "model = Sequential()\n",
        "model.add(LSTM(8, input_shape=(5, 1), return_sequences=False))\n",
        "model.add(Dense(1))"
      ],
      "metadata": {
        "id": "kcFSuMKmq2C_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(loss='mse', optimizer='adam')"
      ],
      "metadata": {
        "id": "qO4Pu8Eaq68Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(np.array(train_x), np.array(train_y), epochs=100)"
      ],
      "metadata": {
        "id": "pISQug2TrAX3",
        "outputId": "c76ac1a4-5c42-4dea-a94b-dc4cdf4d20ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-6dc742151f82>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Train the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/input_spec.py\", line 298, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_6\" is incompatible with the layer: expected shape=(None, 100), found shape=(None, 391)\n"
          ]
        }
      ]
    }
  ]
}