classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AXNnzTQkhHlu",
    "outputId": "00a7f90c-a5e7-44d3-a6db-8b417aa783ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version:  2.4.0-dev20200902\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from packaging import version\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "print(\"TensorFlow version: \", tf.__version__)\n",
    "assert version.parse(tf.__version__).release[0] >= 2, \\\n",
    "    \"This notebook requires TensorFlow 2.0 or above.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "MD_U7nUXhSZp",
    "outputId": "fcf0c8ac-6cb8-460f-fb6b-c386aefca865"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'2.5.0a20201219'"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorboard\n",
    "tensorboard.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "nNc2mdWQhxQY"
   },
   "outputs": [],
   "source": [
    "logdir=\"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8nD8kzSlff5T",
    "outputId": "67d98083-5c1c-4b06-98e9-2eaee2ea0d7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[31mERROR: tf-nightly 2.4.0.dev20200902 has requirement numpy<1.19.0,>=1.16.0, but you'll have numpy 1.19.4 which is incompatible.\u001b[0m\n",
      "Installing collected packages: tf-estimator-nightly, h5py, tf-nightly\n",
      "  Found existing installation: tf-estimator-nightly 2.5.0.dev2020121901\n",
      "    Uninstalling tf-estimator-nightly-2.5.0.dev2020121901:\n",
      "      Successfully uninstalled tf-estimator-nightly-2.5.0.dev2020121901\n",
      "  Found existing installation: h5py 3.1.0\n",
      "    Uninstalling h5py-3.1.0:\n",
      "      Successfully uninstalled h5py-3.1.0\n",
      "  Found existing installation: tf-nightly 2.5.0.dev20201219\n",
      "    Uninstalling tf-nightly-2.5.0.dev20201219:\n",
      "      Successfully uninstalled tf-nightly-2.5.0.dev20201219\n",
      "Successfully installed h5py-2.10.0 tf-estimator-nightly-2.4.0.dev2020090201 tf-nightly-2.4.0.dev20200902\n"
     ]
    }
   ],
   "source": [
    "pip install tflite-model-maker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "ChfeA_tVjGMZ"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import tensorflow as tf\n",
    "assert tf.__version__.startswith('2')\n",
    "\n",
    "from tflite_model_maker import configs\n",
    "from tflite_model_maker import ExportFormat\n",
    "from tflite_model_maker import model_spec\n",
    "from tflite_model_maker import text_classifier\n",
    "from tflite_model_maker import TextClassifierDataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "dC1qruaxjK5Y"
   },
   "outputs": [],
   "source": [
    "spec = model_spec.get('mobilebert_classifier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "FRMc-p6ckA23"
   },
   "outputs": [],
   "source": [
    "train_data = TextClassifierDataLoader.from_csv(\n",
    "      filename=os.path.join(os.path.join('/content/yotube_emenem_cleaned_train.csv')),\n",
    "      text_column='CONTENT',\n",
    "      label_column='CLASS',\n",
    "      model_spec=spec)\n",
    "test_data = TextClassifierDataLoader.from_csv(\n",
    "      filename=os.path.join(os.path.join('/content/yotube_emenem_cleaned_test.csv')),\n",
    "      text_column='CONTENT',\n",
    "      label_column='CLASS',\n",
    "      model_spec=spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rOVanOpIkQ8X",
    "outputId": "1720508b-3e84-4dff-dce8-a9fe105b92dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Retraining the models...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Retraining the models...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "7/7 [==============================] - 204s 20s/step - loss: 4.8256 - test_accuracy: 0.5299\n",
      "Epoch 2/6\n",
      "7/7 [==============================] - 132s 19s/step - loss: 1.3581 - test_accuracy: 0.5426\n",
      "Epoch 3/6\n",
      "7/7 [==============================] - 131s 19s/step - loss: 0.5651 - test_accuracy: 0.7347\n",
      "Epoch 4/6\n",
      "7/7 [==============================] - 130s 19s/step - loss: 0.3895 - test_accuracy: 0.8130\n",
      "Epoch 5/6\n",
      "7/7 [==============================] - 132s 19s/step - loss: 0.3392 - test_accuracy: 0.8664\n",
      "Epoch 6/6\n",
      "7/7 [==============================] - 131s 19s/step - loss: 0.1993 - test_accuracy: 0.9254\n"
     ]
    }
   ],
   "source": [
    "model = text_classifier.create(train_data, model_spec=spec,epochs=6  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xDH77eBHmA2o",
    "outputId": "d014054c-8844-4d40-ce73-73e53c4461d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 19s 3s/step - loss: 0.1666 - test_accuracy: 0.9375\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "TRVf5eeanI5Y"
   },
   "outputs": [],
   "source": [
    "#Since MobileBERT is too big for on-device applications \n",
    "#use dynamic range quantization on the model to compress it by almost 4x with minimal performance degradation.\n",
    "config = configs.QuantizationConfig.create_dynamic_range_quantization(optimizations=[tf.lite.Optimize.OPTIMIZE_FOR_LATENCY])\n",
    "config._experimental_new_quantizer = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XaJe-4FrnTAv",
    "outputId": "9f2eefbf-6b65-4ad3-e5bb-db25f0435731",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpil6dm4n0/saved_model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpil6dm4n0/saved_model/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Vocab file and label file are inside the TFLite model with metadata.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Vocab file and label file are inside the TFLite model with metadata.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saved vocabulary in /tmp/tmpozc9mbt3/vocab.txt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saved vocabulary in /tmp/tmpozc9mbt3/vocab.txt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving labels in /tmp/tmpozc9mbt3/labels.txt.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving labels in /tmp/tmpozc9mbt3/labels.txt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished populating metadata and associated file to the model:\n",
      "mobilebert/model.tflite\n",
      "The metadata json file has been saved to:\n",
      "mobilebert/model.json\n",
      "The associated file that has been been packed to the model is:\n",
      "['labels.txt', 'vocab.txt']\n"
     ]
    }
   ],
   "source": [
    "model.export(export_dir='mobilebert/', quantization_config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ylsUB9VldUAD",
    "outputId": "62e06fd1-e43d-4590-864b-ab4d603eb262"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_word_ids (InputLayer)     [(1, 128)]           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_mask (InputLayer)         [(1, 128)]           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_type_ids (InputLayer)     [(1, 128)]           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "hub_keras_layer_v1v2_1 (HubKera (None, 512)          24581888    input_word_ids[0][0]             \n",
      "                                                                 input_mask[0][0]                 \n",
      "                                                                 input_type_ids[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 512)          0           hub_keras_layer_v1v2_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 2)            1026        dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 24,582,914\n",
      "Trainable params: 24,582,914\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Youtube_cleaned.ipynb",
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  "language_info": {
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