5. Sequence Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>NLP with Deep Learning for Everyone</h1>\n",
    "<h1>Sequence Modeling</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "\n",
    "import gzip\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import tqdm\n",
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding, SimpleRNN\n",
    "from keras.layers import LSTM, GRU\n",
    "from keras.layers import Dense\n",
    "from keras.datasets import imdb\n",
    "from keras_preprocessing import sequence\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.5\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 23.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 3022f3d8aeb2c24ebf2d47d4e9999181a7ad777d\n",
      "\n",
      "pandas             : 2.0.3\n",
      "json               : 2.0.9\n",
      "keras_preprocessing: 1.1.2\n",
      "keras              : 2.12.0\n",
      "matplotlib         : 3.7.2\n",
      "tensorflow         : 2.12.0\n",
      "numpy              : 1.23.5\n",
      "watermark          : 2.4.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the IMDB dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words = 10000\n",
    "max_words = 500\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
    "\n",
    "word_dict = imdb.get_word_index()\n",
    "word_list = [\"<PAD>\", \"<START>\", \"<UNK>\", \"<UNUSED>\"]\n",
    "word_list.extend([k for k, v in sorted(word_dict.items(), key = lambda x:x[1])])\n",
    "word_list = np.array(word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
    "X_test = sequence.pad_sequences(X_test, maxlen=max_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SimpleRNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, None, 32)          320000    \n",
      "                                                                 \n",
      " simple_rnn (SimpleRNN)      (None, 32)                2080      \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 322,113\n",
      "Trainable params: 322,113\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words, 32))\n",
    "## <==> ##\n",
    "model.add(SimpleRNN(32))\n",
    "## <==> ##\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "196/196 [==============================] - 15s 75ms/step - loss: 0.5306 - accuracy: 0.7390 - val_loss: 0.4341 - val_accuracy: 0.8141\n",
      "Epoch 2/10\n",
      "196/196 [==============================] - 15s 78ms/step - loss: 0.3299 - accuracy: 0.8681 - val_loss: 0.3871 - val_accuracy: 0.8300\n",
      "Epoch 3/10\n",
      "196/196 [==============================] - 15s 77ms/step - loss: 0.2342 - accuracy: 0.9103 - val_loss: 0.3442 - val_accuracy: 0.8569\n",
      "Epoch 4/10\n",
      "196/196 [==============================] - 15s 75ms/step - loss: 0.1720 - accuracy: 0.9382 - val_loss: 0.3964 - val_accuracy: 0.8507\n",
      "Epoch 5/10\n",
      "196/196 [==============================] - 15s 74ms/step - loss: 0.1327 - accuracy: 0.9542 - val_loss: 0.4275 - val_accuracy: 0.8538\n",
      "Epoch 6/10\n",
      "196/196 [==============================] - 15s 74ms/step - loss: 0.0924 - accuracy: 0.9709 - val_loss: 0.4483 - val_accuracy: 0.8490\n",
      "Epoch 7/10\n",
      "196/196 [==============================] - 12s 63ms/step - loss: 0.0657 - accuracy: 0.9799 - val_loss: 0.4518 - val_accuracy: 0.8426\n",
      "Epoch 8/10\n",
      "196/196 [==============================] - 11s 56ms/step - loss: 0.0448 - accuracy: 0.9866 - val_loss: 0.5835 - val_accuracy: 0.8438\n",
      "Epoch 9/10\n",
      "196/196 [==============================] - 11s 55ms/step - loss: 0.0242 - accuracy: 0.9936 - val_loss: 0.6262 - val_accuracy: 0.8442\n",
      "Epoch 10/10\n",
      "196/196 [==============================] - 11s 55ms/step - loss: 0.0327 - accuracy: 0.9908 - val_loss: 0.6221 - val_accuracy: 0.8311\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train,\n",
    "                    validation_data=(X_test, y_test), # The testing data for validation\n",
    "                    epochs=10, # How many epochs to train for\n",
    "                    batch_size=128, # The batch size. Gradients are updated after each batch \n",
    "                    verbose=1) # The level of detail in the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):    \n",
    "    fig, ax_lst = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "\n",
    "    epochs = np.array(history.epoch) + 1\n",
    "    \n",
    "    ax_lst[0].plot(epochs, history.history['loss'], label='Training')\n",
    "    ax_lst[0].plot(epochs, history.history['val_loss'], label='Testing')\n",
    "    ax_lst[0].set_ylabel('Loss')\n",
    "    ax_lst[0].set_xlabel('Epoch')\n",
    "    ax_lst[0].set_xticks(epochs)\n",
    "    \n",
    "    best_epoch = np.argmin(history.history['val_loss']) + 1\n",
    "    ax_lst[0].axvline(x=best_epoch, linestyle=':', color=colors[2])\n",
    "    ax_lst[0].legend()\n",
    "    \n",
    "\n",
    "    ax_lst[1].plot(epochs, history.history['accuracy'], label='Training')\n",
    "    ax_lst[1].plot(epochs, history.history['val_accuracy'], label='Testing')\n",
    "    ax_lst[1].set_ylabel('Accuracy')\n",
    "    ax_lst[1].set_xlabel('Epoch')\n",
    "    ax_lst[1].set_xticks(epochs)\n",
    "    ax_lst[1].axvline(x=best_epoch, linestyle=':', color=colors[2])\n",
    "\n",
    "\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_1 (Embedding)     (None, None, 32)          320000    \n",
      "                                                                 \n",
      " gru (GRU)                   (None, 32)                6336      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 326,369\n",
      "Trainable params: 326,369\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words, 32))\n",
    "## <==> ##\n",
    "model.add(GRU(32))\n",
    "## <==> ##\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "196/196 [==============================] - 26s 132ms/step - loss: 0.4863 - accuracy: 0.7470 - val_loss: 0.3360 - val_accuracy: 0.8592\n",
      "Epoch 2/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.2531 - accuracy: 0.9006 - val_loss: 0.3057 - val_accuracy: 0.8741\n",
      "Epoch 3/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.2001 - accuracy: 0.9253 - val_loss: 0.3163 - val_accuracy: 0.8747\n",
      "Epoch 4/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.1543 - accuracy: 0.9441 - val_loss: 0.3437 - val_accuracy: 0.8723\n",
      "Epoch 5/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.1225 - accuracy: 0.9588 - val_loss: 0.3660 - val_accuracy: 0.8668\n",
      "Epoch 6/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.1081 - accuracy: 0.9642 - val_loss: 0.3942 - val_accuracy: 0.8604\n",
      "Epoch 7/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.0817 - accuracy: 0.9738 - val_loss: 0.4624 - val_accuracy: 0.8564\n",
      "Epoch 8/10\n",
      "196/196 [==============================] - 25s 130ms/step - loss: 0.0745 - accuracy: 0.9757 - val_loss: 0.4638 - val_accuracy: 0.8554\n",
      "Epoch 9/10\n",
      "196/196 [==============================] - 26s 131ms/step - loss: 0.0554 - accuracy: 0.9834 - val_loss: 0.5498 - val_accuracy: 0.8587\n",
      "Epoch 10/10\n",
      "196/196 [==============================] - 25s 129ms/step - loss: 0.0384 - accuracy: 0.9888 - val_loss: 0.5906 - val_accuracy: 0.8610\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, y_train,\n",
    "                    validation_data=(X_test, y_test), # The testing data for validation\n",
    "                    epochs=10, # How many epochs to train for\n",
    "                    batch_size=128, # The batch size. Gradients are updated after each batch \n",
    "                    verbose=1) # The level of detail in the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_2 (Embedding)     (None, None, 32)          320000    \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 32)                8320      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 328,353\n",
      "Trainable params: 328,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words, 32))\n",
    "## <==> ##\n",
    "model.add(LSTM(32))\n",
    "## <==> ##\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "196/196 [==============================] - 30s 151ms/step - loss: 0.4760 - accuracy: 0.7738 - val_loss: 0.3532 - val_accuracy: 0.8568\n",
      "Epoch 2/10\n",
      "196/196 [==============================] - 29s 150ms/step - loss: 0.2831 - accuracy: 0.8870 - val_loss: 0.2931 - val_accuracy: 0.8736\n",
      "Epoch 3/10\n",
      "196/196 [==============================] - 29s 150ms/step - loss: 0.1864 - accuracy: 0.9300 - val_loss: 0.3037 - val_accuracy: 0.8744\n",
      "Epoch 4/10\n",
      "196/196 [==============================] - 30s 152ms/step - loss: 0.1609 - accuracy: 0.9414 - val_loss: 0.3341 - val_accuracy: 0.8776\n",
      "Epoch 5/10\n",
      "196/196 [==============================] - 29s 150ms/step - loss: 0.1283 - accuracy: 0.9535 - val_loss: 0.3462 - val_accuracy: 0.8648\n",
      "Epoch 6/10\n",
      "196/196 [==============================] - 30s 154ms/step - loss: 0.0959 - accuracy: 0.9679 - val_loss: 0.4441 - val_accuracy: 0.8612\n",
      "Epoch 7/10\n",
      "196/196 [==============================] - 31s 156ms/step - loss: 0.0832 - accuracy: 0.9734 - val_loss: 0.4328 - val_accuracy: 0.8651\n",
      "Epoch 8/10\n",
      "196/196 [==============================] - 31s 156ms/step - loss: 0.0664 - accuracy: 0.9800 - val_loss: 0.5465 - val_accuracy: 0.8598\n",
      "Epoch 9/10\n",
      "196/196 [==============================] - 30s 155ms/step - loss: 0.0604 - accuracy: 0.9816 - val_loss: 0.4985 - val_accuracy: 0.8591\n",
      "Epoch 10/10\n",
      "196/196 [==============================] - 30s 156ms/step - loss: 0.0784 - accuracy: 0.9734 - val_loss: 0.4961 - val_accuracy: 0.8590\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(X_train, y_train,\n",
    "                    validation_data=(X_test, y_test), # The testing data for validation\n",
    "                    epochs=10, # How many epochs to train for\n",
    "                    batch_size=128, # The batch size. Gradients are updated after each batch \n",
    "                    verbose=1) # The level of detail in the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}