7. Sequence Modeling.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>Natural Language Processing</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": {
    "colab": {},
    "colab_type": "code",
    "id": "moNmVfuvnImW"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import gzip\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "import time\n",
    "\n",
    "import random\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",
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "RANDOM_SEED = 123\n",
    "np.random.seed(RANDOM_SEED)\n",
    "\n",
    "import torch\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torchtext\n",
    "from torchtext.data import Field, BucketIterator, TabularDataset\n",
    "\n",
    "from torch.optim import Adam\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "import torchview\n",
    "from torchview import draw_graph\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       : 3.11.7\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 24.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 9c8c00758f3a2fa8e55e08f5aad405a157ca5dd2\n",
      "\n",
      "pandas    : 2.2.3\n",
      "matplotlib: 3.8.0\n",
      "torchtext : 0.6.0\n",
      "torch     : 2.3.0\n",
      "numpy     : 1.26.4\n",
      "watermark : 2.4.3\n",
      "torchview : 0.2.6\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 Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the text and label fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEXT = Field(tokenize='spacy', tokenizer_language='en_core_web_sm')\n",
    "LABEL = torchtext.data.LabelField(dtype=torch.long)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset with the proper schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = [('TEXT_COLUMN_NAME', TEXT), ('LABEL_COLUMN_NAME', LABEL)]\n",
    "\n",
    "dataset = TabularDataset(path='data/IMDB Dataset.csv', format='csv', skip_header=True, fields=fields)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset into training, validation, and test partitions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "WZ_4jiHVnMxN",
    "outputId": "dfa51c04-4845-44c3-f50b-d36d41f132b8"
   },
   "outputs": [],
   "source": [
    "X_train, X_test = dataset.split(split_ratio=[0.8, 0.2],random_state=random.seed(RANDOM_SEED))\n",
    "X_train, X_validation = X_train.split(split_ratio=[0.85, 0.15],random_state=random.seed(RANDOM_SEED))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training data: 34000\n",
      "Shape of test data: 10000\n",
      "Shape of validation data: 6000\n"
     ]
    }
   ],
   "source": [
    "print('Shape of training data:', len(X_train))\n",
    "print('Shape of test data:', len(X_test))\n",
    "print('Shape of validation data:', len(X_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L-TBwKWPslPa"
   },
   "source": [
    "Build the vocabulary based on the top \"vocabulary_size\" words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary_size = 20_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "e8uNrjdtn4A8",
    "outputId": "6cf499d7-7722-4da0-8576-ee0f218cc6e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 20002\n",
      "Number of classes: 2\n"
     ]
    }
   ],
   "source": [
    "TEXT.build_vocab(X_train, max_size=vocabulary_size)\n",
    "LABEL.build_vocab(X_train)\n",
    "\n",
    "print(f'Vocabulary size: {len(TEXT.vocab)}')\n",
    "print(f'Number of classes: {len(LABEL.vocab)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eIQ_zfKLwjKm"
   },
   "source": [
    "## Define Data Loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i7JiHR1stHNF"
   },
   "outputs": [],
   "source": [
    "train_loader, valid_loader, test_loader = BucketIterator.splits((X_train, X_validation, X_test),\n",
    "         batch_size=batch_size,\n",
    "         sort_within_batch=False,\n",
    "         sort_key=lambda x: len(x.TEXT_COLUMN_NAME),\n",
    "         device=device\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we are ready to define our models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple RNN model\n",
    "\n",
    "As the structure of the model is the same for RNN, GRU, and LSTMs, we implement just one of them for demonstration purposes. We can easily plugin another type of layer as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nQIUm5EjxFNa"
   },
   "outputs": [],
   "source": [
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_dim, embedding_size, hidden_size, output_size):\n",
    "        super().__init__()\n",
    "\n",
    "        self.embedding = nn.Embedding(input_dim, embedding_size)\n",
    "        self.rnn = nn.LSTM(embedding_size, hidden_size)        \n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, text):        \n",
    "        embedded = self.embedding(text)\n",
    "        output, (hidden, cell) = self.rnn(embedded)\n",
    "        hidden.squeeze_(0)        \n",
    "        output = self.fc(hidden)\n",
    "        \n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_size = 128\n",
    "hidden_size = 256\n",
    "num_classes = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ik3NF3faxFmZ"
   },
   "outputs": [],
   "source": [
    "model = RNN(len(TEXT.vocab), # vocabulary_size + 2 for <pad> and <unk>\n",
    "            embedding_size,\n",
    "            hidden_size,\n",
    "            num_classes \n",
    "           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNN(\n",
      "  (embedding): Embedding(20002, 128)\n",
      "  (rnn): LSTM(128, 256)\n",
      "  (fc): Linear(in_features=256, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.005\n",
    "n_epochs = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ik3NF3faxFmZ"
   },
   "outputs": [],
   "source": [
    "model = model.to(device)\n",
    "optimizer = Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lv9Ny9di6VcI"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T5t1Afn4xO11"
   },
   "outputs": [],
   "source": [
    "def compute_accuracy(model, data_loader, device):\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        correct_pred, num_examples = 0, 0\n",
    "\n",
    "        for i, (features, targets) in enumerate(data_loader):\n",
    "\n",
    "            features = features.to(device)\n",
    "            targets = targets.float().to(device)\n",
    "\n",
    "            logits = model(features)\n",
    "            loss = F.cross_entropy(logits, targets)\n",
    "            _, predicted_labels = torch.max(logits, 1)\n",
    "\n",
    "            num_examples += targets.size(0)\n",
    "            correct_pred += (predicted_labels == targets).sum()\n",
    "            \n",
    "    return correct_pred.float()/num_examples * 100, loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this [notebook](https://github.com/rasbt/stat453-deep-learning-ss21/tree/main/L15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1836
    },
    "colab_type": "code",
    "id": "EABZM8Vo0ilB",
    "outputId": "5d45e293-9909-4588-e793-8dfaf72e5c67",
    "scrolled": false
   },
   "outputs": [
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 1:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/015 | Batch 000/266 | Loss: 0.7136\n",
      "Epoch: 001/015 | Batch 050/266 | Loss: 0.6994\n",
      "Epoch: 001/015 | Batch 100/266 | Loss: 0.6898\n",
      "Epoch: 001/015 | Batch 150/266 | Loss: 0.6926\n",
      "Epoch: 001/015 | Batch 200/266 | Loss: 0.6961\n",
      "Epoch: 001/015 | Batch 250/266 | Loss: 0.6951\n",
      "training accuracy: 49.94%\n",
      "valid accuracy: 49.82%\n",
      "Time elapsed: 1.06 min\n"
     ]
    },
    {
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       "model_id": "9202ad78296d440e9dae3ceb2f7e6376",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 2:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 002/015 | Batch 000/266 | Loss: 0.6954\n",
      "Epoch: 002/015 | Batch 050/266 | Loss: 0.6933\n",
      "Epoch: 002/015 | Batch 100/266 | Loss: 0.6932\n",
      "Epoch: 002/015 | Batch 150/266 | Loss: 0.6926\n",
      "Epoch: 002/015 | Batch 200/266 | Loss: 0.6930\n",
      "Epoch: 002/015 | Batch 250/266 | Loss: 0.6915\n",
      "training accuracy: 50.14%\n",
      "valid accuracy: 49.38%\n",
      "Time elapsed: 2.04 min\n"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "Epoch: 3:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 003/015 | Batch 000/266 | Loss: 0.6938\n",
      "Epoch: 003/015 | Batch 050/266 | Loss: 0.6949\n",
      "Epoch: 003/015 | Batch 100/266 | Loss: 0.6908\n",
      "Epoch: 003/015 | Batch 150/266 | Loss: 0.6959\n",
      "Epoch: 003/015 | Batch 200/266 | Loss: 0.6924\n",
      "Epoch: 003/015 | Batch 250/266 | Loss: 0.6910\n",
      "training accuracy: 50.25%\n",
      "valid accuracy: 49.63%\n",
      "Time elapsed: 2.99 min\n"
     ]
    },
    {
     "data": {
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       "model_id": "8dd12c3c4288432a81ac6983dbde736a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 4:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 004/015 | Batch 000/266 | Loss: 0.6919\n",
      "Epoch: 004/015 | Batch 050/266 | Loss: 0.6913\n",
      "Epoch: 004/015 | Batch 100/266 | Loss: 0.6991\n",
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      "Epoch: 004/015 | Batch 200/266 | Loss: 0.6953\n",
      "Epoch: 004/015 | Batch 250/266 | Loss: 0.6928\n",
      "training accuracy: 50.09%\n",
      "valid accuracy: 50.45%\n",
      "Time elapsed: 3.85 min\n"
     ]
    },
    {
     "data": {
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       "model_id": "62c94930f11842cc94df067baae33c31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 005/015 | Batch 000/266 | Loss: 0.6930\n",
      "Epoch: 005/015 | Batch 050/266 | Loss: 0.6906\n",
      "Epoch: 005/015 | Batch 100/266 | Loss: 0.6884\n",
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      "Epoch: 005/015 | Batch 200/266 | Loss: 0.6895\n",
      "Epoch: 005/015 | Batch 250/266 | Loss: 0.6922\n",
      "training accuracy: 50.39%\n",
      "valid accuracy: 50.07%\n",
      "Time elapsed: 4.70 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a09c01214ae848bc9900e90da8121d59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 006/015 | Batch 000/266 | Loss: 0.6860\n",
      "Epoch: 006/015 | Batch 050/266 | Loss: 0.6872\n",
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      "Epoch: 006/015 | Batch 200/266 | Loss: 0.6886\n",
      "Epoch: 006/015 | Batch 250/266 | Loss: 0.6975\n",
      "training accuracy: 50.15%\n",
      "valid accuracy: 50.95%\n",
      "Time elapsed: 5.57 min\n"
     ]
    },
    {
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       "model_id": "3bc94160ec8c4b4ca59290525ea3cd31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 007/015 | Batch 000/266 | Loss: 0.6876\n",
      "Epoch: 007/015 | Batch 050/266 | Loss: 0.6885\n",
      "Epoch: 007/015 | Batch 100/266 | Loss: 0.6894\n",
      "Epoch: 007/015 | Batch 150/266 | Loss: 0.6884\n",
      "Epoch: 007/015 | Batch 200/266 | Loss: 0.6939\n",
      "Epoch: 007/015 | Batch 250/266 | Loss: 0.6451\n",
      "training accuracy: 68.65%\n",
      "valid accuracy: 66.77%\n",
      "Time elapsed: 6.43 min\n"
     ]
    },
    {
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       "model_id": "b50e05849647495593c69946a51777ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 008/015 | Batch 000/266 | Loss: 0.5576\n",
      "Epoch: 008/015 | Batch 050/266 | Loss: 0.6149\n",
      "Epoch: 008/015 | Batch 100/266 | Loss: 0.4773\n",
      "Epoch: 008/015 | Batch 150/266 | Loss: 0.3983\n",
      "Epoch: 008/015 | Batch 200/266 | Loss: 0.3978\n",
      "Epoch: 008/015 | Batch 250/266 | Loss: 0.5196\n",
      "training accuracy: 87.54%\n",
      "valid accuracy: 83.55%\n",
      "Time elapsed: 7.31 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2fdbb12908a46e9b2080cbfcbd33294",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 9:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 009/015 | Batch 000/266 | Loss: 0.2548\n",
      "Epoch: 009/015 | Batch 050/266 | Loss: 0.2753\n",
      "Epoch: 009/015 | Batch 100/266 | Loss: 0.4325\n",
      "Epoch: 009/015 | Batch 150/266 | Loss: 0.3712\n",
      "Epoch: 009/015 | Batch 200/266 | Loss: 0.4064\n",
      "Epoch: 009/015 | Batch 250/266 | Loss: 0.2854\n",
      "training accuracy: 90.88%\n",
      "valid accuracy: 85.28%\n",
      "Time elapsed: 8.21 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4570d7b37d084c4982469734dbc5507a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 10:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 010/015 | Batch 000/266 | Loss: 0.1848\n",
      "Epoch: 010/015 | Batch 050/266 | Loss: 0.1800\n",
      "Epoch: 010/015 | Batch 100/266 | Loss: 0.2642\n",
      "Epoch: 010/015 | Batch 150/266 | Loss: 0.2920\n",
      "Epoch: 010/015 | Batch 200/266 | Loss: 0.2054\n",
      "Epoch: 010/015 | Batch 250/266 | Loss: 0.2201\n",
      "training accuracy: 93.53%\n",
      "valid accuracy: 86.18%\n",
      "Time elapsed: 9.12 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac8709fcc5eb4364ada699a9934a6e9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 11:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 011/015 | Batch 000/266 | Loss: 0.1547\n",
      "Epoch: 011/015 | Batch 050/266 | Loss: 0.1785\n",
      "Epoch: 011/015 | Batch 100/266 | Loss: 0.1617\n",
      "Epoch: 011/015 | Batch 150/266 | Loss: 0.0838\n",
      "Epoch: 011/015 | Batch 200/266 | Loss: 0.2815\n",
      "Epoch: 011/015 | Batch 250/266 | Loss: 0.2952\n",
      "training accuracy: 95.04%\n",
      "valid accuracy: 86.00%\n",
      "Time elapsed: 9.93 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "285d5c68f33948bc8e3372db01549100",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 12:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 012/015 | Batch 000/266 | Loss: 0.1977\n",
      "Epoch: 012/015 | Batch 050/266 | Loss: 0.1446\n",
      "Epoch: 012/015 | Batch 100/266 | Loss: 0.1537\n",
      "Epoch: 012/015 | Batch 150/266 | Loss: 0.1356\n",
      "Epoch: 012/015 | Batch 200/266 | Loss: 0.2212\n",
      "Epoch: 012/015 | Batch 250/266 | Loss: 0.0900\n",
      "training accuracy: 96.53%\n",
      "valid accuracy: 86.62%\n",
      "Time elapsed: 10.70 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4e5c782c0bb468ead61ea7500ca2bd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 13:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 013/015 | Batch 000/266 | Loss: 0.1361\n",
      "Epoch: 013/015 | Batch 050/266 | Loss: 0.0600\n",
      "Epoch: 013/015 | Batch 100/266 | Loss: 0.0953\n",
      "Epoch: 013/015 | Batch 150/266 | Loss: 0.1170\n",
      "Epoch: 013/015 | Batch 200/266 | Loss: 0.2049\n",
      "Epoch: 013/015 | Batch 250/266 | Loss: 0.0634\n",
      "training accuracy: 97.55%\n",
      "valid accuracy: 86.08%\n",
      "Time elapsed: 11.47 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7adaf6c168504049b57c1d6f13adea97",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 14:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 014/015 | Batch 000/266 | Loss: 0.0310\n",
      "Epoch: 014/015 | Batch 050/266 | Loss: 0.1754\n",
      "Epoch: 014/015 | Batch 100/266 | Loss: 0.0735\n",
      "Epoch: 014/015 | Batch 150/266 | Loss: 0.1243\n",
      "Epoch: 014/015 | Batch 200/266 | Loss: 0.1415\n",
      "Epoch: 014/015 | Batch 250/266 | Loss: 0.0495\n",
      "training accuracy: 98.08%\n",
      "valid accuracy: 86.80%\n",
      "Time elapsed: 12.25 min\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a1c371bda5145faa51de5feb465c164",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch: 15:   0%|          | 0/266 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 015/015 | Batch 000/266 | Loss: 0.0661\n",
      "Epoch: 015/015 | Batch 050/266 | Loss: 0.0615\n",
      "Epoch: 015/015 | Batch 100/266 | Loss: 0.0413\n",
      "Epoch: 015/015 | Batch 150/266 | Loss: 0.0564\n",
      "Epoch: 015/015 | Batch 200/266 | Loss: 0.0986\n",
      "Epoch: 015/015 | Batch 250/266 | Loss: 0.0373\n",
      "training accuracy: 98.67%\n",
      "valid accuracy: 86.52%\n",
      "Time elapsed: 13.02 min\n",
      "Total Training Time: 13.02 min\n",
      "Test accuracy: 87.29%\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "history = {\n",
    "        'train_loss': [],\n",
    "        'train_acc': [],\n",
    "        'val_loss': [],\n",
    "        'val_acc': [],\n",
    "        'epochs': n_epochs\n",
    "    }\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    model.train()\n",
    "    for batch_idx, batch_data in tqdm(enumerate(train_loader), \n",
    "                                      total=len(train_loader), \n",
    "                                      desc='Epoch: %u' % (epoch+1),\n",
    "                                      leave=True):\n",
    "        \n",
    "        text = batch_data.TEXT_COLUMN_NAME.to(device)\n",
    "        labels = batch_data.LABEL_COLUMN_NAME.to(device)\n",
    "\n",
    "        # Forward pass\n",
    "        logits = model(text)\n",
    "        \n",
    "        # Back propagation\n",
    "        loss = F.cross_entropy(logits, labels)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        \n",
    "        # update weights\n",
    "        optimizer.step()\n",
    "        \n",
    "        if not batch_idx % 50:\n",
    "            print (f'Epoch: {epoch+1:03d}/{n_epochs:03d} | '\n",
    "                   f'Batch {batch_idx:03d}/{len(train_loader):03d} | '\n",
    "                   f'Loss: {loss:.4f}')\n",
    "\n",
    "    with torch.set_grad_enabled(False):\n",
    "        print(f'training accuracy: '\n",
    "              f'{compute_accuracy(model, train_loader, device)[0]:.2f}%'\n",
    "              f'\\nvalid accuracy: '\n",
    "              f'{compute_accuracy(model, valid_loader, device)[0]:.2f}%')\n",
    "    \n",
    "    history['train_loss'].append(loss.to('cpu').detach().numpy())\n",
    "    history['train_acc'].append(compute_accuracy(model, train_loader, device)[0].to('cpu').detach().numpy())\n",
    "    \n",
    "    val_acc, val_loss = compute_accuracy(model, valid_loader, device)\n",
    "    \n",
    "    history['val_acc'].append(val_acc.to('cpu').detach().numpy())\n",
    "    history['val_loss'].append(val_loss.to('cpu').detach().numpy())\n",
    "\n",
    "    print(f'Time elapsed: {(time.time() - start_time)/60:.2f} min')\n",
    "    \n",
    "print(f'Total Training Time: {(time.time() - start_time)/60:.2f} min')\n",
    "print(f'Test accuracy: {compute_accuracy(model, test_loader, device)[0]:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):    \n",
    "    fig, ax_lst = plt.subplots(1, 2, sharex=True, sharey=False)\n",
    "    \n",
    "    epochs = range(history['epochs'])\n",
    "    \n",
    "    ax_lst[0].plot(epochs, history['train_loss'], label='Training')\n",
    "    ax_lst[0].plot(epochs, 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",
    "    ax_lst[1].plot(epochs, history['train_acc'], label='Training')\n",
    "    ax_lst[1].plot(epochs, history['val_acc'], label='Testing')\n",
    "    ax_lst[1].set_ylabel('Accuracy')\n",
    "    ax_lst[1].set_xlabel('Epoch')\n",
    "    ax_lst[1].set_xticks(epochs)\n",
    "\n",
    "    ax_lst[1].legend()\n",
    "    \n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "<center>\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</center>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "rnn_lstm_packed_imdb.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.11.7"
  },
  "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
}