pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vY4SK0xKAJgm"
   },
   "source": [
    "# Model Zoo -- RNN with GRU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sc6xejhY-NzZ"
   },
   "source": [
    "Demo of a simple RNN for sentiment classification (here: a binary classification problem with two labels, positive and negative) using GRU (Gated Rectified Unit) cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "moNmVfuvnImW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.7.1\n",
      "IPython 7.4.0\n",
      "\n",
      "torch 1.0.1.post2\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchtext import data\n",
    "from torchtext import datasets\n",
    "import time\n",
    "import random\n",
    "\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GSRL42Qgy8I8"
   },
   "source": [
    "## General Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OvW1RgfepCBq"
   },
   "outputs": [],
   "source": [
    "RANDOM_SEED = 123\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "\n",
    "VOCABULARY_SIZE = 20000\n",
    "LEARNING_RATE = 1e-4\n",
    "BATCH_SIZE = 128\n",
    "NUM_EPOCHS = 15\n",
    "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "EMBEDDING_DIM = 128\n",
    "HIDDEN_DIM = 256\n",
    "OUTPUT_DIM = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mQMmKUEisW4W"
   },
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4GnH64XvsV8n"
   },
   "source": [
    "Load the IMDB Movie Review dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "WZ_4jiHVnMxN",
    "outputId": "6653638a-24d2-49a9-988a-9d9165cec548"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloading aclImdb_v1.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "aclImdb_v1.tar.gz: 100%|██████████| 84.1M/84.1M [00:08<00:00, 9.50MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Train: 20000\n",
      "Num Valid: 5000\n",
      "Num Test: 25000\n"
     ]
    }
   ],
   "source": [
    "TEXT = data.Field(tokenize='spacy',\n",
    "                  include_lengths=True) # necessary for packed_padded_sequence\n",
    "LABEL = data.LabelField(dtype=torch.float)\n",
    "train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)\n",
    "train_data, valid_data = train_data.split(random_state=random.seed(RANDOM_SEED),\n",
    "                                          split_ratio=0.8)\n",
    "\n",
    "print(f'Num Train: {len(train_data)}')\n",
    "print(f'Num Valid: {len(valid_data)}')\n",
    "print(f'Num Test: {len(test_data)}')"
   ]
  },
  {
   "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": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "e8uNrjdtn4A8",
    "outputId": "7eb14563-3cdc-4386-8a0c-6066161faeb1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 20002\n",
      "Number of classes: 2\n"
     ]
    }
   ],
   "source": [
    "TEXT.build_vocab(train_data, max_size=VOCABULARY_SIZE)\n",
    "LABEL.build_vocab(train_data)\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": "JpEMNInXtZsb"
   },
   "source": [
    "The TEXT.vocab dictionary will contain the word counts and indices. The reason why the number of words is VOCABULARY_SIZE + 2 is that it contains to special tokens for padding and unknown words: `<unk>` and `<pad>`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eIQ_zfKLwjKm"
   },
   "source": [
    "Make dataset iterators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i7JiHR1stHNF"
   },
   "outputs": [],
   "source": [
    "train_loader, valid_loader, test_loader = data.BucketIterator.splits(\n",
    "    (train_data, valid_data, test_data), \n",
    "    batch_size=BATCH_SIZE,\n",
    "    sort_within_batch=True, # necessary for packed_padded_sequence\n",
    "    device=DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R0pT_dMRvicQ"
   },
   "source": [
    "Testing the iterators (note that the number of rows depends on the longest document in the respective batch):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "y8SP_FccutT0",
    "outputId": "13c0ea99-41fe-416c-e809-c950c2f0737f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train\n",
      "Text matrix size: torch.Size([132, 128])\n",
      "Target vector size: torch.Size([128])\n",
      "\n",
      "Valid:\n",
      "Text matrix size: torch.Size([61, 128])\n",
      "Target vector size: torch.Size([128])\n",
      "\n",
      "Test:\n",
      "Text matrix size: torch.Size([42, 128])\n",
      "Target vector size: torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "print('Train')\n",
    "for batch in train_loader:\n",
    "    print(f'Text matrix size: {batch.text[0].size()}')\n",
    "    print(f'Target vector size: {batch.label.size()}')\n",
    "    break\n",
    "    \n",
    "print('\\nValid:')\n",
    "for batch in valid_loader:\n",
    "    print(f'Text matrix size: {batch.text[0].size()}')\n",
    "    print(f'Target vector size: {batch.label.size()}')\n",
    "    break\n",
    "    \n",
    "print('\\nTest:')\n",
    "for batch in test_loader:\n",
    "    print(f'Text matrix size: {batch.text[0].size()}')\n",
    "    print(f'Target vector size: {batch.label.size()}')\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G_grdW3pxCzz"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nQIUm5EjxFNa"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "class RNN(nn.Module):\n",
    "    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.embedding = nn.Embedding(input_dim, embedding_dim)\n",
    "        self.rnn = nn.GRU(embedding_dim, hidden_dim)\n",
    "        self.fc = nn.Linear(hidden_dim, output_dim)\n",
    "        \n",
    "    def forward(self, text, text_length):\n",
    "\n",
    "        #[sentence len, batch size] => [sentence len, batch size, embedding size]\n",
    "        embedded = self.embedding(text)\n",
    "        \n",
    "        packed = torch.nn.utils.rnn.pack_padded_sequence(embedded, text_length)\n",
    "        \n",
    "        #[sentence len, batch size, embedding size] => \n",
    "        #  output: [sentence len, batch size, hidden size]\n",
    "        #  hidden: [1, batch size, hidden size]\n",
    "        packed_output, hidden = self.rnn(packed)\n",
    "        \n",
    "        return self.fc(hidden.squeeze(0)).view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ik3NF3faxFmZ"
   },
   "outputs": [],
   "source": [
    "INPUT_DIM = len(TEXT.vocab)\n",
    "\n",
    "torch.manual_seed(RANDOM_SEED)\n",
    "model = RNN(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM)\n",
    "model = model.to(DEVICE)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lv9Ny9di6VcI"
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T5t1Afn4xO11"
   },
   "outputs": [],
   "source": [
    "def compute_binary_accuracy(model, data_loader, device):\n",
    "    model.eval()\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, batch_data in enumerate(data_loader):\n",
    "            text, text_lengths = batch_data.text\n",
    "            logits = model(text, text_lengths)\n",
    "            predicted_labels = (torch.sigmoid(logits) > 0.5).long()\n",
    "            num_examples += batch_data.label.size(0)\n",
    "            correct_pred += (predicted_labels == batch_data.label.long()).sum()\n",
    "        return correct_pred.float()/num_examples * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1836
    },
    "colab_type": "code",
    "id": "EABZM8Vo0ilB",
    "outputId": "929868ae-bad3-43c0-a37e-d18cfb6fa9af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/015 | Batch 000/157 | Cost: 0.6869\n",
      "Epoch: 001/015 | Batch 050/157 | Cost: 0.6909\n",
      "Epoch: 001/015 | Batch 100/157 | Cost: 0.6929\n",
      "Epoch: 001/015 | Batch 150/157 | Cost: 0.6667\n",
      "training accuracy: 58.70%\n",
      "valid accuracy: 57.30%\n",
      "Time elapsed: 0.18 min\n",
      "Epoch: 002/015 | Batch 000/157 | Cost: 0.6831\n",
      "Epoch: 002/015 | Batch 050/157 | Cost: 0.6460\n",
      "Epoch: 002/015 | Batch 100/157 | Cost: 0.6443\n",
      "Epoch: 002/015 | Batch 150/157 | Cost: 0.6239\n",
      "training accuracy: 68.70%\n",
      "valid accuracy: 67.54%\n",
      "Time elapsed: 0.36 min\n",
      "Epoch: 003/015 | Batch 000/157 | Cost: 0.4887\n",
      "Epoch: 003/015 | Batch 050/157 | Cost: 0.5954\n",
      "Epoch: 003/015 | Batch 100/157 | Cost: 0.6105\n",
      "Epoch: 003/015 | Batch 150/157 | Cost: 0.5285\n",
      "training accuracy: 75.35%\n",
      "valid accuracy: 73.62%\n",
      "Time elapsed: 0.55 min\n",
      "Epoch: 004/015 | Batch 000/157 | Cost: 0.4711\n",
      "Epoch: 004/015 | Batch 050/157 | Cost: 0.5610\n",
      "Epoch: 004/015 | Batch 100/157 | Cost: 0.4648\n",
      "Epoch: 004/015 | Batch 150/157 | Cost: 0.4983\n",
      "training accuracy: 79.68%\n",
      "valid accuracy: 77.00%\n",
      "Time elapsed: 0.73 min\n",
      "Epoch: 005/015 | Batch 000/157 | Cost: 0.4718\n",
      "Epoch: 005/015 | Batch 050/157 | Cost: 0.4375\n",
      "Epoch: 005/015 | Batch 100/157 | Cost: 0.4393\n",
      "Epoch: 005/015 | Batch 150/157 | Cost: 0.4138\n",
      "training accuracy: 81.71%\n",
      "valid accuracy: 78.18%\n",
      "Time elapsed: 0.92 min\n",
      "Epoch: 006/015 | Batch 000/157 | Cost: 0.3452\n",
      "Epoch: 006/015 | Batch 050/157 | Cost: 0.3552\n",
      "Epoch: 006/015 | Batch 100/157 | Cost: 0.4116\n",
      "Epoch: 006/015 | Batch 150/157 | Cost: 0.4030\n",
      "training accuracy: 82.78%\n",
      "valid accuracy: 79.52%\n",
      "Time elapsed: 1.11 min\n",
      "Epoch: 007/015 | Batch 000/157 | Cost: 0.3604\n",
      "Epoch: 007/015 | Batch 050/157 | Cost: 0.3680\n",
      "Epoch: 007/015 | Batch 100/157 | Cost: 0.3132\n",
      "Epoch: 007/015 | Batch 150/157 | Cost: 0.3442\n",
      "training accuracy: 85.72%\n",
      "valid accuracy: 81.90%\n",
      "Time elapsed: 1.30 min\n",
      "Epoch: 008/015 | Batch 000/157 | Cost: 0.3696\n",
      "Epoch: 008/015 | Batch 050/157 | Cost: 0.2850\n",
      "Epoch: 008/015 | Batch 100/157 | Cost: 0.3538\n",
      "Epoch: 008/015 | Batch 150/157 | Cost: 0.4393\n",
      "training accuracy: 86.21%\n",
      "valid accuracy: 81.56%\n",
      "Time elapsed: 1.48 min\n",
      "Epoch: 009/015 | Batch 000/157 | Cost: 0.3638\n",
      "Epoch: 009/015 | Batch 050/157 | Cost: 0.2887\n",
      "Epoch: 009/015 | Batch 100/157 | Cost: 0.3294\n",
      "Epoch: 009/015 | Batch 150/157 | Cost: 0.2515\n",
      "training accuracy: 86.36%\n",
      "valid accuracy: 82.18%\n",
      "Time elapsed: 1.67 min\n",
      "Epoch: 010/015 | Batch 000/157 | Cost: 0.2781\n",
      "Epoch: 010/015 | Batch 050/157 | Cost: 0.3547\n",
      "Epoch: 010/015 | Batch 100/157 | Cost: 0.2762\n",
      "Epoch: 010/015 | Batch 150/157 | Cost: 0.3104\n",
      "training accuracy: 87.39%\n",
      "valid accuracy: 82.92%\n",
      "Time elapsed: 1.86 min\n",
      "Epoch: 011/015 | Batch 000/157 | Cost: 0.3024\n",
      "Epoch: 011/015 | Batch 050/157 | Cost: 0.2901\n",
      "Epoch: 011/015 | Batch 100/157 | Cost: 0.1955\n",
      "Epoch: 011/015 | Batch 150/157 | Cost: 0.2581\n",
      "training accuracy: 89.20%\n",
      "valid accuracy: 83.66%\n",
      "Time elapsed: 2.05 min\n",
      "Epoch: 012/015 | Batch 000/157 | Cost: 0.1964\n",
      "Epoch: 012/015 | Batch 050/157 | Cost: 0.3578\n",
      "Epoch: 012/015 | Batch 100/157 | Cost: 0.2177\n",
      "Epoch: 012/015 | Batch 150/157 | Cost: 0.3732\n",
      "training accuracy: 88.12%\n",
      "valid accuracy: 82.82%\n",
      "Time elapsed: 2.24 min\n",
      "Epoch: 013/015 | Batch 000/157 | Cost: 0.2964\n",
      "Epoch: 013/015 | Batch 050/157 | Cost: 0.2757\n",
      "Epoch: 013/015 | Batch 100/157 | Cost: 0.4130\n",
      "Epoch: 013/015 | Batch 150/157 | Cost: 0.2817\n",
      "training accuracy: 90.71%\n",
      "valid accuracy: 84.54%\n",
      "Time elapsed: 2.43 min\n",
      "Epoch: 014/015 | Batch 000/157 | Cost: 0.2700\n",
      "Epoch: 014/015 | Batch 050/157 | Cost: 0.2832\n",
      "Epoch: 014/015 | Batch 100/157 | Cost: 0.3164\n",
      "Epoch: 014/015 | Batch 150/157 | Cost: 0.2610\n",
      "training accuracy: 90.95%\n",
      "valid accuracy: 84.68%\n",
      "Time elapsed: 2.61 min\n",
      "Epoch: 015/015 | Batch 000/157 | Cost: 0.2593\n",
      "Epoch: 015/015 | Batch 050/157 | Cost: 0.2185\n",
      "Epoch: 015/015 | Batch 100/157 | Cost: 0.3066\n",
      "Epoch: 015/015 | Batch 150/157 | Cost: 0.3229\n",
      "training accuracy: 90.88%\n",
      "valid accuracy: 84.88%\n",
      "Time elapsed: 2.79 min\n",
      "Total Training Time: 2.79 min\n",
      "Test accuracy: 85.09%\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    model.train()\n",
    "    for batch_idx, batch_data in enumerate(train_loader):\n",
    "        \n",
    "        text, text_lengths = batch_data.text\n",
    "        \n",
    "        ### FORWARD AND BACK PROP\n",
    "        logits = model(text, text_lengths)\n",
    "        cost = F.binary_cross_entropy_with_logits(logits, batch_data.label)\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        cost.backward()\n",
    "        \n",
    "        ### UPDATE MODEL PARAMETERS\n",
    "        optimizer.step()\n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 50:\n",
    "            print (f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | '\n",
    "                   f'Batch {batch_idx:03d}/{len(train_loader):03d} | '\n",
    "                   f'Cost: {cost:.4f}')\n",
    "\n",
    "    with torch.set_grad_enabled(False):\n",
    "        print(f'training accuracy: '\n",
    "              f'{compute_binary_accuracy(model, train_loader, DEVICE):.2f}%'\n",
    "              f'\\nvalid accuracy: '\n",
    "              f'{compute_binary_accuracy(model, valid_loader, DEVICE):.2f}%')\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_binary_accuracy(model, test_loader, DEVICE):.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jt55pscgFdKZ"
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "nlp = spacy.load('en')\n",
    "\n",
    "def predict_sentiment(model, sentence):\n",
    "    # based on:\n",
    "    # https://github.com/bentrevett/pytorch-sentiment-analysis/blob/\n",
    "    # master/2%20-%20Upgraded%20Sentiment%20Analysis.ipynb\n",
    "    model.eval()\n",
    "    tokenized = [tok.text for tok in nlp.tokenizer(sentence)]\n",
    "    indexed = [TEXT.vocab.stoi[t] for t in tokenized]\n",
    "    length = [len(indexed)]\n",
    "    tensor = torch.LongTensor(indexed).to(DEVICE)\n",
    "    tensor = tensor.unsqueeze(1)\n",
    "    length_tensor = torch.LongTensor(length)\n",
    "    prediction = torch.sigmoid(model(tensor, length_tensor))\n",
    "    return prediction.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "O4__q0coFJyw",
    "outputId": "602fe8db-6b8e-4286-b5d5-f25381afaccd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability positive:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8322937488555908"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Probability positive:')\n",
    "predict_sentiment(model, \"I really love this movie. This movie is so great!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7lRusB3dF80X"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "name": "rnn_gru_packed_imdb.ipynb",
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