{
 "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 -- Multilayer bidirectional RNN with LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sc6xejhY-NzZ"
   },
   "source": [
    "Demo of a bidirectional RNN for sentiment classification (here: a binary classification problem with two labels, positive and negative) using LSTM (Long Short Term Memory) 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",
    "BIDIRECTIONAL = True\n",
    "\n",
    "EMBEDDING_DIM = 128\n",
    "NUM_LAYERS = 2\n",
    "HIDDEN_DIM = 128\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": 68
    },
    "colab_type": "code",
    "id": "WZ_4jiHVnMxN",
    "outputId": "003776d6-ac38-4de6-f8e0-ee48a696bd27"
   },
   "outputs": [
    {
     "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": "155fec65-7cee-4c1e-c928-da415a962be1"
   },
   "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": "c7dfa012-d06f-4702-9caa-7cde4eee1ebd"
   },
   "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.LSTM(embedding_dim,\n",
    "                           hidden_dim,\n",
    "                           num_layers=NUM_LAYERS,\n",
    "                           bidirectional=BIDIRECTIONAL)\n",
    "        self.fc = nn.Linear(hidden_dim*2, 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",
    "        packed_output, (hidden, cell) = self.rnn(packed)\n",
    "        \n",
    "        # combine both directions\n",
    "        combined = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)\n",
    "        \n",
    "        return self.fc(combined.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": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1836
    },
    "colab_type": "code",
    "id": "EABZM8Vo0ilB",
    "outputId": "27a806c8-60ed-4d90-8a3a-fb2c4cd68bf6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/015 | Batch 000/157 | Cost: 0.6920\n",
      "Epoch: 001/015 | Batch 050/157 | Cost: 0.6899\n",
      "Epoch: 001/015 | Batch 100/157 | Cost: 0.6789\n",
      "Epoch: 001/015 | Batch 150/157 | Cost: 0.6822\n",
      "training accuracy: 59.57%\n",
      "valid accuracy: 57.96%\n",
      "Time elapsed: 0.33 min\n",
      "Epoch: 002/015 | Batch 000/157 | Cost: 0.6753\n",
      "Epoch: 002/015 | Batch 050/157 | Cost: 0.6222\n",
      "Epoch: 002/015 | Batch 100/157 | Cost: 0.6967\n",
      "Epoch: 002/015 | Batch 150/157 | Cost: 0.5783\n",
      "training accuracy: 70.41%\n",
      "valid accuracy: 68.04%\n",
      "Time elapsed: 0.67 min\n",
      "Epoch: 003/015 | Batch 000/157 | Cost: 0.5828\n",
      "Epoch: 003/015 | Batch 050/157 | Cost: 0.5243\n",
      "Epoch: 003/015 | Batch 100/157 | Cost: 0.4915\n",
      "Epoch: 003/015 | Batch 150/157 | Cost: 0.5100\n",
      "training accuracy: 76.81%\n",
      "valid accuracy: 74.58%\n",
      "Time elapsed: 1.00 min\n",
      "Epoch: 004/015 | Batch 000/157 | Cost: 0.4957\n",
      "Epoch: 004/015 | Batch 050/157 | Cost: 0.4526\n",
      "Epoch: 004/015 | Batch 100/157 | Cost: 0.3544\n",
      "Epoch: 004/015 | Batch 150/157 | Cost: 0.4816\n",
      "training accuracy: 79.42%\n",
      "valid accuracy: 77.06%\n",
      "Time elapsed: 1.34 min\n",
      "Epoch: 005/015 | Batch 000/157 | Cost: 0.4932\n",
      "Epoch: 005/015 | Batch 050/157 | Cost: 0.3864\n",
      "Epoch: 005/015 | Batch 100/157 | Cost: 0.3410\n",
      "Epoch: 005/015 | Batch 150/157 | Cost: 0.4023\n",
      "training accuracy: 83.50%\n",
      "valid accuracy: 80.04%\n",
      "Time elapsed: 1.68 min\n",
      "Epoch: 006/015 | Batch 000/157 | Cost: 0.4139\n",
      "Epoch: 006/015 | Batch 050/157 | Cost: 0.3480\n",
      "Epoch: 006/015 | Batch 100/157 | Cost: 0.3831\n",
      "Epoch: 006/015 | Batch 150/157 | Cost: 0.3973\n",
      "training accuracy: 85.54%\n",
      "valid accuracy: 81.82%\n",
      "Time elapsed: 2.02 min\n",
      "Epoch: 007/015 | Batch 000/157 | Cost: 0.3975\n",
      "Epoch: 007/015 | Batch 050/157 | Cost: 0.3051\n",
      "Epoch: 007/015 | Batch 100/157 | Cost: 0.3075\n",
      "Epoch: 007/015 | Batch 150/157 | Cost: 0.3503\n",
      "training accuracy: 85.21%\n",
      "valid accuracy: 80.54%\n",
      "Time elapsed: 2.35 min\n",
      "Epoch: 008/015 | Batch 000/157 | Cost: 0.3074\n",
      "Epoch: 008/015 | Batch 050/157 | Cost: 0.2903\n",
      "Epoch: 008/015 | Batch 100/157 | Cost: 0.3141\n",
      "Epoch: 008/015 | Batch 150/157 | Cost: 0.2595\n",
      "training accuracy: 87.69%\n",
      "valid accuracy: 82.68%\n",
      "Time elapsed: 2.69 min\n",
      "Epoch: 009/015 | Batch 000/157 | Cost: 0.2799\n",
      "Epoch: 009/015 | Batch 050/157 | Cost: 0.2448\n",
      "Epoch: 009/015 | Batch 100/157 | Cost: 0.2151\n",
      "Epoch: 009/015 | Batch 150/157 | Cost: 0.2847\n",
      "training accuracy: 88.93%\n",
      "valid accuracy: 83.70%\n",
      "Time elapsed: 3.03 min\n",
      "Epoch: 010/015 | Batch 000/157 | Cost: 0.2497\n",
      "Epoch: 010/015 | Batch 050/157 | Cost: 0.2540\n",
      "Epoch: 010/015 | Batch 100/157 | Cost: 0.3835\n",
      "Epoch: 010/015 | Batch 150/157 | Cost: 0.2845\n",
      "training accuracy: 90.00%\n",
      "valid accuracy: 84.10%\n",
      "Time elapsed: 3.38 min\n",
      "Epoch: 011/015 | Batch 000/157 | Cost: 0.2449\n",
      "Epoch: 011/015 | Batch 050/157 | Cost: 0.1758\n",
      "Epoch: 011/015 | Batch 100/157 | Cost: 0.1718\n",
      "Epoch: 011/015 | Batch 150/157 | Cost: 0.2826\n",
      "training accuracy: 91.14%\n",
      "valid accuracy: 85.00%\n",
      "Time elapsed: 3.71 min\n",
      "Epoch: 012/015 | Batch 000/157 | Cost: 0.1856\n",
      "Epoch: 012/015 | Batch 050/157 | Cost: 0.2359\n",
      "Epoch: 012/015 | Batch 100/157 | Cost: 0.2082\n",
      "Epoch: 012/015 | Batch 150/157 | Cost: 0.2608\n",
      "training accuracy: 91.82%\n",
      "valid accuracy: 85.04%\n",
      "Time elapsed: 4.05 min\n",
      "Epoch: 013/015 | Batch 000/157 | Cost: 0.2708\n",
      "Epoch: 013/015 | Batch 050/157 | Cost: 0.2684\n",
      "Epoch: 013/015 | Batch 100/157 | Cost: 0.1688\n",
      "Epoch: 013/015 | Batch 150/157 | Cost: 0.2290\n",
      "training accuracy: 92.83%\n",
      "valid accuracy: 85.64%\n",
      "Time elapsed: 4.39 min\n",
      "Epoch: 014/015 | Batch 000/157 | Cost: 0.2259\n",
      "Epoch: 014/015 | Batch 050/157 | Cost: 0.1845\n",
      "Epoch: 014/015 | Batch 100/157 | Cost: 0.1361\n",
      "Epoch: 014/015 | Batch 150/157 | Cost: 0.2182\n",
      "training accuracy: 93.69%\n",
      "valid accuracy: 85.96%\n",
      "Time elapsed: 4.73 min\n",
      "Epoch: 015/015 | Batch 000/157 | Cost: 0.2125\n",
      "Epoch: 015/015 | Batch 050/157 | Cost: 0.1962\n",
      "Epoch: 015/015 | Batch 100/157 | Cost: 0.2078\n",
      "Epoch: 015/015 | Batch 150/157 | Cost: 0.5364\n",
      "training accuracy: 91.02%\n",
      "valid accuracy: 83.28%\n",
      "Time elapsed: 5.06 min\n",
      "Total Training Time: 5.06 min\n",
      "Test accuracy: 83.21%\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": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "O4__q0coFJyw",
    "outputId": "647a03b4-ff7e-4892-b279-f33fee690f81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability positive:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9388696551322937"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Probability positive:')\n",
    "predict_sentiment(model, \"I really love this movie. This movie is so great!\")"
   ]
  },
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   },
   "outputs": [],
   "source": []
  }
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