translation-attention.ipynb

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   "name": "python",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import unicode_literals, print_function, division\n",
    "from io import open\n",
    "import unicodedata\n",
    "import string\n",
    "import re\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SOS_token = 0\n",
    "EOS_token = 1\n",
    "\n",
    "\n",
    "class Lang:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.word2index = {}\n",
    "        self.word2count = {}\n",
    "        self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
    "        self.n_words = 2  # Count SOS and EOS\n",
    "\n",
    "    def addSentence(self, sentence):\n",
    "        for word in sentence.split(' '):\n",
    "            self.addWord(word)\n",
    "\n",
    "    def addWord(self, word):\n",
    "        if word not in self.word2index:\n",
    "            self.word2index[word] = self.n_words\n",
    "            self.word2count[word] = 1\n",
    "            self.index2word[self.n_words] = word\n",
    "            self.n_words += 1\n",
    "        else:\n",
    "            self.word2count[word] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn a Unicode string to plain ASCII, thanks to\n",
    "# https://stackoverflow.com/a/518232/2809427\n",
    "def unicodeToAscii(s):\n",
    "    return ''.join(\n",
    "        c for c in unicodedata.normalize('NFD', s)\n",
    "        if unicodedata.category(c) != 'Mn'\n",
    "    )\n",
    "\n",
    "# Lowercase, trim, and remove non-letter characters\n",
    "\n",
    "\n",
    "def normalizeString(s):\n",
    "    s = unicodeToAscii(s.lower().strip())\n",
    "    s = re.sub(r\"([.!?])\", r\" \\1\", s)\n",
    "    s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readLangs(lang1, lang2, reverse=False):\n",
    "    print(\"Reading lines...\")\n",
    "\n",
    "    # Read the file and split into lines\n",
    "    lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\\\n",
    "        read().strip().split('\\n')\n",
    "\n",
    "    # Split every line into pairs and normalize\n",
    "    pairs = [[normalizeString(s) for s in l.split('\\t')] for l in lines]\n",
    "\n",
    "    # Reverse pairs, make Lang instances\n",
    "    if reverse:\n",
    "        pairs = [list(reversed(p)) for p in pairs]\n",
    "        input_lang = Lang(lang2)\n",
    "        output_lang = Lang(lang1)\n",
    "    else:\n",
    "        input_lang = Lang(lang1)\n",
    "        output_lang = Lang(lang2)\n",
    "\n",
    "    return input_lang, output_lang, pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LENGTH = 10\n",
    "\n",
    "eng_prefixes = (\n",
    "    \"i am \", \"i m \",\n",
    "    \"he is\", \"he s \",\n",
    "    \"she is\", \"she s \",\n",
    "    \"you are\", \"you re \",\n",
    "    \"we are\", \"we re \",\n",
    "    \"they are\", \"they re \"\n",
    ")\n",
    "\n",
    "\n",
    "def filterPair(p):\n",
    "    return len(p[0].split(' ')) < MAX_LENGTH and \\\n",
    "        len(p[1].split(' ')) < MAX_LENGTH and \\\n",
    "        p[1].startswith(eng_prefixes)\n",
    "\n",
    "\n",
    "def filterPairs(pairs):\n",
    "    return [pair for pair in pairs if filterPair(pair)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Reading lines...\nRead 135842 sentence pairs\nTrimmed to 10599 sentence pairs\nCounting words...\nCounted words:\nfra 4345\neng 2803\n['c est un enfant pourri gate .', 'he is a spoiled child .']\n"
    }
   ],
   "source": [
    "def prepareData(lang1, lang2, reverse=False):\n",
    "    input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)\n",
    "    print(\"Read %s sentence pairs\" % len(pairs))\n",
    "    pairs = filterPairs(pairs)\n",
    "    print(\"Trimmed to %s sentence pairs\" % len(pairs))\n",
    "    print(\"Counting words...\")\n",
    "    for pair in pairs:\n",
    "        input_lang.addSentence(pair[0])\n",
    "        output_lang.addSentence(pair[1])\n",
    "    print(\"Counted words:\")\n",
    "    print(input_lang.name, input_lang.n_words)\n",
    "    print(output_lang.name, output_lang.n_words)\n",
    "    return input_lang, output_lang, pairs\n",
    "\n",
    "\n",
    "input_lang, output_lang, pairs = prepareData('eng', 'fra', True)\n",
    "print(random.choice(pairs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderRNN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size):\n",
    "        super(EncoderRNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        self.embedding = nn.Embedding(input_size, hidden_size)\n",
    "        self.gru = nn.GRU(hidden_size, hidden_size)\n",
    "\n",
    "    def forward(self, input, hidden):\n",
    "        embedded = self.embedding(input).view(1, 1, -1)\n",
    "        output = embedded\n",
    "        output, hidden = self.gru(output, hidden)\n",
    "        return output, hidden\n",
    "\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, 1, self.hidden_size, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AttnDecoderRNN(nn.Module):\n",
    "    def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
    "        super(AttnDecoderRNN, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.output_size = output_size\n",
    "        self.dropout_p = dropout_p\n",
    "        self.max_length = max_length\n",
    "\n",
    "        self.embedding = nn.Embedding(self.output_size, self.hidden_size)\n",
    "        self.attn = nn.Linear(self.hidden_size * 2, self.max_length)\n",
    "        self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)\n",
    "        self.dropout = nn.Dropout(self.dropout_p)\n",
    "        self.gru = nn.GRU(self.hidden_size, self.hidden_size)\n",
    "        self.out = nn.Linear(self.hidden_size, self.output_size)\n",
    "\n",
    "    def forward(self, input, hidden, encoder_outputs):\n",
    "        embedded = self.embedding(input).view(1, 1, -1)\n",
    "        embedded = self.dropout(embedded)\n",
    "\n",
    "        attn_weights = F.softmax(\n",
    "            self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
    "        attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
    "                                 encoder_outputs.unsqueeze(0))\n",
    "\n",
    "        output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
    "        output = self.attn_combine(output).unsqueeze(0)\n",
    "\n",
    "        output = F.relu(output)\n",
    "        output, hidden = self.gru(output, hidden)\n",
    "\n",
    "        output = F.log_softmax(self.out(output[0]), dim=1)\n",
    "        return output, hidden, attn_weights\n",
    "\n",
    "    def initHidden(self):\n",
    "        return torch.zeros(1, 1, self.hidden_size, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def indexesFromSentence(lang, sentence):\n",
    "    return [lang.word2index[word] for word in sentence.split(' ')]\n",
    "\n",
    "\n",
    "def tensorFromSentence(lang, sentence):\n",
    "    indexes = indexesFromSentence(lang, sentence)\n",
    "    indexes.append(EOS_token)\n",
    "    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n",
    "\n",
    "\n",
    "def tensorsFromPair(pair):\n",
    "    input_tensor = tensorFromSentence(input_lang, pair[0])\n",
    "    target_tensor = tensorFromSentence(output_lang, pair[1])\n",
    "    return (input_tensor, target_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "teacher_forcing_ratio = 0.5\n",
    "\n",
    "\n",
    "def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):\n",
    "    encoder_hidden = encoder.initHidden()\n",
    "\n",
    "    encoder_optimizer.zero_grad()\n",
    "    decoder_optimizer.zero_grad()\n",
    "\n",
    "    input_length = input_tensor.size(0)\n",
    "    target_length = target_tensor.size(0)\n",
    "\n",
    "    encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
    "\n",
    "    loss = 0\n",
    "\n",
    "    for ei in range(input_length):\n",
    "        encoder_output, encoder_hidden = encoder(\n",
    "            input_tensor[ei], encoder_hidden)\n",
    "        encoder_outputs[ei] = encoder_output[0, 0]\n",
    "\n",
    "    decoder_input = torch.tensor([[SOS_token]], device=device)\n",
    "\n",
    "    decoder_hidden = encoder_hidden\n",
    "\n",
    "    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
    "\n",
    "    if use_teacher_forcing:\n",
    "        # Teacher forcing: Feed the target as the next input\n",
    "        for di in range(target_length):\n",
    "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
    "                decoder_input, decoder_hidden, encoder_outputs)\n",
    "            loss += criterion(decoder_output, target_tensor[di])\n",
    "            decoder_input = target_tensor[di]  # Teacher forcing\n",
    "\n",
    "    else:\n",
    "        # Without teacher forcing: use its own predictions as the next input\n",
    "        for di in range(target_length):\n",
    "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
    "                decoder_input, decoder_hidden, encoder_outputs)\n",
    "            topv, topi = decoder_output.topk(1)\n",
    "            decoder_input = topi.squeeze().detach()  # detach from history as input\n",
    "\n",
    "            loss += criterion(decoder_output, target_tensor[di])\n",
    "            if decoder_input.item() == EOS_token:\n",
    "                break\n",
    "\n",
    "    loss.backward()\n",
    "\n",
    "    encoder_optimizer.step()\n",
    "    decoder_optimizer.step()\n",
    "\n",
    "    return loss.item() / target_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import math\n",
    "\n",
    "\n",
    "def asMinutes(s):\n",
    "    m = math.floor(s / 60)\n",
    "    s -= m * 60\n",
    "    return '%dm %ds' % (m, s)\n",
    "\n",
    "\n",
    "def timeSince(since, percent):\n",
    "    now = time.time()\n",
    "    s = now - since\n",
    "    es = s / (percent)\n",
    "    rs = es - s\n",
    "    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):\n",
    "    start = time.time()\n",
    "    plot_losses = []\n",
    "    print_loss_total = 0  # Reset every print_every\n",
    "    plot_loss_total = 0  # Reset every plot_every\n",
    "\n",
    "    encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)\n",
    "    decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)\n",
    "    training_pairs = [tensorsFromPair(random.choice(pairs))\n",
    "                      for i in range(n_iters)]\n",
    "    criterion = nn.NLLLoss()\n",
    "\n",
    "    for iter in range(1, n_iters + 1):\n",
    "        training_pair = training_pairs[iter - 1]\n",
    "        input_tensor = training_pair[0]\n",
    "        target_tensor = training_pair[1]\n",
    "\n",
    "        loss = train(input_tensor, target_tensor, encoder,\n",
    "                     decoder, encoder_optimizer, decoder_optimizer, criterion)\n",
    "        print_loss_total += loss\n",
    "        plot_loss_total += loss\n",
    "\n",
    "        if iter % print_every == 0:\n",
    "            print_loss_avg = print_loss_total / print_every\n",
    "            print_loss_total = 0\n",
    "            print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),\n",
    "                                         iter, iter / n_iters * 100, print_loss_avg))\n",
    "\n",
    "        if iter % plot_every == 0:\n",
    "            plot_loss_avg = plot_loss_total / plot_every\n",
    "            plot_losses.append(plot_loss_avg)\n",
    "            plot_loss_total = 0\n",
    "\n",
    "    showPlot(plot_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.switch_backend('agg')\n",
    "import matplotlib.ticker as ticker\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def showPlot(points):\n",
    "    plt.figure()\n",
    "    fig, ax = plt.subplots()\n",
    "    # this locator puts ticks at regular intervals\n",
    "    loc = ticker.MultipleLocator(base=0.2)\n",
    "    ax.yaxis.set_major_locator(loc)\n",
    "    plt.plot(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
    "    with torch.no_grad():\n",
    "        input_tensor = tensorFromSentence(input_lang, sentence)\n",
    "        input_length = input_tensor.size()[0]\n",
    "        encoder_hidden = encoder.initHidden()\n",
    "\n",
    "        encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
    "\n",
    "        for ei in range(input_length):\n",
    "            encoder_output, encoder_hidden = encoder(input_tensor[ei],\n",
    "                                                     encoder_hidden)\n",
    "            encoder_outputs[ei] += encoder_output[0, 0]\n",
    "\n",
    "        decoder_input = torch.tensor([[SOS_token]], device=device)  # SOS\n",
    "\n",
    "        decoder_hidden = encoder_hidden\n",
    "\n",
    "        decoded_words = []\n",
    "        decoder_attentions = torch.zeros(max_length, max_length)\n",
    "\n",
    "        for di in range(max_length):\n",
    "            decoder_output, decoder_hidden, decoder_attention = decoder(\n",
    "                decoder_input, decoder_hidden, encoder_outputs)\n",
    "            decoder_attentions[di] = decoder_attention.data\n",
    "            topv, topi = decoder_output.data.topk(1)\n",
    "            if topi.item() == EOS_token:\n",
    "                decoded_words.append('<EOS>')\n",
    "                break\n",
    "            else:\n",
    "                decoded_words.append(output_lang.index2word[topi.item()])\n",
    "\n",
    "            decoder_input = topi.squeeze().detach()\n",
    "\n",
    "        return decoded_words, decoder_attentions[:di + 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluateRandomly(encoder, decoder, n=10):\n",
    "    for i in range(n):\n",
    "        pair = random.choice(pairs)\n",
    "        print('>', pair[0])\n",
    "        print('=', pair[1])\n",
    "        output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
    "        output_sentence = ' '.join(output_words)\n",
    "        print('<', output_sentence)\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "4m 19s (- 60m 29s) (5000 6%) 2.8346\n8m 35s (- 55m 52s) (10000 13%) 2.2847\n"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-7ea4b06f8a39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mattn_decoder1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAttnDecoderRNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_lang\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropout_p\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrainIters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoder1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_decoder1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m75000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_every\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-12-29eaa6fdc28a>\u001b[0m in \u001b[0;36mtrainIters\u001b[0;34m(encoder, decoder, n_iters, print_every, plot_every, learning_rate)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         loss = train(input_tensor, target_tensor, encoder,\n\u001b[0;32m---> 19\u001b[0;31m                      decoder, encoder_optimizer, decoder_optimizer, criterion)\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mprint_loss_total\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mplot_loss_total\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-77785d753587>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length)\u001b[0m\n\u001b[1;32m     46\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0mencoder_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/ENTER/lib/python3.7/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \"\"\"\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/ENTER/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m     88\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m     89\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m         allow_unreachable=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "hidden_size = 256\n",
    "encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)\n",
    "attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words, dropout_p=0.1).to(device)\n",
    "\n",
    "trainIters(encoder1, attn_decoder1, 75000, print_every=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "> je viens d amerique .\n= i m from america .\n< i m afraid of your . . <EOS>\n\n> tu es tres genereuse .\n= you re very generous .\n< you re very understanding . <EOS>\n\n> je me fais harceler .\n= i m being harassed .\n< i m just about . <EOS>\n\n> vous etes fideles .\n= you re faithful .\n< you re overworked . <EOS>\n\n> je n ai pas faim non plus .\n= i m not hungry either .\n< i m not a doctor . <EOS>\n\n> je suis surpris que vous ignoriez cela .\n= i m surprised you didn t know that .\n< i m glad you re happy . <EOS>\n\n> c est un homme aux multiples talents .\n= he s a man of many talents .\n< he s a man of a man . <EOS>\n\n> tu es entete .\n= you re stubborn .\n< you re overworked . <EOS>\n\n> je suis promu .\n= i m being promoted .\n< i m retired . <EOS>\n\n> je me rejouis que vous en preniez conscience .\n= i m glad you realize that .\n< i m glad you re the . <EOS>\n\n"
    }
   ],
   "source": [
    "evaluateRandomly(encoder1, attn_decoder1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1228dbe48>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
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      "text/plain": "<Figure size 480x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_words, attentions = evaluate(\n",
    "    encoder1, attn_decoder1, \"je suis trop froid .\")\n",
    "plt.matshow(attentions.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "input = elle a cinq ans de moins que moi .\noutput = she s as years than me . <EOS>\n"
    },
    {
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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "input = elle est trop petit .\noutput = she s too young . <EOS>\n"
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": "input = je ne crains pas de mourir .\noutput = i m not afraid of it . <EOS>\n"
    },
    {
     "data": {
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\n",
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      "text/plain": "<Figure size 432x288 with 2 Axes>"
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     "name": "stdout",
     "output_type": "stream",
     "text": "input = c est un jeune directeur plein de talent .\noutput = he s very a very in . <EOS>\n"
    },
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      "text/plain": "<Figure size 432x288 with 2 Axes>"
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "def showAttention(input_sentence, output_words, attentions):\n",
    "    # Set up figure with colorbar\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(attentions.numpy(), cmap='bone')\n",
    "    fig.colorbar(cax)\n",
    "\n",
    "    # Set up axes\n",
    "    ax.set_xticklabels([''] + input_sentence.split(' ') +\n",
    "                       ['<EOS>'], rotation=90)\n",
    "    ax.set_yticklabels([''] + output_words)\n",
    "\n",
    "    # Show label at every tick\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def evaluateAndShowAttention(input_sentence):\n",
    "    output_words, attentions = evaluate(\n",
    "        encoder1, attn_decoder1, input_sentence)\n",
    "    print('input =', input_sentence)\n",
    "    print('output =', ' '.join(output_words))\n",
    "    showAttention(input_sentence, output_words, attentions)\n",
    "\n",
    "\n",
    "evaluateAndShowAttention(\"elle a cinq ans de moins que moi .\")\n",
    "\n",
    "evaluateAndShowAttention(\"elle est trop petit .\")\n",
    "\n",
    "evaluateAndShowAttention(\"je ne crains pas de mourir .\")\n",
    "\n",
    "evaluateAndShowAttention(\"c est un jeune directeur plein de talent .\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}