Machine_translation_Pytorch.ipynb

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   "source": [
    "# Machine translation Pytorch"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import re\n",
    "import imblearn\n",
    "import torch\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from gensim.parsing.porter import PorterStemmer\n",
    "from gensim.utils import tokenize\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import remove_stopwords\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "from torchtext.datasets import IWSLT2017\n",
    "from torchtext.legacy import data\n",
    "from torchtext.vocab import Vocab\n",
    "from torchtext.data.metrics import bleu_score\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torchsummary import summary\n",
    "# from torchtext import data\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "from numpy import interp\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from bokeh.models import ColumnDataSource, LabelSet\n",
    "from bokeh.plotting import figure, show, output_file\n",
    "from collections import Counter\n",
    "from functools import reduce\n",
    "# ! pip install captum bokeh spacy emot parameter-sherpa\n",
    "import sherpa\n",
    "import captum\n",
    "import random\n",
    "import spacy\n",
    "from captum.attr import LayerIntegratedGradients, TokenReferenceBase, visualization, IntegratedGradients, LayerConductance\n",
    "from captum.attr import visualization as viz\n",
    "from captum.attr import configure_interpretable_embedding_layer, remove_interpretable_embedding_layer\n",
    "import emot\n",
    "from bokeh.io import output_notebook\n",
    "output_notebook()\n",
    "# ! python -m spacy download en_core_web_sm\n",
    "# !python -m spacy download de\n",
    "%matplotlib inline"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## Text Preprocessing"
   ],
   "metadata": {
    "collapsed": false
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  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
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      "INFO:root:Finished extracting tar file .pytorch/.data/2017-01-trnmted\\texts/DeEnItNlRo/DeEnItNlRo\\DeEnItNlRo-DeEnItNlRo.tgz.\n"
     ]
    }
   ],
   "source": [
    "spacy_german = spacy.load(\"de\")\n",
    "spacy_english = spacy.load(\"en\")\n",
    "\n",
    "def tokenize_german(text):\n",
    "    return [token.text.lower() for token in spacy_german.tokenizer(text)]\n",
    "\n",
    "def tokenize_english(text):\n",
    "    return [token.text.lower() for token in spacy_english.tokenizer(text)]\n",
    "\n",
    "train_iter, valid_iter, test_iter, = IWSLT2017(root='.pytorch/.data/', language_pair=('en','de'))\n",
    "train_data = list(train_iter)\n",
    "valid_data = list(valid_iter)\n",
    "test_data = list(test_iter)\n",
    "en_counter = Counter()\n",
    "de_counter = Counter()\n",
    "\n",
    "for (en, de) in train_data:\n",
    "    en_counter.update(tokenize_english(en))\n",
    "    de_counter.update(tokenize_german(de))\n",
    "\n",
    "german = Vocab(de_counter, min_freq=10, specials=('<unk>', '<BOS>', '<EOS>', '<PAD>'))\n",
    "english = Vocab(en_counter, min_freq=10, specials=('<unk>', '<BOS>', '<EOS>', '<PAD>'))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique tokens in source (de) vocabs 15927\n",
      "Unique tokens in source (en) vocabs 13190\n"
     ]
    }
   ],
   "source": [
    "print(f'Unique tokens in source (de) vocabs {len(german)}')\n",
    "print(f'Unique tokens in source (en) vocabs {len(english)}')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output of the text_transform: [1, 78, 18, 54, 225, 2]\n"
     ]
    }
   ],
   "source": [
    "english_transform = lambda x: [english['<BOS>']] + [english[token] for token in tokenize_english(x)] + [english['<EOS>']]\n",
    "german_transform = lambda x: [german['<BOS>']] + [german[token] for token in tokenize_german(x)] + [german['<EOS>']]\n",
    "print(\"output of the text_transform:\", english_transform(\"here is an example\"))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Generating Batch iterator"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(tensor([[   1,    1,    1,  ...,    1,    1,    1],\n         [  28,  318,   13,  ...,   11,  183,   42],\n         [  18,   16, 1005,  ..., 1066,    7,   69],\n         ...,\n         [   3,    3,    3,  ...,    3,    3,    3],\n         [   3,    3,    3,  ...,    3,    3,    3],\n         [   3,    3,    3,  ...,    3,    3,    3]]),\n tensor([[    1,     1,     1,  ...,     1,     1,     1],\n         [   24,     0,     0,  ...,     0,     0,     0],\n         [    0, 12667,     0,  ...,     0,     0,    44],\n         ...,\n         [    3,     3,     3,  ...,     3,     3,     3],\n         [    3,     3,     3,  ...,     3,     3,     3],\n         [    3,     3,     3,  ...,     3,     3,     3]]))"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torch.nn.utils.rnn import pad_sequence\n",
    "BATCH_SIZE=32\n",
    "def collate_batch(batch):\n",
    "    de_list, en_list = [], []\n",
    "    for (_en, _de) in batch:\n",
    "        en_list.append(torch.tensor(english_transform(_en)))\n",
    "        de_list.append(torch.tensor(english_transform(_de)))\n",
    "    return pad_sequence(en_list, padding_value=3.0), pad_sequence(de_list, padding_value=3.0)\n",
    "\n",
    "train_dataloader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
    "test_dataloader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
    "valid_dataloader = DataLoader(valid_data, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
    "next(iter(train_dataloader))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model Implementation"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Encoder"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EncoderLSTM(\n",
      "  (dropout): Dropout(p=0.5, inplace=False)\n",
      "  (embedding): Embedding(13190, 300)\n",
      "  (LSTM): LSTM(300, 1024, num_layers=2, dropout=0.5)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class EncoderLSTM(nn.Module):\n",
    "    def __init__(self, input_size, embedding_size, hidden_size, num_layers, p):\n",
    "        super(EncoderLSTM, self).__init__()\n",
    "        self.input_size = input_size # size of input one-hot vector\n",
    "        self.embedding_size = embedding_size\n",
    "        self.hidden_size = hidden_size # output size of embedding NN\n",
    "        self.hidden_size = hidden_size # Dimension of NN's inside lstm cell\n",
    "        num_layers = num_layers # no of stacked lstm\n",
    "        self.dropout = nn.Dropout(p)\n",
    "\n",
    "        self.embedding = nn.Embedding(self.input_size, self.embedding_size) # [input size, embedding dims]\n",
    "        self.LSTM = nn.LSTM(self.embedding_size, hidden_size, num_layers, dropout=p) # [embedding dims, hidden size, num layers]\n",
    "\n",
    "    def forward(self, x):\n",
    "        embd = self.dropout(self.embedding(x)) # x: [Sequence_length, batch_size] emd: [Sequence_length , batch_size , embedding dims]\n",
    "        outputs, (hidden_state, cell_state) = self.LSTM(embd) # output: [Sequence_length , batch_size , hidden_size] (hs, cs): [num_layers, batch_size size, hidden_size]\n",
    "        return hidden_state, cell_state\n",
    "\n",
    "input_size_encoder = len(english)\n",
    "encoder_embedding_size = 300\n",
    "hidden_size = 1024\n",
    "num_layers = 2\n",
    "encoder_dropout = float(0.5)\n",
    "\n",
    "encoder_lstm = EncoderLSTM(input_size_encoder, encoder_embedding_size,\n",
    "                           hidden_size, num_layers, encoder_dropout).to(device)\n",
    "print(encoder_lstm)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Decoder"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecoderLSTM(\n",
      "  (dropout): Dropout(p=0.5, inplace=False)\n",
      "  (embedding): Embedding(15927, 300)\n",
      "  (LSTM): LSTM(300, 1024, num_layers=2, dropout=0.5)\n",
      "  (fc1): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "  (fc2): Linear(in_features=2048, out_features=15927, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class DecoderLSTM(nn.Module):\n",
    "    def __init__(self, input_size, embedding_size, hidden_size, num_layers, p, output_size):\n",
    "        super(DecoderLSTM, self).__init__()\n",
    "\n",
    "        self.input_size = input_size # size of one-hot input vector\n",
    "        self.embedding_size = embedding_size # output size embedding layer\n",
    "        self.hidden_size = hidden_size # dim of NN inside lstm memory\n",
    "        self.num_layers = num_layers # no of stacked lstm\n",
    "        self.output_size = output_size # size of one-hot vector (target language)\n",
    "        self.dropout = nn.Dropout(p)\n",
    "\n",
    "        self.embedding = nn.Embedding(self.input_size, self.embedding_size) # [input size, embedding dims]\n",
    "        self.LSTM = nn.LSTM(self.embedding_size, hidden_size, num_layers, dropout=p) # [embedding dims, hidden size, num layers]\n",
    "\n",
    "        self.fc1 = nn.Linear(self.hidden_size, self.hidden_size*2)\n",
    "        self.fc2 = nn.Linear(self.hidden_size*2, self.output_size)\n",
    "\n",
    "    def forward(self, x, hidden_state, cell_state):\n",
    "        x = x.unsqueeze(0) # [1, batch_size]\n",
    "\n",
    "        embd = self.dropout(self.embedding(x)) # [1, batch_size, embedding dims]\n",
    "        outputs, (hidden_state, cell_state) = self.LSTM(embd, (hidden_state, cell_state)) # [num_layers, batch_size size, hidden_size]\n",
    "        fc = self.fc1(outputs)\n",
    "        pred = self.fc2(self.dropout(fc))\n",
    "\n",
    "        pred = pred.squeeze(0)\n",
    "        return  pred, hidden_state, cell_state\n",
    "\n",
    "\n",
    "input_size_decoder = len(german)\n",
    "decoder_embedding_size = 300\n",
    "hidden_size = 1024\n",
    "num_layers = 2\n",
    "decoder_dropout = float(0.5)\n",
    "output_size = len(german)\n",
    "\n",
    "decoder_lstm = DecoderLSTM(input_size_decoder, decoder_embedding_size,\n",
    "                           hidden_size, num_layers, decoder_dropout, output_size).to(device)\n",
    "print(decoder_lstm)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Module):\n",
    "    def __init__(self, Encoder_LSTM, Decoder_LSTM):\n",
    "        super(Seq2Seq, self).__init__()\n",
    "        self.Encoder_LSTM = Encoder_LSTM\n",
    "        self.Decoder_LSTM = Decoder_LSTM\n",
    "\n",
    "    def forward(self, source, target, tfr=0.5):\n",
    "        batch_size = source.shape[1] # [ seq length, No. of seq]\n",
    "\n",
    "        target_len = target.shape[0] # [ seq length, No. of seq]\n",
    "        target_vocab_size = len(german)\n",
    "\n",
    "        outputs = torch.zeros(target_len, batch_size, target_vocab_size).to(device)\n",
    "        hidden_state_encoder, cell_state_encoder = self.Encoder_LSTM(source)\n",
    "\n",
    "        x = target[0] # trigger <BOS>\n",
    "\n",
    "        for i in range(1, target_len):\n",
    "            output, hidden_state_decoder, cell_state_decoder = self.Decoder_LSTM(x, hidden_state_encoder, cell_state_encoder)\n",
    "            outputs[i] = output\n",
    "            pred = output.argmax(1)\n",
    "            x = target[i] if np.random.rand() < tfr else pred\n",
    "        return outputs"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "writer = SummaryWriter(f\".pytorch/runs/loss_plot\")\n",
    "step = 0\n",
    "\n",
    "model = Seq2Seq(encoder_lstm, decoder_lstm).to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "pad_idx = english.stoi[\"<PAD>\"]\n",
    "criterion = nn.CrossEntropyLoss(ignore_index=pad_idx)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq2Seq(\n",
      "  (Encoder_LSTM): EncoderLSTM(\n",
      "    (dropout): Dropout(p=0.5, inplace=False)\n",
      "    (embedding): Embedding(13190, 300)\n",
      "    (LSTM): LSTM(300, 1024, num_layers=2, dropout=0.5)\n",
      "  )\n",
      "  (Decoder_LSTM): DecoderLSTM(\n",
      "    (dropout): Dropout(p=0.5, inplace=False)\n",
      "    (embedding): Embedding(15927, 300)\n",
      "    (LSTM): LSTM(300, 1024, num_layers=2, dropout=0.5)\n",
      "    (fc1): Linear(in_features=1024, out_features=2048, bias=True)\n",
      "    (fc2): Linear(in_features=2048, out_features=15927, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "def translate_sentenece(model, sentence, english, german, device, max_length=50):\n",
    "    sentence_tensor = torch.LongTensor(english_transform(sentence)).unsqueeze(1).to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        hid, cell = model.Encoder_LSTM(sentence_tensor)\n",
    "\n",
    "    outputs = [german.stoi[\"<BOS>\"]]\n",
    "\n",
    "    for _ in range(max_length):\n",
    "        previous_word = torch.LongTensor([outputs[-1]]).to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            output, hid, cell = model.Decoder_LSTM(previous_word, hid, cell)\n",
    "            pred = output.argmax(1).item()\n",
    "\n",
    "        outputs.append(pred)\n",
    "\n",
    "        if output.argmax(1).item() == german.stoi['<EOS>']:\n",
    "            break\n",
    "    res = [german.itos[i] for i in outputs]\n",
    "    return res[1:]\n",
    "\n",
    "def bleu(data, model, german, english, device):\n",
    "    targets = []\n",
    "    outputs = []\n",
    "\n",
    "    for e in data:\n",
    "        src = e[0]\n",
    "        trg = e[1]\n",
    "        pred = translate_sentenece(model, src, english, german, device)\n",
    "        pred = pred[:-1]\n",
    "        targets.append([trg])\n",
    "        outputs.append(pred)\n",
    "    return  bleu_score(outputs, targets)\n",
    "\n",
    "def checkpoint_and_save(model, best_loss, epoch, optimizer, epoch_loss):\n",
    "    print('saving')\n",
    "    print()\n",
    "    state = {'model': model,'best_loss': best_loss,'epoch': epoch,'rng_state': torch.get_rng_state(), 'optimizer': optimizer.state_dict(),}\n",
    "    torch.save(state, '/content/checkpoint-NMT')\n",
    "    torch.save(model.state_dict(),'checkpoint/eng_ger')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch - 1 / 100\n",
      "Translated example sentence 1: \n",
      " ['.', '.', '.', '<EOS>']\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-14-3c13453ce240>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m     27\u001B[0m         \u001B[0mloss\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mcriterion\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0moutput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtarget\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;31m# calculate the loss for every epoch\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     28\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 29\u001B[1;33m         \u001B[0mloss\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbackward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m     30\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m     31\u001B[0m         \u001B[1;31m# clip the gradient if it exceeds 1\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\program files\\python37\\lib\\site-packages\\torch\\tensor.py\u001B[0m in \u001B[0;36mbackward\u001B[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001B[0m\n\u001B[0;32m    243\u001B[0m                 \u001B[0mcreate_graph\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mcreate_graph\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    244\u001B[0m                 inputs=inputs)\n\u001B[1;32m--> 245\u001B[1;33m         \u001B[0mtorch\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mautograd\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbackward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgradient\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mretain_graph\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcreate_graph\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minputs\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0minputs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    246\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    247\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mregister_hook\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mhook\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mc:\\program files\\python37\\lib\\site-packages\\torch\\autograd\\__init__.py\u001B[0m in \u001B[0;36mbackward\u001B[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001B[0m\n\u001B[0;32m    145\u001B[0m     Variable._execution_engine.run_backward(\n\u001B[0;32m    146\u001B[0m         \u001B[0mtensors\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mgrad_tensors_\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mretain_graph\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcreate_graph\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minputs\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 147\u001B[1;33m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001B[0m\u001B[0;32m    148\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    149\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "epoch_loss = 0.0\n",
    "num_epochs = 100\n",
    "best_loss = 999999\n",
    "best_epoch = -1\n",
    "sentence1 = \"output of the text_transform\"\n",
    "ts1s = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    print(f'Epoch - {epoch+1} / {num_epochs}')\n",
    "    model.eval()\n",
    "    ts1 = translate_sentenece(model, sentence1, english, german, device)\n",
    "    print(f\"Translated example sentence 1: \\n {ts1}\")\n",
    "    ts1s.append(ts1)\n",
    "\n",
    "    model.train()\n",
    "    for i, batch in enumerate(train_dataloader):\n",
    "        input = batch[0].to(device)\n",
    "        target = batch[0].to(device)\n",
    "\n",
    "        # pass the input to the model\n",
    "        output = model(input, target)\n",
    "        output = output[1:].reshape(-1, output.shape[2])\n",
    "        target = target[1:].reshape(-1)\n",
    "\n",
    "        optimizer.zero_grad() # clear out the accumulating grads\n",
    "\n",
    "        loss = criterion(output, target) # calculate the loss for every epoch\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        # clip the gradient if it exceeds 1\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)\n",
    "\n",
    "        optimizer.step()\n",
    "        step += 1\n",
    "        epoch_loss += loss.item()\n",
    "        writer.add_scalar(\"Training loss\", loss, global_step=step)\n",
    "\n",
    "    if epoch_loss < best_loss:\n",
    "        best_loss = epoch_loss\n",
    "        best_epoch = epoch\n",
    "        checkpoint_and_save(model, best_loss, epoch, optimizer, epoch_loss)\n",
    "\n",
    "        if (epoch - best_epoch) >= 10:\n",
    "            print('Model not converging in the last 10 epochs')\n",
    "            break\n",
    "    print(f'Epoch loss - {loss.item()}')\n",
    "    print()\n",
    "\n",
    "print(epoch_loss / len(train_dataloader))\n",
    "\n",
    "score = bleu(test_data[1:100], model, german, english, device)\n",
    "print(f'Bleu score {.2:score*100}')"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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