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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 10. <i>Model rekurencyjny z atencją</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
""
]
},
{
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"cell_type": "markdown",
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"metadata": {},
"source": [
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"notebook na podstawie:\n",
"\n",
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"# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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": 3,
"metadata": {},
"outputs": [],
"source": [
"SOS_token = 0\n",
"EOS_token = 1\n",
"\n",
"class Lang:\n",
" def __init__(self):\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": [
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"# def unicodeToAscii(s):\n",
"# return ''.join(\n",
"# c for c in unicodedata.normalize('NFD', s)\n",
"# if unicodedata.category(c) != 'Mn'\n",
"# )"
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]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"pairs = []\n",
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"with open('data/eng-pol.txt') as f:\n",
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" for line in f:\n",
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" eng_line, pol_line = line.lower().rstrip().split('\\t')\n",
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"\n",
" eng_line = re.sub(r\"([.!?])\", r\" \\1\", eng_line)\n",
" eng_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", eng_line)\n",
"\n",
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" pol_line = re.sub(r\"([.!?])\", r\" \\1\", pol_line)\n",
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" pol_line = re.sub(r\"[^a-zA-Z.!?ąćęłńóśźżĄĆĘŁŃÓŚŹŻ]+\", r\" \", pol_line)\n",
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" \n",
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"# eng_line = unicodeToAscii(eng_line)\n",
"# pol_line = unicodeToAscii(pol_line)\n",
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"\n",
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" pairs.append([eng_line, pol_line])\n",
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"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"['hi .', 'cześć .']"
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]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"MAX_LENGTH = 10\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",
"pairs = [p for p in pairs if len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH]\n",
"pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
"\n",
"eng_lang = Lang()\n",
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"pol_lang = Lang()\n",
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"\n",
"for pair in pairs:\n",
" eng_lang.addSentence(pair[0])\n",
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" pol_lang.addSentence(pair[1])"
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]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"['i m ok .', 'ze mną wszystko w porządku .']"
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]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"['i m up .', 'wstałem .']"
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]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"['i m tom .', 'jestem tom .']"
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]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[2]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 12,
"metadata": {},
"outputs": [],
"source": [
"class DecoderRNN(nn.Module):\n",
" def __init__(self, hidden_size, output_size):\n",
" super(DecoderRNN, self).__init__()\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(output_size, hidden_size)\n",
" self.gru = nn.GRU(hidden_size, hidden_size)\n",
" self.out = nn.Linear(hidden_size, output_size)\n",
" self.softmax = nn.LogSoftmax(dim=1)\n",
"\n",
" def forward(self, input, hidden):\n",
" output = self.embedding(input).view(1, 1, -1)\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
" output = self.softmax(self.out(output[0]))\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": 13,
"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": 14,
"metadata": {},
"outputs": [],
"source": [
"def tensorFromSentence(sentence, lang):\n",
" indexes = [lang.word2index[word] for word in sentence.split(' ')]\n",
" indexes.append(EOS_token)\n",
" return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"teacher_forcing_ratio = 0.5\n",
"\n",
"def train_one_batch(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",
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" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
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" 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",
" for di in range(target_length):\n",
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" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
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" loss += criterion(decoder_output, target_tensor[di])\n",
" decoder_input = target_tensor[di] # Teacher forcing\n",
"\n",
" else:\n",
" for di in range(target_length):\n",
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" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
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" 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": 16,
"metadata": {},
"outputs": [],
"source": [
"def trainIters(encoder, decoder, n_iters, print_every=1000, learning_rate=0.01):\n",
" print_loss_total = 0 # Reset every print_every\n",
"\n",
" encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)\n",
" decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)\n",
" \n",
" training_pairs = [random.choice(pairs) for _ in range(n_iters)]\n",
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" training_pairs = [(tensorFromSentence(p[0], eng_lang), tensorFromSentence(p[1], pol_lang)) for p in training_pairs]\n",
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" \n",
" criterion = nn.NLLLoss()\n",
"\n",
" for i in range(1, n_iters + 1):\n",
" training_pair = training_pairs[i - 1]\n",
" input_tensor = training_pair[0]\n",
" target_tensor = training_pair[1]\n",
"\n",
" loss = train_one_batch(input_tensor,\n",
" target_tensor,\n",
" encoder,\n",
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" decoder,\n",
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" encoder_optimizer,\n",
" decoder_optimizer,\n",
" criterion)\n",
" \n",
" print_loss_total += loss\n",
"\n",
" if i % print_every == 0:\n",
" print_loss_avg = print_loss_total / print_every\n",
" print_loss_total = 0\n",
" print(f'iter: {i}, loss: {print_loss_avg}')\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
" with torch.no_grad():\n",
" input_tensor = tensorFromSentence(sentence, eng_lang)\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",
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" decoded_words.append(pol_lang.index2word[topi.item()])\n",
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"\n",
" decoder_input = topi.squeeze().detach()\n",
"\n",
" return decoded_words, decoder_attentions[:di + 1]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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",
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"execution_count": 19,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"iter: 50, loss: 5.242557571774437\n",
"iter: 100, loss: 4.278488328888303\n",
"iter: 150, loss: 4.345584976514179\n",
"iter: 200, loss: 4.372515664100646\n",
"iter: 250, loss: 4.305285846165248\n",
"iter: 300, loss: 4.340697079749336\n",
"iter: 350, loss: 4.225462563787189\n",
"iter: 400, loss: 3.931193191331531\n",
"iter: 450, loss: 4.255609704971314\n",
"iter: 500, loss: 4.110652269242301\n",
"iter: 550, loss: 4.10534066640763\n",
"iter: 600, loss: 4.21553361088889\n",
"iter: 650, loss: 4.182825716926937\n",
"iter: 700, loss: 4.125172647006929\n",
"iter: 750, loss: 3.92194454044766\n",
"iter: 800, loss: 3.9588410959697904\n",
"iter: 850, loss: 4.045722855431693\n",
"iter: 900, loss: 3.989374102009668\n",
"iter: 950, loss: 3.8188858143791307\n",
"iter: 1000, loss: 4.083652021286979\n",
"iter: 1050, loss: 3.853677998118931\n",
"iter: 1100, loss: 4.240768341064452\n",
"iter: 1150, loss: 3.9389620546991857\n",
"iter: 1200, loss: 3.797674254826137\n",
"iter: 1250, loss: 3.9533765572441957\n",
"iter: 1300, loss: 4.227734768761528\n",
"iter: 1350, loss: 4.098812954554482\n",
"iter: 1400, loss: 3.970981749352953\n",
"iter: 1450, loss: 3.961285910742623\n",
"iter: 1500, loss: 3.7287075826554075\n",
"iter: 1550, loss: 4.011922280311584\n",
"iter: 1600, loss: 3.819334566025507\n",
"iter: 1650, loss: 4.058212718903073\n",
"iter: 1700, loss: 3.7410677611335874\n",
"iter: 1750, loss: 3.6925343862261077\n",
"iter: 1800, loss: 3.686991301317063\n",
"iter: 1850, loss: 4.038253510641673\n",
"iter: 1900, loss: 3.6877949990999133\n",
"iter: 1950, loss: 3.6264906252452302\n",
"iter: 2000, loss: 3.9525268210607845\n",
"iter: 2050, loss: 3.6234626099268596\n",
"iter: 2100, loss: 3.777446182023912\n",
"iter: 2150, loss: 3.6546845786609343\n",
"iter: 2200, loss: 3.5066249518167414\n",
"iter: 2250, loss: 3.4109464065082484\n",
"iter: 2300, loss: 3.3479860273467175\n",
"iter: 2350, loss: 3.4630342653289676\n",
"iter: 2400, loss: 3.565554211571103\n",
"iter: 2450, loss: 3.575563238794841\n",
"iter: 2500, loss: 4.010586709249588\n",
"iter: 2550, loss: 3.7003344478380105\n",
"iter: 2600, loss: 3.8373477258985003\n",
"iter: 2650, loss: 3.622782635825022\n",
"iter: 2700, loss: 3.539290331628588\n",
"iter: 2750, loss: 3.6115450941721594\n",
"iter: 2800, loss: 3.5639050323849633\n",
"iter: 2850, loss: 3.7455426768651083\n",
"iter: 2900, loss: 3.590438606050279\n",
"iter: 2950, loss: 3.6904614701346747\n",
"iter: 3000, loss: 3.8645651381356383\n",
"iter: 3050, loss: 3.704173568400126\n",
"iter: 3100, loss: 3.6185882792548534\n",
"iter: 3150, loss: 3.4422046549131\n",
"iter: 3200, loss: 3.4467696623802184\n",
"iter: 3250, loss: 3.5570392836237725\n",
"iter: 3300, loss: 3.4138823104585923\n",
"iter: 3350, loss: 3.1434556758668686\n",
"iter: 3400, loss: 3.683112149511065\n",
"iter: 3450, loss: 3.548991588297344\n",
"iter: 3500, loss: 3.8323247443380795\n",
"iter: 3550, loss: 3.51619815774191\n",
"iter: 3600, loss: 3.9732376934687297\n",
"iter: 3650, loss: 3.4628570978679356\n",
"iter: 3700, loss: 3.819421215375265\n",
"iter: 3750, loss: 3.7018858858819983\n",
"iter: 3800, loss: 3.332828684034802\n",
"iter: 3850, loss: 3.39565832292466\n",
"iter: 3900, loss: 3.6046563055099\n",
"iter: 3950, loss: 3.4032811139727404\n",
"iter: 4000, loss: 3.188702507541294\n",
"iter: 4050, loss: 3.246296736966995\n",
"iter: 4100, loss: 3.3872493017287484\n",
"iter: 4150, loss: 3.2912982750620166\n",
"iter: 4200, loss: 3.439030005250657\n",
"iter: 4250, loss: 3.6874865720536967\n",
"iter: 4300, loss: 3.2006266547081967\n",
"iter: 4350, loss: 3.3141552084968198\n",
"iter: 4400, loss: 3.1777613387107846\n",
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"iter: 30250, loss: 0.8539114402177789\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m encoder1 \u001b[38;5;241m=\u001b[39m EncoderRNN(eng_lang\u001b[38;5;241m.\u001b[39mn_words, hidden_size)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 3\u001b[0m attn_decoder1 \u001b[38;5;241m=\u001b[39m AttnDecoderRNN(hidden_size, pol_lang\u001b[38;5;241m.\u001b[39mn_words, dropout_p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mtrainIters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mencoder1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattn_decoder1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m75000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_every\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\n",
"Input \u001b[0;32mIn [16]\u001b[0m, in \u001b[0;36mtrainIters\u001b[0;34m(encoder, decoder, n_iters, print_every, learning_rate)\u001b[0m\n\u001b[1;32m 14\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 15\u001b[0m target_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 17\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecoder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m print_loss_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m%\u001b[39m print_every \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36mtrain_one_batch\u001b[0;34m(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decoder_input\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m==\u001b[39m EOS_token:\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m encoder_optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 45\u001b[0m decoder_optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
"File \u001b[0;32m~/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/torch/_tensor.py:363\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 356\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 357\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 361\u001b[0m create_graph\u001b[38;5;241m=\u001b[39mcreate_graph,\n\u001b[1;32m 362\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs)\n\u001b[0;32m--> 363\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/torch/autograd/__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 168\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 170\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
2022-05-29 18:14:19 +02:00
]
}
],
"source": [
"hidden_size = 256\n",
"encoder1 = EncoderRNN(eng_lang.n_words, hidden_size).to(device)\n",
2022-05-29 19:05:03 +02:00
"attn_decoder1 = AttnDecoderRNN(hidden_size, pol_lang.n_words, dropout_p=0.1).to(device)\n",
2022-05-29 18:14:19 +02:00
"\n",
"trainIters(encoder1, attn_decoder1, 75000, print_every=50)"
]
},
{
"cell_type": "code",
2022-05-29 20:00:36 +02:00
"execution_count": 20,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> he s a very important person .\n",
"= on jest bardzo ważnym człowiekiem .\n",
"< on jest bardzo ważnym człowiekiem . <EOS>\n",
"\n",
"> i m beautiful .\n",
"= jestem piękny .\n",
"< jestem piękna . <EOS>\n",
"\n",
"> we re quite certain of that .\n",
"= jesteśmy tego całkiem pewni .\n",
"< jesteśmy tego całkiem pewni . <EOS>\n",
"\n",
"> we are all looking forward to seeing you .\n",
"= miło nam będzie ponownie się z panem spotkać .\n",
"< miło nam nam ponownie się z tobą . . <EOS>\n",
"\n",
"> i m inside .\n",
"= jestem w środku .\n",
"< jestem w środku . <EOS>\n",
"\n",
"> i m giving up smoking .\n",
"= rzucam palenie .\n",
"< rzuciłem palenie . <EOS>\n",
"\n",
"> we re not arguing .\n",
"= nie kłócimy się .\n",
"< nie wychodzimy . <EOS>\n",
"\n",
"> i m not prepared to do that yet .\n",
"= nie jestem jeszcze przygotowany żeby to zrobić .\n",
"< nie jestem jeszcze przygotowany żeby to zrobić . <EOS>\n",
"\n",
"> i m a free man .\n",
"= jestem wolnym człowiekiem .\n",
"< jestem wolnym człowiekiem . <EOS>\n",
"\n",
"> i m still on the clock .\n",
"= jeszcze jestem w pracy .\n",
"< wciąż jestem w domu . <EOS>\n",
"\n"
]
}
],
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"source": [
"evaluateRandomly(encoder1, attn_decoder1)"
]
},
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{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m ok .', 'ze mną wszystko w porządku .']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[0]"
]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}
