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This commit is contained in:
Jakub Pokrywka
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Modelowanie Języka</h1>\n",
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"<h2> 10. <i>Model rekurencyjny z atencją</i> [ćwiczenia]</h2> \n",
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"<h3> Jakub Pokrywka (2022)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import unicode_literals, print_function, division\n",
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"from io import open\n",
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"import unicodedata\n",
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"import string\n",
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"import re\n",
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"import random\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch import optim\n",
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"import torch.nn.functional as F\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"SOS_token = 0\n",
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"EOS_token = 1\n",
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"\n",
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"class Lang:\n",
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" def __init__(self):\n",
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" self.word2index = {}\n",
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" self.word2count = {}\n",
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" self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
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" self.n_words = 2 # Count SOS and EOS\n",
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"\n",
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" def addSentence(self, sentence):\n",
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" for word in sentence.split(' '):\n",
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" self.addWord(word)\n",
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"\n",
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" def addWord(self, word):\n",
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" if word not in self.word2index:\n",
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" self.word2index[word] = self.n_words\n",
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" self.word2count[word] = 1\n",
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" self.index2word[self.n_words] = word\n",
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" self.n_words += 1\n",
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" else:\n",
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" self.word2count[word] += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def unicodeToAscii(s):\n",
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" return ''.join(\n",
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" c for c in unicodedata.normalize('NFD', s)\n",
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" if unicodedata.category(c) != 'Mn'\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"pairs = []\n",
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"with open('data/eng-fra.txt') as f:\n",
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" for line in f:\n",
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" eng_line, fra_line = line.lower().rstrip().split('\\t')\n",
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"\n",
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" eng_line = re.sub(r\"([.!?])\", r\" \\1\", eng_line)\n",
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" eng_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", eng_line)\n",
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"\n",
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" fra_line = re.sub(r\"([.!?])\", r\" \\1\", fra_line)\n",
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" fra_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", fra_line)\n",
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" \n",
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" eng_line = unicodeToAscii(eng_line)\n",
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" fra_line = unicodeToAscii(fra_line)\n",
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"\n",
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" pairs.append([eng_line, fra_line])\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['run !', 'cours !']"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pairs[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"MAX_LENGTH = 10\n",
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"eng_prefixes = (\n",
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" \"i am \", \"i m \",\n",
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" \"he is\", \"he s \",\n",
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" \"she is\", \"she s \",\n",
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" \"you are\", \"you re \",\n",
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" \"we are\", \"we re \",\n",
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" \"they are\", \"they re \"\n",
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")\n",
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"\n",
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"pairs = [p for p in pairs if len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH]\n",
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"pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
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"\n",
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"eng_lang = Lang()\n",
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"fra_lang = Lang()\n",
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"\n",
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"for pair in pairs:\n",
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" eng_lang.addSentence(pair[0])\n",
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" fra_lang.addSentence(pair[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['i m .', 'j ai ans .']"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pairs[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['i m ok .', 'je vais bien .']"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pairs[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['i m ok .', ' a va .']"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pairs[2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"class EncoderRNN(nn.Module):\n",
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" def __init__(self, input_size, hidden_size):\n",
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" super(EncoderRNN, self).__init__()\n",
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" self.hidden_size = hidden_size\n",
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"\n",
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" self.embedding = nn.Embedding(input_size, hidden_size)\n",
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" self.gru = nn.GRU(hidden_size, hidden_size)\n",
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"\n",
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" def forward(self, input, hidden):\n",
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" embedded = self.embedding(input).view(1, 1, -1)\n",
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" output = embedded\n",
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" output, hidden = self.gru(output, hidden)\n",
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" return output, hidden\n",
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"\n",
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" def initHidden(self):\n",
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" return torch.zeros(1, 1, self.hidden_size, device=device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"class DecoderRNN(nn.Module):\n",
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" def __init__(self, hidden_size, output_size):\n",
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" super(DecoderRNN, self).__init__()\n",
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" self.hidden_size = hidden_size\n",
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"\n",
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" self.embedding = nn.Embedding(output_size, hidden_size)\n",
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" self.gru = nn.GRU(hidden_size, hidden_size)\n",
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" self.out = nn.Linear(hidden_size, output_size)\n",
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" self.softmax = nn.LogSoftmax(dim=1)\n",
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"\n",
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" def forward(self, input, hidden):\n",
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" output = self.embedding(input).view(1, 1, -1)\n",
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" output = F.relu(output)\n",
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" output, hidden = self.gru(output, hidden)\n",
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" output = self.softmax(self.out(output[0]))\n",
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" return output, hidden\n",
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"\n",
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" def initHidden(self):\n",
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" return torch.zeros(1, 1, self.hidden_size, device=device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"class AttnDecoderRNN(nn.Module):\n",
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" def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
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" super(AttnDecoderRNN, self).__init__()\n",
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" self.hidden_size = hidden_size\n",
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" self.output_size = output_size\n",
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" self.dropout_p = dropout_p\n",
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" self.max_length = max_length\n",
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"\n",
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" self.embedding = nn.Embedding(self.output_size, self.hidden_size)\n",
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" self.attn = nn.Linear(self.hidden_size * 2, self.max_length)\n",
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" self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)\n",
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" self.dropout = nn.Dropout(self.dropout_p)\n",
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" self.gru = nn.GRU(self.hidden_size, self.hidden_size)\n",
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" self.out = nn.Linear(self.hidden_size, self.output_size)\n",
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"\n",
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" def forward(self, input, hidden, encoder_outputs):\n",
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" embedded = self.embedding(input).view(1, 1, -1)\n",
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" embedded = self.dropout(embedded)\n",
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"\n",
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" attn_weights = F.softmax(\n",
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" self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
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" attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
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" encoder_outputs.unsqueeze(0))\n",
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"\n",
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" output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
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" output = self.attn_combine(output).unsqueeze(0)\n",
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"\n",
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" output = F.relu(output)\n",
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" output, hidden = self.gru(output, hidden)\n",
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"\n",
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" output = F.log_softmax(self.out(output[0]), dim=1)\n",
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" return output, hidden, attn_weights\n",
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"\n",
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" def initHidden(self):\n",
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" return torch.zeros(1, 1, self.hidden_size, device=device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"def tensorFromSentence(sentence, lang):\n",
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" indexes = [lang.word2index[word] for word in sentence.split(' ')]\n",
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" indexes.append(EOS_token)\n",
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" return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"teacher_forcing_ratio = 0.5\n",
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"\n",
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"def train_one_batch(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):\n",
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" encoder_hidden = encoder.initHidden()\n",
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"\n",
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" encoder_optimizer.zero_grad()\n",
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" decoder_optimizer.zero_grad()\n",
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"\n",
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" input_length = input_tensor.size(0)\n",
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" target_length = target_tensor.size(0)\n",
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"\n",
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" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
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"\n",
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" loss = 0\n",
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"\n",
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" for ei in range(input_length):\n",
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" encoder_output, encoder_hidden = encoder(\n",
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" input_tensor[ei], encoder_hidden)\n",
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" encoder_outputs[ei] = encoder_output[0, 0]\n",
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"\n",
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" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
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"\n",
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" decoder_hidden = encoder_hidden\n",
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"\n",
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" use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
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"\n",
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" if use_teacher_forcing:\n",
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" # Teacher forcing: Feed the target as the next input\n",
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" for di in range(target_length):\n",
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" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
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" decoder_input, decoder_hidden, encoder_outputs)\n",
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" loss += criterion(decoder_output, target_tensor[di])\n",
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" decoder_input = target_tensor[di] # Teacher forcing\n",
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"\n",
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" else:\n",
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" # Without teacher forcing: use its own predictions as the next input\n",
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" for di in range(target_length):\n",
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" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
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" decoder_input, decoder_hidden, encoder_outputs)\n",
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" topv, topi = decoder_output.topk(1)\n",
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" decoder_input = topi.squeeze().detach() # detach from history as input\n",
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"\n",
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" loss += criterion(decoder_output, target_tensor[di])\n",
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" if decoder_input.item() == EOS_token:\n",
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" break\n",
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"\n",
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" loss.backward()\n",
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"\n",
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" encoder_optimizer.step()\n",
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" decoder_optimizer.step()\n",
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"\n",
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" return loss.item() / target_length"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"def trainIters(encoder, decoder, n_iters, print_every=1000, learning_rate=0.01):\n",
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" print_loss_total = 0 # Reset every print_every\n",
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"\n",
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" encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)\n",
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" decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)\n",
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" \n",
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" 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], fra_lang)) for p in training_pairs]\n",
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" \n",
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" criterion = nn.NLLLoss()\n",
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"\n",
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" for i in range(1, n_iters + 1):\n",
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" training_pair = training_pairs[i - 1]\n",
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" input_tensor = training_pair[0]\n",
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" target_tensor = training_pair[1]\n",
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"\n",
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" loss = train_one_batch(input_tensor,\n",
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" target_tensor,\n",
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" encoder,\n",
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" encoder,\n",
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" encoder_optimizer,\n",
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" decoder_optimizer,\n",
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" criterion)\n",
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" \n",
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" print_loss_total += loss\n",
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"\n",
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" if i % print_every == 0:\n",
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" print_loss_avg = print_loss_total / print_every\n",
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" print_loss_total = 0\n",
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" print(f'iter: {i}, loss: {print_loss_avg}')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
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" with torch.no_grad():\n",
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" input_tensor = tensorFromSentence(sentence, eng_lang)\n",
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" input_length = input_tensor.size()[0]\n",
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" encoder_hidden = encoder.initHidden()\n",
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"\n",
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" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
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"\n",
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" for ei in range(input_length):\n",
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" encoder_output, encoder_hidden = encoder(input_tensor[ei],\n",
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" encoder_hidden)\n",
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" encoder_outputs[ei] += encoder_output[0, 0]\n",
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"\n",
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" decoder_input = torch.tensor([[SOS_token]], device=device) # SOS\n",
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"\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(fra_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": 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",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
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"iter: 150, loss: 4.238516052685087\n",
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"iter: 200, loss: 4.279887475513276\n",
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"iter: 250, loss: 4.1802274973884455\n",
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"iter: 300, loss: 4.2113521892305394\n",
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"iter: 350, loss: 4.266180963228619\n",
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"iter: 400, loss: 4.225914733432588\n",
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"iter: 450, loss: 4.1369073431075565\n",
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"iter: 500, loss: 3.9906799076019768\n",
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"iter: 550, loss: 3.842005534717016\n",
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"iter: 600, loss: 4.081443620484972\n",
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"iter: 650, loss: 4.030401878296383\n",
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"iter: 700, loss: 3.869014380984837\n",
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"iter: 750, loss: 3.8505467753031906\n",
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"iter: 800, loss: 3.855170104072209\n",
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"iter: 850, loss: 3.675745445599631\n",
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"iter: 900, loss: 3.9147777624584386\n",
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"iter: 950, loss: 3.766264297788106\n",
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"iter: 1000, loss: 3.6813155986997814\n",
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"iter: 1050, loss: 3.9307321495934144\n",
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"iter: 1100, loss: 3.9047770059525027\n",
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"iter: 1150, loss: 3.655722749588981\n",
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"iter: 1200, loss: 3.540693810886806\n",
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"iter: 1250, loss: 3.790360960324605\n",
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"iter: 1300, loss: 3.7472636015907153\n",
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"iter: 1350, loss: 3.641857419574072\n",
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"iter: 1400, loss: 3.717327400631375\n",
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"iter: 1450, loss: 3.4848567311423166\n",
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"iter: 1500, loss: 3.56774485397339\n",
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"iter: 2500, loss: 3.3513535446742218\n",
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"iter: 2800, loss: 3.467306881359647\n",
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"iter: 3000, loss: 3.3507530433563955\n",
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"iter: 3100, loss: 3.1755515496390205\n",
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"iter: 3250, loss: 3.3089625614862603\n",
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"iter: 3900, loss: 3.095807164230044\n",
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"iter: 3950, loss: 3.2343999077024916\n",
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"iter: 4000, loss: 3.3681417366512245\n",
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"iter: 4050, loss: 3.0732023419879737\n",
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"iter: 4100, loss: 3.0663742440617283\n",
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"iter: 4150, loss: 3.396770855048347\n",
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"iter: 4200, loss: 3.4262332421522292\n",
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"iter: 4250, loss: 3.060121847773354\n",
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"iter: 4300, loss: 2.895130627753243\n",
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"iter: 5000, loss: 3.3217560536218063\n",
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"iter: 5100, loss: 3.3575944598061698\n",
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"iter: 6850, loss: 2.935619188210321\n",
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"iter: 6900, loss: 2.9734530233807033\n",
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"iter: 6950, loss: 2.785320390822396\n",
|
||||
"iter: 7000, loss: 3.1911680922054106\n",
|
||||
"iter: 7050, loss: 2.7732513120363635\n",
|
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"iter: 7100, loss: 2.7432456348282948\n",
|
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"iter: 7150, loss: 2.823985375283256\n",
|
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"iter: 7200, loss: 2.927504679808541\n",
|
||||
"iter: 7250, loss: 3.0693400076760184\n",
|
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"iter: 7300, loss: 2.666468213043515\n",
|
||||
"iter: 7350, loss: 2.808132514378382\n",
|
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"iter: 7400, loss: 2.558679431067573\n",
|
||||
"iter: 7450, loss: 2.6974468813850763\n",
|
||||
"iter: 7500, loss: 2.8497490201223457\n",
|
||||
"iter: 7550, loss: 2.7490190564337236\n",
|
||||
"iter: 7600, loss: 2.8300208840067427\n",
|
||||
"iter: 7650, loss: 2.793417969741518\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, fra_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, plot_every, learning_rate)\u001b[0m\n\u001b[1;32m 16\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 17\u001b[0m target_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 19\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_tensor\u001b[49m\u001b[43m,\u001b[49m\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\u001b[43m \u001b[49m\u001b[43mencoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m print_loss_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n\u001b[1;32m 22\u001b[0m plot_loss_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n",
|
||||
"Input \u001b[0;32mIn [15]\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 45\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 46\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m---> 48\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 50\u001b[0m encoder_optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 51\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: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hidden_size = 256\n",
|
||||
"encoder1 = EncoderRNN(eng_lang.n_words, hidden_size).to(device)\n",
|
||||
"attn_decoder1 = AttnDecoderRNN(hidden_size, fra_lang.n_words, dropout_p=0.1).to(device)\n",
|
||||
"\n",
|
||||
"trainIters(encoder1, attn_decoder1, 75000, print_every=50)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"> you re sad .\n",
|
||||
"= tu es triste .\n",
|
||||
"< vous tes . . <EOS>\n",
|
||||
"\n",
|
||||
"> she is sewing a dress .\n",
|
||||
"= elle coud une robe .\n",
|
||||
"< elle est une une . . <EOS>\n",
|
||||
"\n",
|
||||
"> he is suffering from a headache .\n",
|
||||
"= il souffre d un mal de t te .\n",
|
||||
"< il est un un un un . <EOS>\n",
|
||||
"\n",
|
||||
"> i m glad to see you .\n",
|
||||
"= je suis heureux de vous voir .\n",
|
||||
"< je suis content de vous voir . <EOS>\n",
|
||||
"\n",
|
||||
"> you are only young once .\n",
|
||||
"= on n est jeune qu une fois .\n",
|
||||
"< vous tes trop plus une enfant . <EOS>\n",
|
||||
"\n",
|
||||
"> you re so sweet .\n",
|
||||
"= vous tes si gentille !\n",
|
||||
"< vous tes trop si . <EOS>\n",
|
||||
"\n",
|
||||
"> i m running out of closet space .\n",
|
||||
"= je manque d espace dans mon placard .\n",
|
||||
"< je suis un de de <EOS>\n",
|
||||
"\n",
|
||||
"> i m sort of an extrovert .\n",
|
||||
"= je suis en quelque sorte extraverti .\n",
|
||||
"< je suis un un . . <EOS>\n",
|
||||
"\n",
|
||||
"> i m out of practice .\n",
|
||||
"= je manque de pratique .\n",
|
||||
"< j ai ai pas de <EOS>\n",
|
||||
"\n",
|
||||
"> you re the last hope for humanity .\n",
|
||||
"= tu es le dernier espoir de l humanit .\n",
|
||||
"< vous tes le la la . . <EOS>\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"evaluateRandomly(encoder1, attn_decoder1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
Loading…
Reference in New Issue