update seq2seq training
This commit is contained in:
ritchie46
parent
34a55eeadb
commit
47b5045ca9
@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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@ -20,15 +20,29 @@
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"from torch import nn\n",
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"import re\n",
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"import os\n",
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"from tensorboardX import SummaryWriter\n",
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"\n",
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"try:\n",
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" from tensorboardX import SummaryWriter\n",
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"except ModuleNotFoundError:\n",
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" pass\n",
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"\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"print(device)"
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]
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},
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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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"# Sequence to sequence learning\n",
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"We will use pytorch to translate short sentences from French to English and vice versa\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": 2,
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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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@ -39,7 +53,7 @@
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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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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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@ -66,14 +80,45 @@
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" print(f.read(200))"
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]
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},
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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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"# Preparing the data I\n",
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"\n",
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"* Create a Language class that maps indexes to words and words to indexes\n",
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"\n",
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"**indexes to word**\n",
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"```python\n",
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"{0: SOS,\n",
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" 1: EOS,\n",
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" 2: The\n",
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" ...\n",
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" n: World\n",
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"}\n",
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"```\n",
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"\n",
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"**words to indexes**\n",
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"```python\n",
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"{SOS: 0,\n",
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" EOS: 1,\n",
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" The: 2\n",
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" ...\n",
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" World: n\n",
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"}\n",
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"```\n",
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"\n",
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"* implement functions to convert the letters to asscii and remove rare letters. (á, ò, ê -> a, o, e)"
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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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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"class Lang:\n",
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"class Language:\n",
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" \"\"\"\n",
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" Utility class that serves as a language dictionary\n",
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" \"\"\"\n",
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@ -142,27 +187,31 @@
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" # Reverse pairs, make Lang instances\n",
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" if reverse:\n",
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" pairs = [list(reversed(p)) for p in pairs]\n",
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" input_lang = Lang(lang2)\n",
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" output_lang = Lang(lang1)\n",
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" input_lang = Language(lang2)\n",
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" output_lang = Language(lang1)\n",
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" else:\n",
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" input_lang = Lang(lang1)\n",
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" output_lang = Lang(lang2)\n",
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" input_lang = Language(lang1)\n",
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" output_lang = Language(lang2)\n",
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"\n",
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" return input_lang, output_lang, pairs"
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]
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},
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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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"# Preparing the data II\n",
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"Since there are a lot of example sentences and we want to train something quickly, we'll trim the data set to only relatively short and simple sentences. \n",
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"Here the maximum length is 10 words (that includes ending punctuation) and we're filtering to sentences that translate to the form \"I am\" or \"He is\" etc. \n",
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"(accounting for apostrophes replaced earlier).\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": 5,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Since there are a lot of example sentences and we want to train something quickly, we'll trim the data set to only relatively short and simple sentences. \n",
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"# Here the maximum length is 10 words (that includes ending punctuation) and we're filtering to sentences that translate to the form \"I am\" or \"He is\" etc. \n",
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"# (accounting for apostrophes replaced earlier).\n",
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"\n",
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"\n",
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"\n",
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"def filter_pairs(pairs):\n",
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" MAX_LENGTH = 10\n",
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" \n",
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@ -183,37 +232,17 @@
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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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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Data:\n",
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" def __init__(self, pairs, lang_1, lang_2):\n",
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" self.pairs = np.array(pairs)\n",
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" np.random.seed(9)\n",
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" np.random.shuffle(self.pairs)\n",
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" idx_1 = [[lang_1.word2index[word] for word in s.split(' ')] \n",
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" for s in self.pairs[:, 0]]\n",
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" idx_2 = [[lang_2.word2index[word] for word in s.split(' ')]\n",
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" for s in self.pairs[:, 1]]\n",
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" self.idx_pairs = np.array(list(zip(idx_1, idx_2)))\n",
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" self.shuffle_idx = np.arange(len(pairs))\n",
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" \n",
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" def __str__(self):\n",
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" return(self.pairs)\n",
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" \n",
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" def shuffle(self):\n",
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" np.random.shuffle(self.shuffle_idx)\n",
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" self.pairs = self.pairs[self.shuffle_idx]\n",
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" self.idx_pairs = self.idx_pairs[self.shuffle_idx] \n",
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" \n",
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" "
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"# Preparing the data III\n",
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"\n",
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"Read the data from the text files, normalize the sentences, create the Language instances from the Language class and wrap the two languages in a Data class so we can shuffle the sentences and query them later."
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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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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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@ -235,14 +264,34 @@
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"array(['we are even EOS', 'nous sommes a egalite EOS'], dtype='<U60')"
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]
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},
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"execution_count": 7,
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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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"class Data:\n",
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" def __init__(self, pairs, lang_1, lang_2):\n",
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" self.pairs = np.array(pairs)\n",
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" np.random.seed(9)\n",
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" np.random.shuffle(self.pairs)\n",
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" idx_1 = [[lang_1.word2index[word] for word in s.split(' ')] \n",
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" for s in self.pairs[:, 0]]\n",
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" idx_2 = [[lang_2.word2index[word] for word in s.split(' ')]\n",
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" for s in self.pairs[:, 1]]\n",
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" self.idx_pairs = np.array(list(zip(idx_1, idx_2)))\n",
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" self.shuffle_idx = np.arange(len(pairs))\n",
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" \n",
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" def __str__(self):\n",
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" return(self.pairs)\n",
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" \n",
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" def shuffle(self):\n",
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" np.random.shuffle(self.shuffle_idx)\n",
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" self.pairs = self.pairs[self.shuffle_idx]\n",
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" self.idx_pairs = self.idx_pairs[self.shuffle_idx] \n",
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" \n",
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"def prepare_data(lang1, lang2, reverse=False):\n",
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" # read_langs initialized the Lang objects (still empty) and returns the pair sentences.\n",
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" # read_langs initialized the Language objects (still empty) and returns the pair sentences.\n",
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" input_lang, output_lang, pairs = read_langs(lang1, lang2, reverse)\n",
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" print(\"Read %s sentence pairs\" % len(pairs))\n",
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" \n",
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@ -269,7 +318,11 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The Encoder\n",
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"## Sequence to sequence model\n",
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"\n",
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"\n",
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"\n",
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"## The Encoder\n",
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"\n",
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"The encoder of a seq2seq network is a RNN that outputs some value for every word from the input sentence. For every input word the encoder outputs a vector and a hidden state, and uses the hidden state for the next input word.\n",
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"\n",
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@ -282,7 +335,7 @@
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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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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -291,7 +344,7 @@
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"torch.Size([5, 1, 2])"
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]
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},
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"execution_count": 8,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -324,9 +377,9 @@
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" return x, h\n",
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" \n",
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"\n",
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"m = Encoder(eng.n_words, 10, 2, False, 'cpu')\n",
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"scentence = torch.tensor([400, 1, 2, 6, 8])\n",
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"a = m(scentence)\n",
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"m = Encoder(eng.n_words, 10, 2, False, device)\n",
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"sentence = torch.tensor([400, 1, 2, 6, 8], device=device)\n",
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"a = m(sentence)\n",
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"a[0].shape"
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]
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},
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@ -340,22 +393,22 @@
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"\n",
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"At every step of decoding, the decoder is given an input token and hidden state. The initial input token is the start-of-string <SOS> token, and the first hidden state is the context vector (the encoder’s last hidden state).\n",
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" \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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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 13,
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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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"tensor(-23351.4941, grad_fn=<SumBackward0>)"
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"tensor(-23347.4961, grad_fn=<SumBackward0>)"
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]
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},
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"execution_count": 9,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -371,7 +424,7 @@
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" self.relu = nn.LeakyReLU()\n",
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" self.rnn = nn.GRU(embedding_size, hidden_size)\n",
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" self.out = nn.Sequential(\n",
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" nn.LeakyReLU(),\n",
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"# nn.ReLU(),\n",
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" nn.Linear(hidden_size, output_size),\n",
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" nn.LogSoftmax(2)\n",
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" )\n",
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@ -400,12 +453,12 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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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": 10,
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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@ -421,7 +474,7 @@
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"torch.Size([1, 1, 2])"
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]
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},
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"execution_count": 10,
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -435,7 +488,7 @@
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" self.device = device\n",
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" self.embedding = nn.Sequential(\n",
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" nn.Embedding(output_size, embedding_size),\n",
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" nn.Dropout(dropout)\n",
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"# nn.Dropout(dropout)\n",
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" )\n",
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" \n",
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" # Seperate neural network to learn the attention weights\n",
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@ -487,22 +540,22 @@
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"hidden_size = 256\n",
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"max_length = 10\n",
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"\n",
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"m = Encoder(eng.n_words, embedding_size, hidden_size, bidirectional=False, device='cpu')\n",
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"scentence = torch.tensor([1, 23, 9])\n",
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"out, h = m(scentence)\n",
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"m = Encoder(eng.n_words, embedding_size, hidden_size, bidirectional=False, device=device)\n",
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"sentence = torch.tensor([1, 23, 9], device=device)\n",
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"out, h = m(sentence)\n",
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"print(out.shape)\n",
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"\n",
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"encoder_outputs = torch.zeros(max_length, out.shape[-1], device='cpu')\n",
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"encoder_outputs = torch.zeros(max_length, out.shape[-1], device=device)\n",
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"encoder_outputs[:out.shape[0], :out.shape[-1]] = out.view(out.shape[0], -1)\n",
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"\n",
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"\n",
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"m = AttentionDecoder(embedding_size, hidden_size, 2, device='cpu')\n",
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"m(torch.tensor([1]), h, encoder_outputs)[0].shape"
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"m = AttentionDecoder(embedding_size, hidden_size, 2, device=device)\n",
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"m(torch.tensor([1], device=device), h, encoder_outputs)[0].shape"
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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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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -527,10 +580,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"epochs = 10\n",
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"teacher_forcing_ratio = 0.5\n",
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"\n",
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"embedding_size = 100\n",
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@ -539,12 +593,14 @@
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"encoder = Encoder(eng.n_words, embedding_size, context_vector_size, bidirectional)\n",
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"context_vector_size = context_vector_size * 2 if bidirectional else context_vector_size \n",
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"decoder = AttentionDecoder(embedding_size, context_vector_size, fra.n_words)\n",
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"writer = SummaryWriter('tb/train3')"
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"\n",
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"if 'SummaryWriter' in globals():\n",
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" writer = SummaryWriter('tb/train-3')"
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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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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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@ -553,7 +609,7 @@
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"'cuda'"
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]
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},
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"execution_count": 13,
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -564,7 +620,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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@ -574,7 +630,13 @@
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"epoch 0\n",
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"epoch 1\n",
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"epoch 2\n",
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"epoch 3\n"
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"epoch 3\n",
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"epoch 4\n",
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"epoch 5\n",
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"epoch 6\n",
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"epoch 7\n",
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"epoch 8\n",
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"epoch 9\n"
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]
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}
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],
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@ -583,10 +645,7 @@
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"def train(encoder, decoder):\n",
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" criterion = nn.NLLLoss()\n",
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" optim_encoder = torch.optim.SGD(encoder.parameters(), lr=0.01)\n",
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" optim_decoder = torch.optim.SGD(decoder.parameters(), lr=0.01)\n",
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"\n",
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" epochs = 4\n",
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" batch_size = 1\n",
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" optim_decoder = torch.optim.SGD(decoder.parameters(), lr=0.01) \n",
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"\n",
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" encoder.train(True)\n",
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" decoder.train(True)\n",
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@ -614,7 +673,8 @@
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" loss = run_decoder(decoder, criterion, fra_sentence, h, teacher_forcing, encoder_outputs)\n",
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"\n",
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" loss.backward()\n",
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" writer.add_scalar('loss', loss.cpu().item() / (len(fra_sentence)))\n",
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" if 'SummaryWriter' in globals():\n",
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" writer.add_scalar('loss', loss.cpu().item() / (len(fra_sentence)))\n",
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"\n",
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" optim_decoder.step()\n",
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" optim_encoder.step()\n",
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@ -626,260 +686,251 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"m = AttentionDecoder"
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]
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},
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{
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"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"English scentence:\t we re all single\n",
|
||||
"French scentence:\t nous sommes toutes celibataires\n",
|
||||
"English scentence:\t i am playing video games\n",
|
||||
"French scentence:\t je joue a des jeux video\n",
|
||||
"\n",
|
||||
"Model translation:\t nous sommes toutes celibataire \n",
|
||||
"Model translation:\t je joue a a ce \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he is amusing himself by playing video games\n",
|
||||
"French scentence:\t il s amuse en jouant aux jeux videos\n",
|
||||
"English scentence:\t they re looking for you\n",
|
||||
"French scentence:\t ils te cherchent\n",
|
||||
"\n",
|
||||
"Model translation:\t il est plein a des anglais \n",
|
||||
"Model translation:\t ils sont en train de \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m an artist\n",
|
||||
"French scentence:\t je suis une artiste\n",
|
||||
"English scentence:\t i m sure that s wrong\n",
|
||||
"French scentence:\t je suis sur que que c est mal\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis un artiste \n",
|
||||
"Model translation:\t je suis sur que c est \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he s enjoying himself\n",
|
||||
"French scentence:\t il s amuse\n",
|
||||
"English scentence:\t i m about to go out\n",
|
||||
"French scentence:\t je vais sortir\n",
|
||||
"\n",
|
||||
"Model translation:\t il est est a \n",
|
||||
"Model translation:\t je vais sortir de sortir \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he is an american\n",
|
||||
"French scentence:\t il est americain\n",
|
||||
"English scentence:\t he is on night duty tonight\n",
|
||||
"French scentence:\t il travaille de nuit ce soir\n",
|
||||
"\n",
|
||||
"Model translation:\t il est un \n",
|
||||
"Model translation:\t il est au soir sur ce \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you re the oldest\n",
|
||||
"French scentence:\t tu es le plus vieux\n",
|
||||
"English scentence:\t i m not saying anything\n",
|
||||
"French scentence:\t je ne dis rien\n",
|
||||
"\n",
|
||||
"Model translation:\t vous etes le plus \n",
|
||||
"Model translation:\t je ne suis pas du \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he s about to leave\n",
|
||||
"French scentence:\t il est sur le point de partir\n",
|
||||
"English scentence:\t we re not lost\n",
|
||||
"French scentence:\t nous ne sommes pas perdues\n",
|
||||
"\n",
|
||||
"Model translation:\t il est sur le point \n",
|
||||
"Model translation:\t nous ne sommes pas \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m really happy\n",
|
||||
"French scentence:\t je suis vraiment heureuse\n",
|
||||
"English scentence:\t you re very funny\n",
|
||||
"French scentence:\t tu es tres drole\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis vraiment contente \n",
|
||||
"Model translation:\t tu es tres droles \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t we re all hungry\n",
|
||||
"French scentence:\t nous avons tous faim\n",
|
||||
"English scentence:\t i m writing a novel\n",
|
||||
"French scentence:\t j ecris un roman\n",
|
||||
"\n",
|
||||
"Model translation:\t nous avons toutes faim \n",
|
||||
"Model translation:\t j ecris en roman \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m not giving you any more money\n",
|
||||
"French scentence:\t je ne te donnerai pas davantage d argent\n",
|
||||
"English scentence:\t we re all busy\n",
|
||||
"French scentence:\t nous sommes toutes occupees\n",
|
||||
"\n",
|
||||
"Model translation:\t je ne te donnerai d argent d argent \n",
|
||||
"Model translation:\t nous sommes tous occupes \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he is independent of his parents\n",
|
||||
"French scentence:\t il est independant de ses parents\n",
|
||||
"English scentence:\t you re fired\n",
|
||||
"French scentence:\t tu es vire\n",
|
||||
"\n",
|
||||
"Model translation:\t il est plein de ses parents \n",
|
||||
"Model translation:\t tu es licencie \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i am not a doctor but a teacher\n",
|
||||
"French scentence:\t je ne suis pas medecin mais professeur\n",
|
||||
"English scentence:\t he is likely to win the game\n",
|
||||
"French scentence:\t il est probable qu il remporte la partie\n",
|
||||
"\n",
|
||||
"Model translation:\t je ne suis pas medecin mais enseignant \n",
|
||||
"Model translation:\t il a des chances de remporter le \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you are responsible for the result\n",
|
||||
"French scentence:\t vous etes responsable des resultats\n",
|
||||
"English scentence:\t i m afraid for his life\n",
|
||||
"French scentence:\t je crains pour sa vie\n",
|
||||
"\n",
|
||||
"Model translation:\t vous es responsable de l \n",
|
||||
"Model translation:\t je crains pour sa vie sa \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m sympathetic\n",
|
||||
"French scentence:\t j eprouve de la compassion\n",
|
||||
"English scentence:\t we re going to work tonight\n",
|
||||
"French scentence:\t nous allons travailler ce soir\n",
|
||||
"\n",
|
||||
"Model translation:\t je ne suis \n",
|
||||
"Model translation:\t nous allons travailler ce soir \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you are drunk\n",
|
||||
"French scentence:\t tu es saoul\n",
|
||||
"English scentence:\t she is not quite content\n",
|
||||
"French scentence:\t elle n est pas tout a fait satisfaite\n",
|
||||
"\n",
|
||||
"Model translation:\t vous etes impressionnee \n",
|
||||
"Model translation:\t elle n est pas tout \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you aren t as short as i am\n",
|
||||
"French scentence:\t tu n es pas aussi petite que moi\n",
|
||||
"English scentence:\t i m a tv addict\n",
|
||||
"French scentence:\t je suis accro a la tele\n",
|
||||
"\n",
|
||||
"Model translation:\t vous n etes pas aussi petit que moi \n",
|
||||
"Model translation:\t je suis un a la \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m resilient\n",
|
||||
"French scentence:\t je suis endurante\n",
|
||||
"English scentence:\t we re not so sure\n",
|
||||
"French scentence:\t nous n en sommes pas si sures\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis endurant \n",
|
||||
"Model translation:\t nous n en sommes pas \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you re wrong again\n",
|
||||
"French scentence:\t vous avez a nouveau tort\n",
|
||||
"English scentence:\t she stopped talking\n",
|
||||
"French scentence:\t elle arreta de parler\n",
|
||||
"\n",
|
||||
"Model translation:\t tu as tort \n",
|
||||
"Model translation:\t elle a arrete \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i am thinking of my vacation\n",
|
||||
"French scentence:\t je pense a mes vacances\n",
|
||||
"English scentence:\t i m out of ammo\n",
|
||||
"French scentence:\t je suis a court de munitions\n",
|
||||
"\n",
|
||||
"Model translation:\t je songe de mes enfants \n",
|
||||
"Model translation:\t je suis a court de \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t she is able to sing very well\n",
|
||||
"French scentence:\t elle sait tres bien chanter\n",
|
||||
"English scentence:\t i m sorry i don t recognize you\n",
|
||||
"French scentence:\t je suis desolee je ne te reconnais pas\n",
|
||||
"\n",
|
||||
"Model translation:\t elle va bien bien bien bien \n",
|
||||
"Model translation:\t je suis desole je vous remets pas \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m lazy\n",
|
||||
"French scentence:\t je suis faineant\n",
|
||||
"English scentence:\t you re very brave\n",
|
||||
"French scentence:\t tu es tres brave\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis paresseux \n",
|
||||
"Model translation:\t vous etes fort brave \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he is a cruel person\n",
|
||||
"French scentence:\t c est un homme cruel\n",
|
||||
"English scentence:\t she is older and wiser now\n",
|
||||
"French scentence:\t elle est plus agee et plus sage maintenant\n",
|
||||
"\n",
|
||||
"Model translation:\t il est une personne \n",
|
||||
"Model translation:\t elle est plus et et plus \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t he is a poet\n",
|
||||
"French scentence:\t c est un poete\n",
|
||||
"English scentence:\t you re rude\n",
|
||||
"French scentence:\t vous etes grossieres\n",
|
||||
"\n",
|
||||
"Model translation:\t il est un \n",
|
||||
"Model translation:\t vous etes grossiers \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t we re plastered\n",
|
||||
"French scentence:\t nous sommes bourres\n",
|
||||
"English scentence:\t she s a jealous woman\n",
|
||||
"French scentence:\t c est une femme jalouse\n",
|
||||
"\n",
|
||||
"Model translation:\t nous sommes bourrees \n",
|
||||
"Model translation:\t c est une femme jalouse \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t she suddenly fell silent\n",
|
||||
"French scentence:\t elle se tut soudain\n",
|
||||
"English scentence:\t i m going to reconsider it\n",
|
||||
"French scentence:\t je vais y repenser encore une fois\n",
|
||||
"\n",
|
||||
"Model translation:\t elle est deux deux \n",
|
||||
"Model translation:\t je vais y aller \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you re not making this easy\n",
|
||||
"French scentence:\t vous ne rendez pas ca facile\n",
|
||||
"English scentence:\t i m not happy about it\n",
|
||||
"French scentence:\t je n en suis pas contente\n",
|
||||
"\n",
|
||||
"Model translation:\t vous ne fais pas ca \n",
|
||||
"Model translation:\t je n en suis pas heureux \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t she s very afraid of dogs\n",
|
||||
"French scentence:\t elle a une peur bleue des chiens\n",
|
||||
"English scentence:\t you re not the first\n",
|
||||
"French scentence:\t vous n etes pas le premier\n",
|
||||
"\n",
|
||||
"Model translation:\t elle a tres peur des chiens \n",
|
||||
"Model translation:\t tu n es pas le \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t she is living abroad\n",
|
||||
"French scentence:\t elle vit actuellement a l etranger\n",
|
||||
"English scentence:\t i m just following orders\n",
|
||||
"French scentence:\t je ne fais qu obeir aux ordres\n",
|
||||
"\n",
|
||||
"Model translation:\t elle est l a \n",
|
||||
"Model translation:\t je suis fais d \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m going to my grandmother s\n",
|
||||
"French scentence:\t je vais chez ma grand mere\n",
|
||||
"English scentence:\t i m on crutches for the next month\n",
|
||||
"French scentence:\t je suis en bequilles pour un mois\n",
|
||||
"\n",
|
||||
"Model translation:\t je vais me mon de la \n",
|
||||
"Model translation:\t je suis en bequilles pour le mois \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t they re not always right\n",
|
||||
"French scentence:\t elles n ont pas toujours raison\n",
|
||||
"English scentence:\t i m in charge of security\n",
|
||||
"French scentence:\t je suis responsable de la securite\n",
|
||||
"\n",
|
||||
"Model translation:\t elles n ont pas toujours \n",
|
||||
"Model translation:\t je suis la la la la \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you are very attractive in blue\n",
|
||||
"French scentence:\t le bleu vous va tres bien\n",
|
||||
"English scentence:\t she s young enough to be your daughter\n",
|
||||
"French scentence:\t elle est assez jeune pour etre ta fille\n",
|
||||
"\n",
|
||||
"Model translation:\t vous avez vraiment beaucoup a \n",
|
||||
"Model translation:\t elle est assez jeune pour etre ta fille \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t you re winning aren t you\n",
|
||||
"French scentence:\t vous gagnez n est ce pas\n",
|
||||
"English scentence:\t you re a jolly good feller\n",
|
||||
"French scentence:\t t es un joyeux drille\n",
|
||||
"\n",
|
||||
"Model translation:\t vous etes est n est ce \n",
|
||||
"Model translation:\t t es un sacre \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m ticklish\n",
|
||||
"French scentence:\t je suis chatouilleuse\n",
|
||||
"English scentence:\t he s a bit rough around the edges\n",
|
||||
"French scentence:\t il est un peu rugueux\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis mal \n",
|
||||
"Model translation:\t il est un peu peu \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m not going to name names\n",
|
||||
"French scentence:\t je ne vais pas citer de noms\n",
|
||||
"English scentence:\t we re giving up\n",
|
||||
"French scentence:\t nous abandonnons\n",
|
||||
"\n",
|
||||
"Model translation:\t je ne vais pas m a \n",
|
||||
"Model translation:\t nous abandonnons \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t they are at broadway avenue\n",
|
||||
"French scentence:\t ils sont au broadway avenue\n",
|
||||
"English scentence:\t they are very big\n",
|
||||
"French scentence:\t ils sont tres grands\n",
|
||||
"\n",
|
||||
"Model translation:\t ils sont a l des \n",
|
||||
"Model translation:\t ils sont tres gros \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m sure\n",
|
||||
"French scentence:\t j en suis sure\n",
|
||||
"English scentence:\t i m puzzled\n",
|
||||
"French scentence:\t je suis perplexe\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis certain \n",
|
||||
"Model translation:\t je suis en \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t she said that she was happy\n",
|
||||
"French scentence:\t elle a dit qu elle etait heureuse\n",
|
||||
"English scentence:\t he is no longer a child\n",
|
||||
"French scentence:\t ce n est plus un enfant\n",
|
||||
"\n",
|
||||
"Model translation:\t elle dit qu a heureuse \n",
|
||||
"Model translation:\t ce n est plus un enfant \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m all done\n",
|
||||
"French scentence:\t j ai tout fini\n",
|
||||
"English scentence:\t we re up early\n",
|
||||
"French scentence:\t nous sommes debout tot\n",
|
||||
"\n",
|
||||
"Model translation:\t je ai tout termine \n",
|
||||
"Model translation:\t nous sommes tot tot \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t we re the problem\n",
|
||||
"French scentence:\t nous sommes le probleme\n",
|
||||
"English scentence:\t i am going to be fourteen\n",
|
||||
"French scentence:\t je vais avoir quatorze ans\n",
|
||||
"\n",
|
||||
"Model translation:\t nous sommes le probleme \n",
|
||||
"Model translation:\t je vais a tout \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"English scentence:\t i m being serious\n",
|
||||
"French scentence:\t je suis serieux\n",
|
||||
"English scentence:\t i m sorry i missed your birthday\n",
|
||||
"French scentence:\t je suis desolee d avoir rate ton anniversaire\n",
|
||||
"\n",
|
||||
"Model translation:\t je suis serieux \n",
|
||||
"Model translation:\t je suis desole d avoir rate votre \n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
@ -917,15 +968,6 @@
|
||||
" \n",
|
||||
"translate(20, 60)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"decoder"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
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seq2seq/img/attention-decoder-network-adapted.png
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seq2seq/img/hello-lead.png
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seq2seq/img/hello-lead.png
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After Width: | Height: | Size: 4.5 KiB |
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seq2seq/img/seq2seq.png
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|
After Width: | Height: | Size: 22 KiB |
Loading…
x
Reference in New Issue
Block a user