first commit

.gitignore

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+.idea/
+__pycache__/
+checkpoints/

README.md

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+# PyTorch Seq2Seq Model
+
+![PicName](http://ofwzcunzi.bkt.clouddn.com/EItTIwqpcrAsrexq.png)
+
+
+> Aim to build a Marvelous ChatBot
+
+
+# Synopsis
+
+This is the first and the only opensource of **ChatBot**, I will continues update this repo personally, aim to build a intelligent ChatBot, as the next version of Jarvis.
+
+This repo will maintain to build a **Marvelous ChatBot** based on PyTorch,
+welcome star and submit PR.
+
+![PicName](http://ofwzcunzi.bkt.clouddn.com/FmLv0IpsmiMkLGiQ.png)
+# Already Done
+
+Currently this repo did those work:
+
+* based on official tutorial, this repo will move on develop a seq2seq chatbot, QA system;
+* re-constructed whole project, separate mess code into `data`, `model`, `train logic`;
+* model can be save into local, and reload from previous saved dir, which is lack in official tutorial;
+* just replace the dataset you can train your own data!
+
+Last but not least, this project will maintain or move on other repo in the future but we will
+continue implement a practical seq2seq based project to build anything you want: **Translate Machine**,
+**ChatBot**, **QA System**... anything you want.
+
+
+# Requirements
+
+```
+PyTorch
+python3
+Ubuntu Any Version
+Both CPU and GPU can works
+```
+
+# Usage
+
+Before dive into this repo, you want to glance the whole structure, we have these setups:
+
+* `config`: contains the config params, which is global in this project, you can change a global param here;
+* `datasets`: contains data and data_loader, using your own dataset, you should implement your own data_loader but just a liitle change on this one;
+* `models`: contains seq2seq model definition;
+* `utils`: this folder is very helpful, it contains some code may help you get out of anoying things, such as save model, or catch KeyboardInterrupt exception or load previous model, all can be done in here.
+
+to train model is also straightforward, just type:
+```
+python3 train.py
+```
+
+# Contribute
+
+wecome submit PR!!!! Let's build ChatBot together!
+
+# Contact
+
+if you have anyquestion, you can find me via wechat `jintianiloveu`

config/global_config.py

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+# -*- coding: utf-8 -*-
+# file: global_config.py
+# author: JinTian
+# time: 10/05/2017 4:51 PM
+# Copyright 2017 JinTian. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ------------------------------------------------------------------------
+import torch
+import os
+
+
+MODEL_PREFIX = 'seq2seq_translate'
+CHECKPOINT_DIR = './checkpoints'
+MAX_LENGTH = 10
+
+
+use_cuda = torch.cuda.is_available()
+teacher_forcing_ratio = 0.5

datasets/data_loader.py

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+# -*- coding: utf-8 -*-
+# file: data_loader.py
+# author: JinTian
+# time: 10/05/2017 6:27 PM
+# Copyright 2017 JinTian. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ------------------------------------------------------------------------
+"""
+this file load pair data into seq2seq model
+raw file contains:
+sequenceA   sequenceB
+....
+
+"""
+import torch
+from torch.autograd import Variable
+import math
+import random
+import re
+import time
+import unicodedata
+from io import open
+from config.global_config import *
+
+
+class PairDataLoader(object):
+    """
+    this class load raw file and generate pair data.
+    """
+
+    def __init__(self):
+
+        self.SOS_token = 0
+        self.EOS_token = 1
+        self.eng_prefixes = (
+            "i am ", "i m ",
+            "he is", "he s ",
+            "she is", "she s",
+            "you are", "you re ",
+            "we are", "we re ",
+            "they are", "they re "
+        )
+
+        self._prepare_data('eng', 'fra')
+
+    class Lang(object):
+
+        def __init__(self, name):
+            self.name = name
+            self.word2index = {}
+            self.word2count = {}
+            self.index2word = {0: "SOS", 1: "EOS"}
+            self.n_words = 2  # Count SOS and EOS
+
+        def add_sentence(self, sentence):
+            for word in sentence.split(' '):
+                self.add_word(word)
+
+        def add_word(self, word):
+            if word not in self.word2index:
+                self.word2index[word] = self.n_words
+                self.word2count[word] = 1
+                self.index2word[self.n_words] = word
+                self.n_words += 1
+            else:
+                self.word2count[word] += 1
+
+    def filter_pair(self, p):
+        return len(p[0].split(' ')) < MAX_LENGTH and \
+               len(p[1].split(' ')) < MAX_LENGTH and \
+               p[0].startswith(self.eng_prefixes)
+
+    def filter_pairs(self, pairs):
+        return [pair for pair in pairs if self.filter_pair(pair)]
+
+    @staticmethod
+    def unicode_to_ascii(s):
+        return ''.join(
+            c for c in unicodedata.normalize('NFD', s)
+            if unicodedata.category(c) != 'Mn'
+        )
+
+    def normalize_string(self, s):
+        s = self.unicode_to_ascii(s).lower().strip()
+        s = re.sub(r"([.!?])", r" \1", s)
+        s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
+        return s
+
+    def read_lang(self, lang1, lang2, reverse=False):
+        print("Reading lines...")
+        lines = open('./datasets/%s-%s.txt' % (lang1, lang2), encoding='utf-8'). \
+            read().strip().split('\n')
+        pairs = [[self.normalize_string(s) for s in l.split('\t')] for l in lines]
+        if reverse:
+            pairs = [list(reversed(p)) for p in pairs]
+            input_lang = self.Lang(lang2)
+            output_lang = self.Lang(lang1)
+        else:
+            input_lang = self.Lang(lang1)
+            output_lang = self.Lang(lang2)
+
+        return input_lang, output_lang, pairs
+
+    @staticmethod
+    def indexes_from_sentence(lang, sentence):
+        return [lang.word2index[word] for word in sentence.split(' ')]
+
+    def variable_from_sentence(self, lang, sentence):
+        indexes = self.indexes_from_sentence(lang, sentence)
+        indexes.append(self.EOS_token)
+        result = Variable(torch.LongTensor(indexes).view(-1, 1))
+        if use_cuda:
+            return result.cuda()
+        else:
+            return result
+
+    def _prepare_data(self, lang1, lang2, reverse=False):
+        input_lang, output_lang, pairs = self.read_lang(lang1, lang2, reverse)
+        print("Read %s sentence pairs" % len(pairs))
+        self.pairs = self.filter_pairs(pairs)
+        print("Trimmed to %s sentence pairs" % len(self.pairs))
+        print("Counting words...")
+        for pair in self.pairs:
+            input_lang.add_sentence(pair[0])
+            output_lang.add_sentence(pair[1])
+        self.input_lang = input_lang
+        self.output_lang = output_lang
+        print("Counted words:")
+        print(input_lang.name, input_lang.n_words)
+        print(output_lang.name, output_lang.n_words)
+
+    def get_pair_variable(self):
+        input_variable = self.variable_from_sentence(self.input_lang, random.choice(self.pairs)[0])
+        target_variable = self.variable_from_sentence(self.output_lang, random.choice(self.pairs)[1])
+        return input_variable, target_variable
+