├── .python-version ├── .gitignore ├── requirements.txt ├── parse_candidates.py ├── bin ├── train_all.sh ├── task1.sh ├── task2.sh ├── task3.sh ├── task4.sh ├── task5.sh ├── task6.sh ├── utils.sh └── test_all.sh ├── utils.py ├── make_tensor.py ├── parse_dialogs.py ├── README.md ├── model.py ├── test.py ├── train.py └── LICENSE /.python-version: -------------------------------------------------------------------------------- 1 | 3.6.0 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | dataset/ 2 | .ipynb_checkpoints/ 3 | checkpoints/ 4 | data/ 5 | log/ 6 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # tensorflow==1.0.0 (install it by yourself for gpu) 2 | numpy==1.12.0 3 | -------------------------------------------------------------------------------- /parse_candidates.py: -------------------------------------------------------------------------------- 1 | from sys import argv 2 | 3 | 4 | def parse_candidates(filename): 5 | with open(filename, 'r') as f: 6 | return [' '.join(line.strip().split(' ')[1:]) for line in f] 7 | 8 | 9 | if __name__ == '__main__': 10 | filename = argv[1] 11 | for cand in parse_candidates(filename): 12 | print('{}\t'.format(cand)) 13 | -------------------------------------------------------------------------------- /bin/train_all.sh: -------------------------------------------------------------------------------- 1 | screen -S task1 -d -m bash -c "bin/task1.sh True > log/task1" 2 | screen -S task2 -d -m bash -c "bin/task2.sh True > log/task2" 3 | screen -S task3 -d -m bash -c "bin/task3.sh True > log/task3" 4 | screen -S task4 -d -m bash -c "bin/task4.sh True > log/task4" 5 | screen -S task5 -d -m bash -c "bin/task5.sh True > log/task5" 6 | screen -S task6 -d -m bash -c "bin/task6.sh True > log/task6" 7 | -------------------------------------------------------------------------------- /bin/task1.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | source bin/utils.sh 4 | 5 | task="task-1" 6 | mkdir -p checkpoints/$task/ 7 | WITH_PREPROCESS="$1" 8 | [ -z "$WITH_PREPROCESS" ] && WITH_PREPROCESS='False' 9 | 10 | if [ "$WITH_PREPROCESS" == "True" ]; then 11 | python parse_candidates.py data/dialog-bAbI-tasks/dialog-babi-candidates.txt > data/candidates.tsv 12 | parse_dialogs 'dialog-babi-task1-API-calls' $task '--with_history' 13 | fi 14 | 15 | python train.py --train data/train-$task.tsv --dev data/dev-$task-500.tsv \ 16 | --vocab data/vocab-$task.tsv --save_dir checkpoints/$task/model 17 | -------------------------------------------------------------------------------- /bin/task2.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | source bin/utils.sh 4 | 5 | task="task-2" 6 | mkdir -p checkpoints/$task/ 7 | WITH_PREPROCESS="$1" 8 | [ -z "$WITH_PREPROCESS" ] && WITH_PREPROCESS='False' 9 | 10 | 11 | if [ "$WITH_PREPROCESS" == "True" ]; then 12 | python parse_candidates.py data/dialog-bAbI-tasks/dialog-babi-candidates.txt > data/candidates.tsv 13 | parse_dialogs 'dialog-babi-task2-API-refine' $task 14 | fi 15 | 16 | python train.py --train data/train-$task.tsv --dev data/dev-$task-500.tsv \ 17 | --vocab data/vocab-$task.tsv --emb_dim 128 --save_dir checkpoints/$task/model 18 | -------------------------------------------------------------------------------- /bin/task3.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | source bin/utils.sh 4 | 5 | task="task-3" 6 | mkdir -p checkpoints/$task/ 7 | WITH_PREPROCESS="$1" 8 | [ -z "$WITH_PREPROCESS" ] && WITH_PREPROCESS='False' 9 | 10 | if [ "$WITH_PREPROCESS" == "True" ]; then 11 | python parse_candidates.py data/dialog-bAbI-tasks/dialog-babi-candidates.txt > data/candidates.tsv 12 | parse_dialogs 'dialog-babi-task3-options' $task "--ignore_options" 13 | fi 14 | 15 | python train.py --train data/train-$task.tsv --dev data/dev-$task-500.tsv \ 16 | --vocab data/vocab-$task.tsv --emb_dim 128 --save_dir checkpoints/$task/model \ 17 | --margin 0.1 --negative_cand 1000 18 | -------------------------------------------------------------------------------- /bin/task4.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | source bin/utils.sh 4 | 5 | task="task-4" 6 | mkdir -p checkpoints/$task/ 7 | WITH_PREPROCESS="$1" 8 | [ -z "$WITH_PREPROCESS" ] && WITH_PREPROCESS='False' 9 | 10 | if [ "$WITH_PREPROCESS" == "True" ]; then 11 | python parse_candidates.py data/dialog-bAbI-tasks/dialog-babi-candidates.txt > data/candidates.tsv 12 | parse_dialogs 'dialog-babi-task4-phone-address' $task "--ignore_options" 13 | fi 14 | 15 | python train.py --train data/train-$task.tsv --dev data/dev-$task-500.tsv \ 16 | --vocab data/vocab-$task.tsv --emb_dim 128 --save_dir checkpoints/$task/model \ 17 | --margin 0.1 --negative_cand 1000 --learning_rate 0.001 18 | -------------------------------------------------------------------------------- /bin/task5.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | source bin/utils.sh 4 | 5 | task="task-5" 6 | mkdir -p checkpoints/$task/ 7 | WITH_PREPROCESS="$1" 8 | [ -z "$WITH_PREPROCESS" ] && WITH_PREPROCESS='False' 9 | 10 | if [ "$WITH_PREPROCESS" == "True" ]; then 11 | python parse_candidates.py data/dialog-bAbI-tasks/dialog-babi-candidates.txt > data/candidates.tsv 12 | parse_dialogs 'dialog-babi-task5-full-dialogs' $task "--ignore_options --with_history" 13 | fi 14 | 15 | python train.py --train data/train-$task.tsv --dev data/dev-$task-500.tsv \ 16 | --vocab data/vocab-$task.tsv --emb_dim 32 --save_dir checkpoints/$task/model \ 17 | --margin 0.01 --negative_cand 100 --learning_rate 0.01 18 | -------------------------------------------------------------------------------- /bin/task6.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | source bin/utils.sh 4 | 5 | task="task-6" 6 | mkdir -p checkpoints/$task/ 7 | WITH_PREPROCESS="$1" 8 | [ -z "$WITH_PREPROCESS" ] && WITH_PREPROCESS='False' 9 | 10 | if [ "$WITH_PREPROCESS" == "True" ]; then 11 | python parse_candidates.py data/dialog-bAbI-tasks/dialog-babi-task6-dstc2-candidates.txt > data/candidates-dstc2.tsv 12 | parse_dialogs 'dialog-babi-task6-dstc2' $task "--ignore_options" 13 | fi 14 | 15 | python train.py --train data/train-$task.tsv --dev data/dev-$task-500.tsv \ 16 | --vocab data/vocab-$task.tsv --emb_dim 128 --save_dir checkpoints/$task/model \ 17 | --margin 0.01 --negative_cand 100 --learning_rate 0.001 --candidates data/candidates-dstc2.tsv 18 | -------------------------------------------------------------------------------- /bin/utils.sh: -------------------------------------------------------------------------------- 1 | if which gshuf >/dev/null; then 2 | shuf_cmd="gshuf" 3 | else 4 | shuf_cmd="shuf" 5 | fi 6 | 7 | function parse_dialogs { 8 | prefix=$1 9 | task=$2 10 | additional_options="$3" 11 | base_path=data/dialog-bAbI-tasks 12 | python parse_dialogs.py --input $base_path/$prefix-trn.txt $additional_options > data/train-$task.tsv 13 | python parse_dialogs.py --input $base_path/$prefix-dev.txt $additional_options > data/dev-$task.tsv 14 | python parse_dialogs.py --input $base_path/$prefix-tst.txt $additional_options > data/test-$task.tsv 15 | eval $shuf_cmd -n 500 data/dev-$task.tsv > data/dev-$task-500.tsv 16 | cat data/train-$task.tsv data/dev-$task.tsv data/test-$task.tsv | python build_vocabulary.py > data/vocab-$task.tsv 17 | } 18 | -------------------------------------------------------------------------------- /bin/test_all.sh: -------------------------------------------------------------------------------- 1 | echo "Task-1 on test set" 2 | python test.py --test data/test-task-1.tsv --candidates data/candidates.tsv \ 3 | --vocab data/vocab-task-1.tsv --checkpoint_dir checkpoints/task-1 --emb_dim 32 4 | 5 | 6 | echo "Task-2 on test set" 7 | python test.py --test data/test-task-2.tsv --candidates data/candidates.tsv \ 8 | --vocab data/vocab-task-2.tsv --checkpoint_dir checkpoints/task-2 --emb_dim 128 9 | 10 | 11 | echo "Task-3 on test set" 12 | python test.py --test data/test-task-3.tsv --candidates data/candidates.tsv \ 13 | --vocab data/vocab-task-3.tsv --checkpoint_dir checkpoints/task-3 --emb_dim 128 14 | 15 | echo "Task-4 on test set" 16 | python test.py --test data/test-task-4.tsv --candidates data/candidates.tsv \ 17 | --vocab data/vocab-task-4.tsv --checkpoint_dir checkpoints/task-4 --emb_dim 128 18 | 19 | 20 | echo "Task-5 on test set" 21 | python test.py --test data/test-task-5.tsv --candidates data/candidates.tsv \ 22 | --vocab data/vocab-task-5.tsv --checkpoint_dir checkpoints/task-5 --emb_dim 32 23 | 24 | 25 | echo "Task-6 on test set" 26 | python test.py --test data/test-task-6.tsv --candidates data/candidates-dstc2.tsv \ 27 | --vocab data/vocab-task-6.tsv --checkpoint_dir checkpoints/task-6 --emb_dim 128 28 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def batch_iter(tensor, batch_size, shuffle=False): 5 | batches_count = tensor.shape[0] // batch_size 6 | 7 | if shuffle: 8 | shuffle_indices = np.random.permutation(np.arange(tensor.shape[0])) 9 | data = tensor[shuffle_indices] 10 | else: 11 | data = tensor 12 | 13 | neg_shuffle_indices = np.random.permutation(np.arange(tensor.shape[0])) 14 | negative_data = tensor[neg_shuffle_indices] 15 | 16 | for batch_num in range(batches_count): 17 | start_index = batch_num * batch_size 18 | end_index = min((batch_num + 1)*batch_size, tensor.shape[0]) 19 | yield data[start_index:end_index] 20 | 21 | 22 | def neg_sampling_iter(tensor, batch_size, count, seed=None): 23 | batches_count = tensor.shape[0] // batch_size 24 | trials = 0 25 | np.random.seed(seed) 26 | shuffle_indices = np.random.permutation(np.arange(tensor.shape[0])) 27 | data = tensor[shuffle_indices] 28 | for batch_num in range(batches_count): 29 | trials += 1 30 | start_index = batch_num * batch_size 31 | end_index = min((batch_num + 1)*batch_size, tensor.shape[0]) 32 | if trials > count: 33 | return 34 | else: 35 | yield data[start_index:end_index] 36 | 37 | -------------------------------------------------------------------------------- /make_tensor.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from sys import argv 3 | 4 | 5 | def vectorize_utt(utt, vocab): 6 | vec = np.zeros(len(vocab)) 7 | for w in utt.split(' '): 8 | try: 9 | vec[vocab[w]] = 1 10 | except KeyError: 11 | pass 12 | return vec 13 | 14 | 15 | def vectorize_all(context_response_pairs, vocab): 16 | tensor = np.ndarray((len(context_response_pairs), 2, len(vocab))) 17 | 18 | for ind, context_response in enumerate(context_response_pairs): 19 | context, response = context_response 20 | context_vec = vectorize_utt(context, vocab) 21 | response_vec = vectorize_utt(response, vocab) 22 | tensor[ind][0] = context_vec 23 | tensor[ind][1] = response_vec 24 | 25 | return tensor 26 | 27 | 28 | def load_vocab(vocab_filename): 29 | vocab = {} 30 | with open(vocab_filename, 'r') as f: 31 | for line in f: 32 | ind, word = line.strip().split('\t') 33 | vocab[word] = int(ind) 34 | return vocab 35 | 36 | 37 | def load_train(train_filename): 38 | context_response_pairs = [] 39 | with open(train_filename, 'r') as f: 40 | for line in f: 41 | context, response = line.strip().split('\t') 42 | context_response_pairs.append((context, response)) 43 | return context_response_pairs 44 | 45 | 46 | def make_tensor(train_filename, vocab): 47 | if type(vocab) == 'str': 48 | vocab = load_vocab(vocab_filename) 49 | train = load_train(train_filename) 50 | X = vectorize_all(train, vocab) 51 | print(train_filename, X.shape) 52 | return X 53 | 54 | 55 | if __name__ == '__main__': 56 | train_filename = argv[1] 57 | vocab_filename = argv[2] 58 | main(train_filename, vocab_filename) 59 | -------------------------------------------------------------------------------- /parse_dialogs.py: -------------------------------------------------------------------------------- 1 | from sys import argv 2 | import argparse 3 | 4 | 5 | def parse_dialogs(filename, with_history, ignore_options): 6 | dialogs = [] 7 | with open(filename, 'r') as f: 8 | dialog = [] 9 | for line in f: 10 | if line.strip() == '': 11 | dialogs.append(dialog) 12 | dialog = [] 13 | else: 14 | splitted = line.strip().split('\t') 15 | if len(splitted) == 1 and ignore_options: 16 | continue 17 | elif len(splitted) == 1: 18 | raise ValueError('Line has not 2 utterances (seems like an option) {}'.format(splitted)) 19 | user_utt, bot_utt = splitted 20 | utt_num = user_utt.split(' ')[0] 21 | user_utt = ' '.join(user_utt.split(' ')[1:]) 22 | if user_utt == '': 23 | user_utt = '' 24 | 25 | if bot_utt == '': 26 | bot_utt = '' 27 | if with_history: 28 | if len(dialog) > 0: 29 | prev_step = dialog[len(dialog) - 1] 30 | user_utt_with_history = "{} {} {}".format(prev_step[1], prev_step[2], user_utt) 31 | else: 32 | user_utt_with_history = user_utt 33 | dialog.append((utt_num, user_utt_with_history, bot_utt)) 34 | else: 35 | dialog.append((utt_num, user_utt, bot_utt)) 36 | return dialogs 37 | 38 | 39 | def _parse_args(): 40 | parser = argparse.ArgumentParser() 41 | 42 | parser.add_argument('--input', help='Path to filename') 43 | parser.add_argument('--with_history', action='store_true', default=False) 44 | parser.add_argument('--ignore_options', action='store_true', default=False) 45 | 46 | args = parser.parse_args() 47 | 48 | return args 49 | 50 | 51 | if __name__ == '__main__': 52 | args = _parse_args() 53 | dialogs = parse_dialogs(args.input, args.with_history, args.ignore_options) 54 | for dialog in dialogs: 55 | for _, user_utt, bot_utt in dialog: 56 | print('{}\t{}'.format(user_utt, bot_utt)) 57 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Description 2 | 3 | It is the implementation of Supervised embedding models from 4 | [[Learning End-to-End Goal-Oriented Dialog](https://arxiv.org/abs/1605.07683v3)] paper. 5 | 6 | Results almost the same as in the paper. 7 | 8 | Here you can find Russian paper-note of the paper: [link](https://github.com/sld/deeplearning-papernotes/blob/master/notes/end-to-end-goal.md). 9 | 10 | # Environment 11 | 12 | * Python 3.6.0 13 | * tensorflow 1.0.0 14 | * Dialog bAbI Tasks Data 1-6 corpus, download by the [link](https://research.fb.com/downloads/babi/). 15 | This corpus should be placed in data/dialog-bAbI-tasks directory. 16 | 17 | 18 | All packages are listed in requirements.txt. 19 | 20 | # Reproduce results 21 | 22 | 0. Setup the environment. 23 | 1. Run: `bin/train_all.sh` 24 | 2. After approx. 1 hour run it in test set: `bin/test_all.sh` 25 | 26 | 27 | # Results 28 | 29 | 16.03.17. 30 | 31 | In the table per-response accuracy is shown. 32 | 33 | 34 | 35 | 38 | 41 | 44 | 45 | 46 | 49 | 52 | 55 | 56 | 57 | 60 | 63 | 66 | 67 | 68 | 71 | 74 | 77 | 78 | 79 | 82 | 85 | 88 | 89 | 90 | 93 | 96 | 99 | 100 | 101 | 104 | 107 | 110 | 111 |
36 | Task 37 | 39 | Supervised Embedding (Article) 40 | 42 | Supervised Embedding (Ours) 43 |
47 | T1: Issuing API calls 48 | 50 | 100 51 | 53 | 99.6 54 |
58 | T2: Updating API calls 59 | 61 | 68.4 62 | 64 | 68.4 65 |
69 | T3: Displaying options 70 | 72 | 64.9 73 | 75 | 56.9 76 |
80 | T4: Providing information 81 | 83 | 57.2 84 | 86 | 57.1 87 |
91 | T5: Full dialogs 92 | 94 | 75.4 95 | 97 | 62.1 98 |
102 | T6: Dialog state tracking 2 103 | 105 | 22.6 106 | 108 | 10.8 109 |
112 | 113 | Open question: 114 | 115 | 1. When we training with use_history=True should we test on pre-processed 116 | dataset as in train? Or should we concat each output in test and build history 117 | on the fly? 118 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import math 3 | import numpy as np 4 | 5 | 6 | class Model: 7 | def __init__(self, vocab_dim, emb_dim, margin=0.01): 8 | self._vocab_dim = vocab_dim 9 | self._emb_dim = emb_dim 10 | self._random_seed = 42 11 | self._margin = margin 12 | self._assemble_graph() 13 | 14 | def _assemble_graph(self): 15 | self._create_placeholders() 16 | tf.set_random_seed(self._random_seed + 1) 17 | 18 | A_var = tf.Variable( 19 | initial_value=tf.random_uniform( 20 | shape=[self._emb_dim, self._vocab_dim], 21 | minval=-1, maxval=1, seed=(self._random_seed + 2) 22 | ) 23 | ) 24 | B_var = tf.Variable( 25 | initial_value=tf.random_uniform( 26 | shape=[self._emb_dim, self._vocab_dim], 27 | minval=-1, maxval=1, seed=(self._random_seed + 3) 28 | ) 29 | ) 30 | self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step') 31 | 32 | cont_mult = tf.transpose(tf.matmul(A_var, tf.transpose(self.context_batch))) 33 | resp_mult = tf.matmul(B_var, tf.transpose(self.response_batch)) 34 | neg_resp_mult = tf.matmul(B_var, tf.transpose(self.neg_response_batch)) 35 | 36 | pos_raw_f = tf.diag_part(tf.matmul(cont_mult, resp_mult)) 37 | neg_raw_f = tf.diag_part(tf.matmul(cont_mult, neg_resp_mult)) 38 | self.f_pos = pos_raw_f 39 | self.f_neg = neg_raw_f 40 | 41 | self.loss = tf.reduce_sum(tf.nn.relu(self.f_neg - self.f_pos + self._margin)) 42 | 43 | def _create_placeholders(self): 44 | self.context_batch = tf.placeholder(dtype=tf.float32, name='Context', shape=[None, self._vocab_dim]) 45 | self.response_batch = tf.placeholder(dtype=tf.float32, name='Response', shape=[None, self._vocab_dim]) 46 | self.neg_response_batch = tf.placeholder(dtype=tf.float32, name='NegResponse', shape=[None, self._vocab_dim]) 47 | 48 | # TODO: Implement summaries 49 | def _init_summaries(self): 50 | self.accuracy = tf.placeholder_with_default(0.0, shape=(), name='Accuracy') 51 | self.accuracy_summary = tf.scalar_summary('Accuracy summary', self.accuracy) 52 | 53 | self.f_pos_summary = tf.histogram_summary('f_pos', self.f_pos) 54 | self.f_neg_summary = tf.histogram_summary('f_neg', self.f_neg) 55 | 56 | self.loss_summary = tf.scalar_summary('Mini-batch loss', self.loss) 57 | self.summary_op = tf.merge_summary( 58 | [ 59 | self.f_pos_summary, 60 | self.f_neg_summary, 61 | self.loss_summary 62 | ] 63 | ) 64 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | from make_tensor import make_tensor, load_vocab 2 | from model import Model 3 | from sys import argv 4 | from utils import batch_iter 5 | from tqdm import tqdm 6 | import numpy as np 7 | import tensorflow as tf 8 | import argparse 9 | 10 | 11 | def main(test_tensor, candidates_tensor, model, checkpoint_dir): 12 | saver = tf.train.Saver() 13 | config = tf.ConfigProto() 14 | config.gpu_options.allow_growth = True 15 | with tf.Session(config=config) as sess: 16 | sess.run(tf.global_variables_initializer()) 17 | ckpt = tf.train.get_checkpoint_state(checkpoint_dir) 18 | saver.restore(sess, ckpt.model_checkpoint_path) 19 | print(evaluate(test_tensor, candidates_tensor, sess, model)) 20 | 21 | 22 | def evaluate(test_tensor, candidates_tensor, sess, model): 23 | neg = 0 24 | pos = 0 25 | for row in tqdm(test_tensor): 26 | true_context = [row[0]] 27 | test_score = sess.run( 28 | model.f_pos, 29 | feed_dict={model.context_batch: true_context, 30 | model.response_batch: [row[1]], 31 | model.neg_response_batch: [row[1]]} 32 | ) 33 | test_score = test_score[0] 34 | 35 | is_pos = evaluate_one_row(candidates_tensor, true_context, sess, model, test_score, row[1]) 36 | if is_pos: 37 | pos += 1 38 | else: 39 | neg += 1 40 | return (pos, neg, pos / (pos+neg)) 41 | 42 | 43 | def evaluate_one_row(candidates_tensor, true_context, sess, model, test_score, true_response): 44 | for batch in batch_iter(candidates_tensor, 512): 45 | candidate_responses = batch[:, 0, :] 46 | context_batch = np.repeat(true_context, candidate_responses.shape[0], axis=0) 47 | 48 | scores = sess.run( 49 | model.f_pos, 50 | feed_dict={model.context_batch: context_batch, 51 | model.response_batch: candidate_responses, 52 | model.neg_response_batch: candidate_responses} 53 | ) 54 | for ind, score in enumerate(scores): 55 | if score == float('Inf') or score == -float('Inf') or score == float('NaN'): 56 | print(score, ind, scores[ind]) 57 | raise ValueError 58 | if score >= test_score and not np.array_equal(candidate_responses[ind], true_response): 59 | return False 60 | return True 61 | 62 | 63 | def _parse_args(): 64 | parser = argparse.ArgumentParser() 65 | 66 | parser.add_argument('--test', help='Path to test filename') 67 | parser.add_argument('--vocab', default='data/vocab.tsv') 68 | parser.add_argument('--candidates', default='data/candidates.tsv') 69 | parser.add_argument('--checkpoint_dir') 70 | parser.add_argument('--emb_dim', type=int, default=32) 71 | 72 | args = parser.parse_args() 73 | 74 | return args 75 | 76 | if __name__ == '__main__': 77 | args = _parse_args() 78 | vocab = load_vocab(args.vocab) 79 | test_tensor = make_tensor(args.test, vocab) 80 | candidates_tensor = make_tensor(args.candidates, vocab) 81 | model = Model(len(vocab), args.emb_dim) 82 | main(test_tensor, candidates_tensor, model, args.checkpoint_dir) 83 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | import numpy as np 3 | import argparse 4 | import logging 5 | import sys 6 | from tqdm import tqdm 7 | from make_tensor import make_tensor, load_vocab 8 | from model import Model 9 | from sys import argv 10 | from test import evaluate 11 | from utils import batch_iter, neg_sampling_iter 12 | 13 | 14 | def _setup_logger(): 15 | logging.basicConfig( 16 | format='[%(levelname)s] %(asctime)s: %(message)s (%(pathname)s:%(lineno)d)', 17 | datefmt="%Y-%m-%dT%H:%M:%S%z", 18 | stream=sys.stdout) 19 | logger = logging.getLogger('babi-dialog') 20 | logger.setLevel(logging.DEBUG) 21 | return logger 22 | 23 | 24 | logger = _setup_logger() 25 | 26 | 27 | def _parse_args(): 28 | parser = argparse.ArgumentParser() 29 | 30 | parser.add_argument('--train', help='Path to train filename') 31 | parser.add_argument('--dev', help='Path to dev filename') 32 | parser.add_argument('--vocab', default='data/vocab.tsv') 33 | parser.add_argument('--candidates', default='data/candidates.tsv') 34 | parser.add_argument('--emb_dim', default=32, type=int) 35 | parser.add_argument('--save_dir') 36 | parser.add_argument('--margin', type=float, default=0.01) 37 | parser.add_argument('--negative_cand', type=int, default=100) 38 | parser.add_argument('--learning_rate', type=float, default=0.01) 39 | 40 | args = parser.parse_args() 41 | 42 | return args 43 | 44 | 45 | def _train(train_tensor, batch_size, neg_size, model, optimizer, sess): 46 | avg_loss = 0 47 | for batch in batch_iter(train_tensor, batch_size, True): 48 | for neg_batch in neg_sampling_iter(train_tensor, batch_size, neg_size): 49 | loss = sess.run( 50 | [model.loss, optimizer], 51 | feed_dict={model.context_batch: batch[:, 0, :], 52 | model.response_batch: batch[:, 1, :], 53 | model.neg_response_batch: neg_batch[:, 1, :]} 54 | ) 55 | avg_loss += loss[0] 56 | avg_loss = avg_loss / (train_tensor.shape[0]*neg_size) 57 | return avg_loss 58 | 59 | 60 | def _forward_all(dev_tensor, model, sess): 61 | avg_dev_loss = 0 62 | for batch in batch_iter(dev_tensor, 256): 63 | for neg_batch in neg_sampling_iter(dev_tensor, 256, 1, 42): 64 | loss = sess.run( 65 | [model.loss], 66 | feed_dict={model.context_batch: batch[:, 0, :], 67 | model.response_batch: batch[:, 1, :], 68 | model.neg_response_batch: neg_batch[:, 1, :]} 69 | ) 70 | avg_dev_loss += loss[0] 71 | avg_dev_loss = avg_dev_loss / (dev_tensor.shape[0]*1) 72 | return avg_dev_loss 73 | 74 | 75 | def main(train_tensor, dev_tensor, candidates_tensor, model, config): 76 | logger.info('Run main with config {}'.format(config)) 77 | 78 | epochs = config['epochs'] 79 | batch_size = config['batch_size'] 80 | negative_cand = config['negative_cand'] 81 | save_dir = config['save_dir'] 82 | 83 | # TODO: Add LR decay 84 | optimizer = tf.train.AdamOptimizer(config['lr']).minimize(model.loss) 85 | 86 | prev_best_accuracy = 0 87 | 88 | saver = tf.train.Saver() 89 | config = tf.ConfigProto() 90 | config.gpu_options.allow_growth = True 91 | with tf.Session(config=config) as sess: 92 | sess.run(tf.global_variables_initializer()) 93 | 94 | for epoch in range(epochs): 95 | avg_loss = _train(train_tensor, batch_size, negative_cand, model, optimizer, sess) 96 | # TODO: Refine dev loss calculation 97 | avg_dev_loss = _forward_all(dev_tensor, model, sess) 98 | logger.info('Epoch: {}; Train loss: {}; Dev loss: {};'.format(epoch, avg_loss, avg_dev_loss)) 99 | 100 | if epoch % 2 == 0: 101 | dev_eval = evaluate(dev_tensor, candidates_tensor, sess, model) 102 | logger.info('Evaluation: {}'.format(dev_eval)) 103 | accuracy = dev_eval[2] 104 | if accuracy >= prev_best_accuracy: 105 | logger.debug('Saving checkpoint') 106 | prev_best_accuracy = accuracy 107 | saver.save(sess, save_dir) 108 | 109 | 110 | if __name__ == '__main__': 111 | args = _parse_args() 112 | vocab = load_vocab(args.vocab) 113 | train_tensor = make_tensor(args.train, vocab) 114 | dev_tensor = make_tensor(args.dev, vocab) 115 | candidates_tensor = make_tensor(args.candidates, vocab) 116 | config = {'batch_size': 32, 'epochs': 400, 117 | 'negative_cand': args.negative_cand, 'save_dir': args.save_dir, 118 | 'lr': args.learning_rate} 119 | model = Model(len(vocab), emb_dim=args.emb_dim, margin=args.margin) 120 | main(train_tensor, dev_tensor, candidates_tensor, model, config) 121 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------