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kgcvae_swda.py
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kgcvae_swda.py
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# Copyright (C) 2017 Tiancheng Zhao, Carnegie Mellon University
import os
import time
import numpy as np
import tensorflow as tf
from beeprint import pp
from config_utils import KgCVAEConfig as Config
from data_apis.corpus import SWDADialogCorpus
from data_apis.data_utils import SWDADataLoader
from models.cvae import KgRnnCVAE
# constants
tf.app.flags.DEFINE_string("word2vec_path", None, "The path to word2vec. Can be None.")
tf.app.flags.DEFINE_string("data_dir", "data/", "Raw data directory.")
tf.app.flags.DEFINE_string("work_dir", "working", "Experiment results directory.")
tf.app.flags.DEFINE_bool("equal_batch", True, "Make each batch has similar length.")
tf.app.flags.DEFINE_bool("resume", False, "Resume from previous")
tf.app.flags.DEFINE_bool("forward_only", False, "Only do decoding")
tf.app.flags.DEFINE_bool("save_model", True, "Create checkpoints")
tf.app.flags.DEFINE_string("test_path", "run1557988278", "the dir to load checkpoint for forward only")
FLAGS = tf.app.flags.FLAGS
def main():
# config for training
config = Config()
# config for validation
valid_config = Config()
valid_config.keep_prob = 1.0
valid_config.dec_keep_prob = 1.0
valid_config.batch_size = 60
# configuration for testing
test_config = Config()
test_config.keep_prob = 1.0
test_config.dec_keep_prob = 1.0
test_config.batch_size = 1
# pp(config)
# get data set
api = SWDADialogCorpus(FLAGS.data_dir, word2vec=FLAGS.word2vec_path, word2vec_dim=config.embed_size)
dial_corpus = api.get_dialog_corpus()
train_source_dial, train_target_dial = dial_corpus.get("train_source"), dial_corpus.get("train_target")
valid_source_dial, valid_target_dial = dial_corpus.get("valid_source"), dial_corpus.get("valid_target")
test_source_dial, test_target_dial = dial_corpus.get("test_source"), dial_corpus.get("test_target")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_source_dial, train_target_dial, config)
valid_feed = SWDADataLoader("Valid", valid_source_dial, valid_target_dial, config)
test_feed = SWDADataLoader("Test", test_source_dial, test_target_dial, config)
if FLAGS.forward_only or FLAGS.resume:
log_dir = os.path.join(FLAGS.work_dir, FLAGS.test_path)
else:
log_dir = os.path.join(FLAGS.work_dir, "run"+str(int(time.time())))
# begin training
with tf.Session() as sess:
initializer = tf.random_uniform_initializer(-1.0 * config.init_w, config.init_w)
scope = "model"
with tf.variable_scope(scope, reuse=None, initializer=initializer):
model = KgRnnCVAE(sess, config, api, log_dir=None if FLAGS.forward_only else log_dir, forward=False, scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
valid_model = KgRnnCVAE(sess, valid_config, api, log_dir=None, forward=False, scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
test_model = KgRnnCVAE(sess, test_config, api, log_dir=None, forward=True, scope=scope)
print("Created computation graphs")
if api.word2vec is not None and not FLAGS.forward_only:
print("Loaded word2vec")
sess.run(model.embedding.assign(np.array(api.word2vec)))
# write config to a file for logging
if not FLAGS.forward_only:
with open(os.path.join(log_dir, "run.log"), "wb") as f:
f.write(pp(config, output=False))
# create a folder by force
ckp_dir = os.path.join(log_dir, "checkpoints")
if not os.path.exists(ckp_dir):
os.mkdir(ckp_dir)
ckpt = tf.train.get_checkpoint_state(ckp_dir)
print("Created models with fresh parameters.")
sess.run(tf.global_variables_initializer())
if ckpt:
print("Reading dm models parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
if not FLAGS.forward_only:
dm_checkpoint_path = os.path.join(ckp_dir, model.__class__.__name__+ ".ckpt")
global_t = 1
patience = 10 # wait for at least 10 epoch before stop
dev_loss_threshold = np.inf
best_dev_loss = np.inf
for epoch in range(config.max_epoch):
print(">> Epoch %d with lr %f" % (epoch, model.learning_rate.eval()))
# begin training
if train_feed.num_batch is None or train_feed.ptr >= train_feed.num_batch:
train_feed.epoch_init(config.batch_size, config.backward_size,
config.step_size, shuffle=True)
global_t, train_loss = model.train(global_t, sess, train_feed, update_limit=config.update_limit)
# begin validation
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
valid_loss = valid_model.valid("ELBO_VALID", sess, valid_feed)
test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=True, intra_shuffle=False)
test_model.test(sess, test_feed, num_batch=5)
done_epoch = epoch + 1
# only save a models if the dev loss is smaller
# Decrease learning rate if no improvement was seen over last 3 times.
if config.op == "sgd" and done_epoch > config.lr_hold:
sess.run(model.learning_rate_decay_op)
if valid_loss < best_dev_loss:
if valid_loss <= dev_loss_threshold * config.improve_threshold:
patience = max(patience, done_epoch * config.patient_increase)
dev_loss_threshold = valid_loss
# still save the best train model
if FLAGS.save_model:
print("Save model!!")
model.saver.save(sess, dm_checkpoint_path, global_step=epoch)
best_dev_loss = valid_loss
if config.early_stop and patience <= done_epoch:
print("!!Early stop due to run out of patience!!")
break
print("Best validation loss %f" % best_dev_loss)
print("Done training")
else:
# begin validation
# begin validation
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
valid_model.valid("ELBO_VALID", sess, valid_feed)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
valid_model.valid("ELBO_TEST", sess, test_feed)
dest_f = open(os.path.join(log_dir, "test.txt"), "wb")
test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=False, intra_shuffle=False)
test_model.test(sess, test_feed, num_batch=None, repeat=10, dest=dest_f)
dest_f.close()
if __name__ == "__main__":
if FLAGS.forward_only:
if FLAGS.test_path is None:
print("Set test_path before forward only")
exit(1)
main()