def main():
# config for training
config = Config()
config.use_bow = False
# config for validation
valid_config = Config()
valid_config.keep_prob = 1.0
valid_config.dec_keep_prob = 1.0
valid_config.batch_size = 60
valid_config.use_bow = False
# configuration for testing
test_config = Config()
test_config.keep_prob = 1.0
test_config.dec_keep_prob = 1.0
test_config.batch_size = 1
test_config.use_bow = False
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()
meta_corpus = api.get_meta_corpus()
train_meta, valid_meta, test_meta = meta_corpus.get(
"train"), meta_corpus.get("valid"), meta_corpus.get("test")
train_dial, valid_dial, test_dial = dial_corpus.get(
"train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, config)
# begin training
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
# sess_config.gpu_options.allow_growth = True
sess_config.gpu_options.per_process_gpu_memory_fraction = 0.45
with tf.Session(config=sess_config) 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)
test_model.prepare_mul_ref()
logger.info("Created computation graphs")
if api.word2vec is not None and not FLAGS.forward_only:
logger.info("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)
logger.info("Created models with fresh parameters.")
sess.run(tf.global_variables_initializer())
if ckpt:
logger.info("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):
logger.info(">> Epoch %d with lr %f" %
(epoch,
sess.run(model.learning_rate_cyc,
{model.global_t: global_t})))
# 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
logger.record_tabular("Epoch", epoch)
logger.record_tabular("Mode", "Val")
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)
logger.record_tabular("Epoch", epoch)
logger.record_tabular("Mode", "Test")
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)
# test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
# test_config.step_size, shuffle=True, intra_shuffle=False)
# test_model.test_mul_ref(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:
logger.info("Save model!!")
model.saver.save(sess, dm_checkpoint_path)
best_dev_loss = valid_loss
if (epoch % 3) == 2:
tmp_model_path = os.path.join(
ckp_dir,
model.__class__.__name__ + str(epoch) + ".ckpt")
model.saver.save(sess, tmp_model_path)
if config.early_stop and patience <= done_epoch:
logger.info("!!Early stop due to run out of patience!!")
break
logger.info("Best validation loss %f" % best_dev_loss)
logger.info("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_mul_ref(sess,
test_feed,
num_batch=None,
repeat=5,
dest=dest_f)
dest_f.close()
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()
meta_corpus = api.get_meta_corpus()
train_meta, valid_meta, test_meta = meta_corpus.get(
"train"), meta_corpus.get("valid"), meta_corpus.get("test")
train_dial, valid_dial, test_dial = dial_corpus.get(
"train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, 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
if True:
scope = "model"
model = KgRnnCVAE(config,
api,
log_dir=None if FLAGS.forward_only else log_dir,
scope=scope)
print("Created computation graphs")
# 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 = get_checkpoint_state(ckp_dir)
print("Created models with fresh parameters.")
model.apply(lambda m: [
torch.nn.init.uniform(p.data, -1.0 * config.init_w, config.init_w)
for p in m.parameters()
])
# Load word2vec weight
if api.word2vec is not None and not FLAGS.forward_only:
print("Loaded word2vec")
model.embedding.weight.data.copy_(
torch.from_numpy(np.array(api.word2vec)))
model.embedding.weight.data[0].fill_(0)
if ckpt:
print("Reading dm models parameters from %s" % ckpt)
model.load_state_dict(torch.load(ckpt))
#
# turn to cuda
model.cuda()
if not FLAGS.forward_only:
dm_checkpoint_path = os.path.join(
ckp_dir, model.__class__.__name__ + "-%d.pth")
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))
# 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_model(
global_t, 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)
model.eval()
valid_loss = model.valid_model("ELBO_VALID", valid_feed)
test_feed.epoch_init(test_config.batch_size,
test_config.backward_size,
test_config.step_size,
shuffle=True,
intra_shuffle=False)
model.test_model(test_feed, num_batch=5)
model.train()
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:
model.learning_rate_decay()
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!!")
torch.save(model.state_dict(),
dm_checkpoint_path % (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)
model.eval()
model.valid_model("ELBO_VALID", valid_feed)
test_feed.epoch_init(valid_config.batch_size,
valid_config.backward_size,
valid_config.step_size,
shuffle=False,
intra_shuffle=False)
model.valid_model("ELBO_TEST", 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)
model.test_model(test_feed, num_batch=None, repeat=10, dest=dest_f)
model.train()
dest_f.close()
def main():
## random seeds
seed = FLAGS.seed
# tf.random.set_seed(seed)
np.random.seed(seed)
## config for training
config = Config()
pid = PIDControl(FLAGS.exp_KL)
# 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()
meta_corpus = api.get_meta_corpus()
train_meta, valid_meta, test_meta = meta_corpus.get("train"), meta_corpus.get("valid"), meta_corpus.get("test")
train_dial, valid_dial, test_dial = dial_corpus.get("train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, config)
if FLAGS.forward_only or FLAGS.resume:
# log_dir = os.path.join(FLAGS.work_dir, FLAGS.test_path)
log_dir = os.path.join(FLAGS.work_dir, FLAGS.model_name)
else:
log_dir = os.path.join(FLAGS.work_dir, FLAGS.model_name)
## 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, pid_control=pid, scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
valid_model = KgRnnCVAE(sess, valid_config, api, log_dir=None, forward=False, pid_control=pid, scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
test_model = KgRnnCVAE(sess, test_config, api, log_dir=None, forward=True, pid_control=pid, 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, "configure.log"), "wb") as f:
f.write(pp(config, output=False))
# create a folder by force
ckp_dir = os.path.join(log_dir, "checkpoints")
print("*******checkpoint path: ", ckp_dir)
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)
### save log when running
if not FLAGS.forward_only:
logfileName = "train.log"
else:
logfileName = "test.log"
fw_log = open(os.path.join(log_dir, logfileName), "w")
print("log directory >>> : ", os.path.join(log_dir, "run.log"))
if not FLAGS.forward_only:
print('--start training now---')
dm_checkpoint_path = os.path.join(ckp_dir, model.__class__.__name__+ ".ckpt")
global_t = 1
patience = 20 # wait for at least 10 epoch before stop
dev_loss_threshold = np.inf
best_dev_loss = np.inf
pbar = tqdm(total = config.max_epoch)
## epoch start training
for epoch in range(config.max_epoch):
pbar.update(1)
print(">> Epoch %d with lr %f" % (epoch, model.learning_rate.eval()))
## begin training
FLAGS.mode = 'train'
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)
FLAGS.mode = 'valid'
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
elbo, nll, ppl, au_count, kl_loss = valid_model.valid("ELBO_TEST", sess, valid_feed, test_feed)
print('middle test nll: {} ppl: {} ActiveUnit: {} kl_loss:{}\n'.format(nll, ppl,au_count,kl_loss))
fw_log.write('epoch:{} testing nll:{} ppl:{} ActiveUnit:{} kl_loss:{} elbo:{}\n'.\
format(epoch, nll, ppl, au_count, kl_loss, elbo))
fw_log.flush()
'''
## begin validation
FLAGS.mode = 'valid'
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 model
FLAGS.mode = 'test'
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 and save checkpoint")
if FLAGS.save_model:
print("Save model!!")
model.saver.save(sess, dm_checkpoint_path, global_step=epoch)
# begin validation
print('--------after training to testing now-----')
FLAGS.mode = 'test'
# 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)
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
elbo, nll, ppl, au_count,kl_loss = valid_model.valid("ELBO_TEST", sess, valid_feed, test_feed)
print('final test nll: {} ppl: {} ActiveUnit: {} kl_loss:{}\n'.format(nll, ppl,au_count,kl_loss))
fw_log.write('Final testing nll:{} ppl:{} ActiveUnit:{} kl_loss:{} elbo:{}\n'.\
format(nll, ppl, au_count, kl_loss, elbo))
dest_f = open(os.path.join(log_dir, FLAGS.test_res), "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()
print("****testing done****")
else:
# begin validation
# begin validation
print('*'*89)
print('--------testing now-----')
print('*'*89)
FLAGS.mode = 'test'
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)
elbo, nll, ppl, au_count, kl_loss = valid_model.valid("ELBO_TEST", sess, valid_feed, test_feed)
print('final test nll: {} ppl: {} ActiveUnit: {} kl_loss:{}\n'.format(nll, ppl,au_count,kl_loss))
fw_log.write('Final testing nll:{} ppl:{} ActiveUnit:{} kl_loss:{} elbo:{}\n'.\
format(nll, ppl, au_count, kl_loss, elbo))
# dest_f = open(os.path.join(log_dir, FLAGS.test_res), "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()
print("****testing done****")
fw_log.close()
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()
meta_corpus = api.get_meta_corpus()
'''item in meta like:
([0.46, 0.6666666666666666, 1, 0], [0.48, 0.6666666666666666, 1, 0], 29)
'''
train_meta, valid_meta, test_meta = meta_corpus.get("train"), meta_corpus.get("valid"), meta_corpus.get("test")
'''item in dial
([utt_id,], spearker_id, [dialogact_id, [,,,]])
'''
train_dial, valid_dial, test_dial = dial_corpus.get("train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, 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")
elif not FLAGS.demo:
# 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()
else:
infer_api = Corpus(FLAGS.vocab, FLAGS.da_vocab, FLAGS.topic_vocab)
while(1):
demo_sent = raw_input('Please input your sentence:\t')
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
speaker = raw_input('Are you speaker A?: 1 or 0\t')
topic = raw_input('what is your topic:\t')
# demo_sent = 'Hello'
# speaker = '1'
# topic = 'MUSIC'
meta, dial = infer_api.format_input(demo_sent, speaker, topic)
infer_feed = SWDADataLoader("Infer", dial, meta, config)
infer_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=False, intra_shuffle=False)
pred_strs, pred_das = test_model.inference(sess, infer_feed, num_batch=None, repeat=3)
if pred_strs and pred_das:
print('Here is your answer')
for da, answer in zip(pred_strs, pred_das):
print('{} >> {}'.format(da, answer))
def main(model_type):
# 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)
# which model to run
if model_type == "kgcvae":
model_class = KgRnnCVAE
backward_size = config.backward_size
config.use_hcf, valid_config.use_hcf, test_config.use_hcf = True
elif model_type == "cvae":
model_class = KgRnnCVAE
backward_size = config.backward_size
config.use_hcf, valid_config.use_hcf, test_config.use_hcf = False
elif model_type == 'hierbaseline':
model_class = HierBaseline
backward_size = config.backward_size
else:
raise ValueError("This shouldn't happen.")
# LDA Model
ldamodel = LDAModel(config,
trained_model_path=FLAGS.lda_model_path,
id2word_path=FLAGS.id2word_path)
# get data set
api = SWDADialogCorpus(FLAGS.data_dir,
word2vec=FLAGS.word2vec_path,
word2vec_dim=config.embed_size,
vocab_dict_path=FLAGS.vocab_dict_path,
lda_model=ldamodel,
imdb=FLAGS.use_imdb)
dial_corpus = api.get_dialog_corpus()
meta_corpus = api.get_meta_corpus()
train_meta, valid_meta, test_meta = meta_corpus.get(
"train"), meta_corpus.get("valid"), meta_corpus.get("test")
train_dial, valid_dial, test_dial = dial_corpus.get(
"train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, config)
# if you're testing an existing implementation or resuming training
if FLAGS.forward_only or FLAGS.resume:
log_dir = os.path.join(FLAGS.work_dir + "_" + FLAGS.model_type,
FLAGS.test_path)
else:
log_dir = os.path.join(FLAGS.work_dir + "_" + FLAGS.model_type,
"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 = model_class(
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 = model_class(sess,
valid_config,
api,
log_dir=None,
forward=False,
scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
test_model = model_class(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 you're training a model
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
# train for a max of max_epoch's. saves the model after the epoch if it's some amount better than current best
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,
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 and testing
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=1
) #TODO change this batch size back to a reasonably large number
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 True: #valid_loss < best_dev_loss: # TODO this change makes the model always save. Change this back when corpus not trivial
if True: #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 if you're just testing an existing model
else:
# 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)
# begin testing
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()
