def main(_):
model_type=FLAGS.model_type
# chkpt_path = FLAGS.model_dir+'saved/qgen-maluuba-crop-glove-smart'
# chkpt_path = FLAGS.model_dir+'qgen-saved/MALUUBA-CROP-LATENT/1533247183'
disc_path = FLAGS.model_dir+'saved/discriminator-trained-latent'
chkpt_path = FLAGS.model_dir+'qgen/'+ model_type+'/'+FLAGS.eval_model_id
# load dataset
# train_data = loader.load_squad_triples(FLAGS.data_path, False)
dev_data = loader.load_squad_triples(FLAGS.data_path, dev=FLAGS.eval_on_dev, test=FLAGS.eval_on_test)
if len(dev_data) < FLAGS.num_eval_samples:
exit('***ERROR*** Eval dataset is smaller than the num_eval_samples flag!')
if len(dev_data) > FLAGS.num_eval_samples:
print('***WARNING*** Eval dataset is larger than the num_eval_samples flag!')
# train_contexts_unfilt, _,_,train_a_pos_unfilt = zip(*train_data)
dev_contexts_unfilt, _,_,dev_a_pos_unfilt = zip(*dev_data)
if FLAGS.filter_window_size_before >-1:
# train_data = preprocessing.filter_squad(train_data, window_size=FLAGS.filter_window_size, max_tokens=FLAGS.filter_max_tokens)
dev_data = preprocessing.filter_squad(dev_data, window_size_before=FLAGS.filter_window_size_before, window_size_after=FLAGS.filter_window_size_after, max_tokens=FLAGS.filter_max_tokens)
# print('Loaded SQuAD with ',len(train_data),' triples')
print('Loaded SQuAD dev set with ',len(dev_data),' triples')
# train_contexts, train_qs, train_as,train_a_pos = zip(*train_data)
dev_contexts, dev_qs, dev_as, dev_a_pos = zip(*dev_data)
# vocab = loader.get_vocab(train_contexts, tf.app.flags.FLAGS.vocab_size)
with open(chkpt_path+'/vocab.json') as f:
vocab = json.load(f)
with SquadStreamer(vocab, FLAGS.eval_batch_size, 1, shuffle=False) as dev_data_source:
glove_embeddings = loader.load_glove(FLAGS.data_path)
# Create model
if model_type[:7] == "SEQ2SEQ":
model = Seq2SeqModel(vocab, training_mode=False)
elif model_type[:2] == "RL":
# TEMP - no need to spin up the LM or QA model at eval time
FLAGS.qa_weight = 0
FLAGS.lm_weight = 0
model = RLModel(vocab, training_mode=False)
else:
exit("Unrecognised model type: "+model_type)
with model.graph.as_default():
saver = tf.train.Saver()
if FLAGS.eval_metrics:
lm = LstmLmInstance()
# qa = MpcmQaInstance()
qa = QANetInstance()
lm.load_from_chkpt(FLAGS.model_dir+'saved/lmtest')
# qa.load_from_chkpt(FLAGS.model_dir+'saved/qatest')
qa.load_from_chkpt(FLAGS.model_dir+'saved/qanet2')
discriminator = DiscriminatorInstance(trainable=False, path=disc_path)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_limit)
with tf.Session(graph=model.graph, config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
if not os.path.exists(chkpt_path):
exit('Checkpoint path doesnt exist! '+chkpt_path)
# summary_writer = tf.summary.FileWriter(FLAGS.log_directory+"eval/"+str(int(time.time())), sess.graph)
saver.restore(sess, tf.train.latest_checkpoint(chkpt_path))
# print('Loading not implemented yet')
# else:
# sess.run(tf.global_variables_initializer())
# sess.run(model.glove_init_ops)
num_steps = FLAGS.num_eval_samples//FLAGS.eval_batch_size
# Initialise the dataset
# np.random.shuffle(dev_data)
dev_data_source.initialise(dev_data)
f1s=[]
bleus=[]
qa_scores=[]
qa_scores_gold=[]
lm_scores=[]
nlls=[]
disc_scores=[]
sowe_similarities=[]
copy_probs=[]
qgolds=[]
qpreds=[]
qpred_ids=[]
qgold_ids=[]
ctxts=[]
answers=[]
ans_positions=[]
metric_individuals=[]
res=[]
for e in range(1):
for i in tqdm(range(num_steps), desc='Epoch '+str(e)):
dev_batch, curr_batch_size = dev_data_source.get_batch()
pred_batch,pred_beam,pred_beam_lens,pred_ids,pred_lens,gold_batch, gold_lens,gold_ids,ctxt,ctxt_len,ans,ans_len,nll,copy_prob= sess.run([model.q_hat_beam_string, model.q_hat_full_beam_str, model.q_hat_full_beam_lens,model.q_hat_beam_ids,model.q_hat_beam_lens,model.question_raw, model.question_length, model.question_ids, model.context_raw, model.context_length, model.answer_locs, model.answer_length, model.nll, model.mean_copy_prob], feed_dict={model.input_batch: dev_batch ,model.is_training:False})
unfilt_ctxt_batch = [dev_contexts_unfilt[ix] for ix in dev_batch[3]]
a_text_batch = ops.byte_token_array_to_str(dev_batch[2][0], dev_batch[2][2], is_array=False)
unfilt_apos_batch = [dev_a_pos_unfilt[ix] for ix in dev_batch[3]]
# subtract 1 to remove the "end sent token"
pred_q_batch = [q.replace(' </Sent>',"").replace(" <PAD>","") for q in ops.byte_token_array_to_str(pred_batch, pred_lens-1)]
ctxts.extend(unfilt_ctxt_batch)
answers.extend(a_text_batch)
ans_positions.extend([dev_a_pos_unfilt[ix] for ix in dev_batch[3]])
copy_probs.extend(copy_prob.tolist())
# get QA score
# gold_str=[]
# pred_str=[]
gold_ans = ops.byte_token_array_to_str(dev_batch[2][0], dev_batch[2][2], is_array=False)
# pred_str = ops.byte_token_array_to_str([dev_batch[0][0][b][qa_pred[b][0]:qa_pred[b][1]] for b in range(curr_batch_size)], is_array=False)
nlls.extend(nll.tolist())
if FLAGS.eval_metrics:
qa_pred = qa.get_ans(unfilt_ctxt_batch, ops.byte_token_array_to_str(pred_batch, pred_lens))
gold_qa_pred = qa.get_ans(unfilt_ctxt_batch, ops.byte_token_array_to_str(dev_batch[1][0], dev_batch[1][3]))
qa_score_batch = [metrics.f1(metrics.normalize_answer(gold_ans[b]), metrics.normalize_answer(qa_pred[b])) for b in range(curr_batch_size)]
qa_score_gold_batch = [metrics.f1(metrics.normalize_answer(gold_ans[b]), metrics.normalize_answer(gold_qa_pred[b])) for b in range(curr_batch_size)]
lm_score_batch = lm.get_seq_perplexity(pred_q_batch).tolist()
disc_score_batch = discriminator.get_pred(unfilt_ctxt_batch, pred_q_batch, gold_ans, unfilt_apos_batch).tolist()
for b, pred in enumerate(pred_batch):
pred_str = pred_q_batch[b].replace(' </Sent>',"").replace(" <PAD>","")
gold_str = tokens_to_string(gold_batch[b][:gold_lens[b]-1])
f1s.append(metrics.f1(gold_str, pred_str))
bleus.append(metrics.bleu(gold_str, pred_str))
qgolds.append(gold_str)
qpreds.append(pred_str)
# calc cosine similarity between sums of word embeddings
pred_sowe = np.sum(np.asarray([glove_embeddings[w] if w in glove_embeddings.keys() else np.zeros((FLAGS.embedding_size,)) for w in preprocessing.tokenise(pred_str ,asbytes=False)]) ,axis=0)
gold_sowe = np.sum(np.asarray([glove_embeddings[w] if w in glove_embeddings.keys() else np.zeros((FLAGS.embedding_size,)) for w in preprocessing.tokenise(gold_str ,asbytes=False)]) ,axis=0)
this_similarity = np.inner(pred_sowe, gold_sowe)/np.linalg.norm(pred_sowe, ord=2)/np.linalg.norm(gold_sowe, ord=2)
sowe_similarities.append(this_similarity)
this_metric_dict={
'f1':f1s[-1],
'bleu': bleus[-1],
'nll': nlls[-1],
'sowe': sowe_similarities[-1]
}
if FLAGS.eval_metrics:
this_metric_dict={
**this_metric_dict,
'qa': qa_score_batch[b],
'lm': lm_score_batch[b],
'disc': disc_score_batch[b]}
qa_scores.extend(qa_score_batch)
lm_scores.extend(lm_score_batch)
disc_scores.extend(disc_score_batch)
metric_individuals.append(this_metric_dict)
res.append({
'c':unfilt_ctxt_batch[b],
'q_pred': pred_str,
'q_gold': gold_str,
'a_pos': unfilt_apos_batch[b],
'a_text': a_text_batch[b],
'metrics': this_metric_dict,
'q_pred_ids': pred_ids.tolist()[b],
'q_gold_ids': dev_batch[1][1][b].tolist()
})
# Quick output
if i==0:
# print(copy_prob.tolist())
# print(copy_probs)
pred_str = tokens_to_string(pred_batch[0][:pred_lens[0]-1])
gold_str = tokens_to_string(gold_batch[0][:gold_lens[0]-1])
# print(pred_str)
print(qpreds[0])
print(gold_str)
title=chkpt_path
out_str = output_eval(title,pred_batch, pred_ids, pred_lens, gold_batch, gold_lens, ctxt, ctxt_len, ans, ans_len)
with open(FLAGS.log_directory+'out_eval_'+model_type+'.htm', 'w', encoding='utf-8') as fp:
fp.write(out_str)
# res = list(zip(qpreds,qgolds,ctxts,answers,ans_positions,metric_individuals))
metric_dict={
'f1':np.mean(f1s),
'bleu': metrics.bleu_corpus(qgolds, qpreds),
'nll':np.mean(nlls),
'sowe': np.mean(sowe_similarities)
}
if FLAGS.eval_metrics:
metric_dict={**metric_dict,
'qa':np.mean(qa_scores),
'lm':np.mean(lm_scores),
'disc': np.mean(disc_scores)}
# print(res)
with open(FLAGS.log_directory+'out_eval_'+model_type+("_test" if FLAGS.eval_on_test else "")+("_train" if (not FLAGS.eval_on_dev and not FLAGS.eval_on_test) else "")+'.json', 'w', encoding='utf-8') as fp:
json.dump({"metrics":metric_dict, "results": res}, fp)
print("F1: ", np.mean(f1s))
print("BLEU: ", metrics.bleu_corpus(qgolds, qpreds))
print("NLL: ", np.mean(nlls))
print("SOWE: ", np.mean(sowe_similarities))
print("Copy prob: ", np.mean(copy_probs))
if FLAGS.eval_metrics:
print("QA: ", np.mean(qa_scores))
print("LM: ", np.mean(lm_scores))
print("Disc: ", np.mean(disc_scores))
def main(_):
if FLAGS.testing:
print('TEST MODE - reducing model size')
FLAGS.context_encoder_units = 100
FLAGS.answer_encoder_units = 100
FLAGS.decoder_units = 100
FLAGS.batch_size = 8
FLAGS.eval_batch_size = 8
# FLAGS.embedding_size=50
run_id = str(int(time.time()))
chkpt_path = FLAGS.model_dir + 'qgen/' + FLAGS.model_type + '/' + run_id
restore_path = FLAGS.model_dir + 'qgen/' + FLAGS.restore_path if FLAGS.restore_path is not None else None #'MALUUBA-CROP-LATENT'+'/'+'1534123959'
# restore_path=FLAGS.model_dir+'saved/qgen-maluuba-crop-glove-smart'
disc_path = FLAGS.model_dir + 'saved/discriminator-trained-latent'
print("Run ID is ", run_id)
print("Model type is ", FLAGS.model_type)
if not os.path.exists(chkpt_path):
os.makedirs(chkpt_path)
# load dataset
train_data = loader.load_squad_triples(FLAGS.data_path, False)
dev_data = loader.load_squad_triples(FLAGS.data_path, True)
train_contexts_unfilt, _, ans_text_unfilt, ans_pos_unfilt = zip(
*train_data)
dev_contexts_unfilt, _, dev_ans_text_unfilt, dev_ans_pos_unfilt = zip(
*dev_data)
if FLAGS.testing:
train_data = train_data[:1000]
num_dev_samples = 100
else:
num_dev_samples = FLAGS.num_dev_samples
if FLAGS.filter_window_size_before > -1:
train_data = preprocessing.filter_squad(
train_data,
window_size_before=FLAGS.filter_window_size_before,
window_size_after=FLAGS.filter_window_size_after,
max_tokens=FLAGS.filter_max_tokens)
dev_data = preprocessing.filter_squad(
dev_data,
window_size_before=FLAGS.filter_window_size_before,
window_size_after=FLAGS.filter_window_size_after,
max_tokens=FLAGS.filter_max_tokens)
print('Loaded SQuAD with ', len(train_data), ' triples')
train_contexts, train_qs, train_as, train_a_pos = zip(*train_data)
if FLAGS.restore:
if restore_path is None:
exit('You need to specify a restore path!')
with open(restore_path + '/vocab.json', encoding="utf-8") as f:
vocab = json.load(f)
elif FLAGS.glove_vocab:
vocab = loader.get_glove_vocab(FLAGS.data_path,
size=FLAGS.vocab_size,
d=FLAGS.embedding_size)
with open(chkpt_path + '/vocab.json', 'w',
encoding="utf-8") as outfile:
json.dump(vocab, outfile)
else:
vocab = loader.get_vocab(train_contexts + train_qs, FLAGS.vocab_size)
with open(chkpt_path + '/vocab.json', 'w',
encoding="utf-8") as outfile:
json.dump(vocab, outfile)
# Create model
if FLAGS.model_type[:7] == "SEQ2SEQ":
model = Seq2SeqModel(vocab,
training_mode=True,
use_embedding_loss=FLAGS.embedding_loss)
elif FLAGS.model_type[:7] == "MALUUBA":
# TEMP
if not FLAGS.policy_gradient:
FLAGS.qa_weight = 0
FLAGS.lm_weight = 0
model = MaluubaModel(vocab,
training_mode=True,
use_embedding_loss=FLAGS.embedding_loss)
# if FLAGS.model_type[:10] == "MALUUBA_RL":
# qa_vocab=model.qa.vocab
# lm_vocab=model.lm.vocab
if FLAGS.policy_gradient:
discriminator = DiscriminatorInstance(trainable=FLAGS.disc_train,
path=disc_path)
else:
exit("Unrecognised model type: " + FLAGS.model_type)
# create data streamer
with SquadStreamer(vocab, FLAGS.batch_size, FLAGS.num_epochs,
shuffle=True) as train_data_source, SquadStreamer(
vocab, FLAGS.eval_batch_size, 1,
shuffle=True) as dev_data_source:
with model.graph.as_default():
saver = tf.train.Saver(max_to_keep=1, save_relative_paths=True)
# change visible devices if using RL models
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_limit,
visible_device_list='0',
allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=False),
graph=model.graph) as sess:
summary_writer = tf.summary.FileWriter(
FLAGS.log_dir + 'qgen/' + FLAGS.model_type + '/' + run_id,
sess.graph)
train_data_source.initialise(train_data)
num_steps_train = len(train_data) // FLAGS.batch_size
num_steps_dev = num_dev_samples // FLAGS.eval_batch_size
if FLAGS.restore:
saver.restore(sess, tf.train.latest_checkpoint(restore_path))
start_e = 15 #FLAGS.num_epochs
print('Loaded model')
else:
start_e = 0
sess.run(tf.global_variables_initializer())
# sess.run(model.glove_init_ops)
f1summary = tf.Summary(value=[
tf.Summary.Value(tag="dev_perf/f1", simple_value=0.0)
])
bleusummary = tf.Summary(value=[
tf.Summary.Value(tag="dev_perf/bleu", simple_value=0.0)
])
summary_writer.add_summary(f1summary, global_step=0)
summary_writer.add_summary(bleusummary, global_step=0)
# Initialise the dataset
# sess.run(model.iterator.initializer, feed_dict={model.context_ph: train_contexts,
# model.qs_ph: train_qs, model.as_ph: train_as, model.a_pos_ph: train_a_pos})
best_oos_nll = 1e6
lm_score_moments = online_moments.OnlineMoment()
qa_score_moments = online_moments.OnlineMoment()
disc_score_moments = online_moments.OnlineMoment()
# for e in range(start_e,start_e+FLAGS.num_epochs):
# Train for one epoch
for i in tqdm(range(num_steps_train * FLAGS.num_epochs),
desc='Training'):
# Get a batch
train_batch, curr_batch_size = train_data_source.get_batch()
# Are we doing policy gradient? Do a forward pass first, then build the PG batch and do an update step
if FLAGS.model_type[:
10] == "MALUUBA_RL" and FLAGS.policy_gradient:
# do a fwd pass first, get the score, then do another pass and optimize
qhat_str, qhat_ids, qhat_lens = sess.run(
[
model.q_hat_beam_string, model.q_hat_beam_ids,
model.q_hat_beam_lens
],
feed_dict={
model.input_batch: train_batch,
model.is_training: FLAGS.pg_dropout,
model.hide_answer_in_copy: True
})
# The output is as long as the max allowed len - remove the pointless extra padding
qhat_ids = qhat_ids[:, :np.max(qhat_lens)]
qhat_str = qhat_str[:, :np.max(qhat_lens)]
pred_str = byte_token_array_to_str(qhat_str, qhat_lens - 1)
gold_q_str = byte_token_array_to_str(
train_batch[1][0], train_batch[1][3])
# Get reward values
lm_score = (-1 * model.lm.get_seq_perplexity(pred_str)
).tolist() # lower perplexity is better
# retrieve the uncropped context for QA evaluation
unfilt_ctxt_batch = [
train_contexts_unfilt[ix] for ix in train_batch[3]
]
ans_text_batch = [
ans_text_unfilt[ix] for ix in train_batch[3]
]
ans_pos_batch = [
ans_pos_unfilt[ix] for ix in train_batch[3]
]
qa_pred = model.qa.get_ans(unfilt_ctxt_batch, pred_str)
qa_pred_gold = model.qa.get_ans(unfilt_ctxt_batch,
gold_q_str)
# gold_str=[]
# pred_str=[]
qa_f1s = []
gold_ans_str = byte_token_array_to_str(train_batch[2][0],
train_batch[2][2],
is_array=False)
qa_f1s.extend([
metrics.f1(metrics.normalize_answer(gold_ans_str[b]),
metrics.normalize_answer(qa_pred[b]))
for b in range(curr_batch_size)
])
disc_scores = discriminator.get_pred(
unfilt_ctxt_batch, pred_str, ans_text_batch,
ans_pos_batch)
if i > FLAGS.pg_burnin // 2:
lm_score_moments.push(lm_score)
qa_score_moments.push(qa_f1s)
disc_score_moments.push(disc_scores)
# print(disc_scores)
# print((e-start_e)*num_steps_train+i, flags.pg_burnin)
if i > FLAGS.pg_burnin:
# A variant of popart
qa_score_whitened = (
qa_f1s - qa_score_moments.mean
) / np.sqrt(qa_score_moments.variance + 1e-6)
lm_score_whitened = (
lm_score - lm_score_moments.mean
) / np.sqrt(lm_score_moments.variance + 1e-6)
disc_score_whitened = (
disc_scores - disc_score_moments.mean
) / np.sqrt(disc_score_moments.variance + 1e-6)
lm_summary = tf.Summary(value=[
tf.Summary.Value(tag="rl_rewards/lm",
simple_value=np.mean(lm_score))
])
summary_writer.add_summary(lm_summary, global_step=(i))
qa_summary = tf.Summary(value=[
tf.Summary.Value(tag="rl_rewards/qa",
simple_value=np.mean(qa_f1s))
])
summary_writer.add_summary(qa_summary, global_step=(i))
disc_summary = tf.Summary(value=[
tf.Summary.Value(tag="rl_rewards/disc",
simple_value=np.mean(disc_scores))
])
summary_writer.add_summary(disc_summary,
global_step=(i))
lm_white_summary = tf.Summary(value=[
tf.Summary.Value(tag="rl_rewards/lm_white",
simple_value=np.mean(
lm_score_whitened))
])
summary_writer.add_summary(lm_white_summary,
global_step=(i))
qa_white_summary = tf.Summary(value=[
tf.Summary.Value(tag="rl_rewards/qa_white",
simple_value=np.mean(
qa_score_whitened))
])
summary_writer.add_summary(qa_white_summary,
global_step=(i))
disc_white_summary = tf.Summary(value=[
tf.Summary.Value(tag="rl_rewards/disc_white",
simple_value=np.mean(
disc_score_whitened))
])
summary_writer.add_summary(disc_white_summary,
global_step=(i))
# Build a combined batch - half ground truth for MLE, half generated for PG
train_batch_ext = duplicate_batch_and_inject(
train_batch, qhat_ids, qhat_str, qhat_lens)
# print(qhat_ids)
# print(qhat_lens)
# print(train_batch_ext[2][2])
rl_dict = {
model.lm_score:
np.asarray((lm_score_whitened *
FLAGS.lm_weight).tolist() + [
FLAGS.pg_ml_weight
for b in range(curr_batch_size)
]),
model.qa_score:
np.asarray((qa_score_whitened *
FLAGS.qa_weight).tolist() +
[0 for b in range(curr_batch_size)]),
model.disc_score:
np.asarray((disc_score_whitened *
FLAGS.disc_weight).tolist() +
[0 for b in range(curr_batch_size)]),
model.rl_lm_enabled:
True,
model.rl_qa_enabled:
True,
model.rl_disc_enabled:
FLAGS.disc_weight > 0,
model.step:
i - FLAGS.pg_burnin,
model.hide_answer_in_copy:
True
}
# perform a policy gradient step, but combine with a XE step by using appropriate rewards
ops = [
model.pg_optimizer, model.train_summary,
model.q_hat_string
]
if i % FLAGS.eval_freq == 0:
ops.extend([
model.q_hat_ids, model.question_ids,
model.copy_prob, model.question_raw,
model.question_length
])
res_offset = 5
else:
res_offset = 0
ops.extend([model.lm_loss, model.qa_loss])
res = sess.run(ops,
feed_dict={
model.input_batch: train_batch_ext,
model.is_training: False,
**rl_dict
})
summary_writer.add_summary(res[1], global_step=(i))
# Log only the first half of the PG related losses
lm_loss_summary = tf.Summary(value=[
tf.Summary.Value(
tag="train_loss/lm",
simple_value=np.mean(res[3 + res_offset]
[:curr_batch_size]))
])
summary_writer.add_summary(lm_loss_summary,
global_step=(i))
qa_loss_summary = tf.Summary(value=[
tf.Summary.Value(
tag="train_loss/qa",
simple_value=np.mean(res[4 + res_offset]
[:curr_batch_size]))
])
summary_writer.add_summary(qa_loss_summary,
global_step=(i))
# TODO: more principled scheduling here than alternating steps
if FLAGS.disc_train:
ixs = np.round(
np.random.binomial(1, 0.5, curr_batch_size))
qbatch = [
pred_str[ix].replace(" </Sent>", "").replace(
" <PAD>", "")
if ixs[ix] < 0.5 else gold_q_str[ix].replace(
" </Sent>", "").replace(" <PAD>", "")
for ix in range(curr_batch_size)
]
loss = discriminator.train_step(unfilt_ctxt_batch,
qbatch,
ans_text_batch,
ans_pos_batch,
ixs,
step=(i))
else:
# Normal single pass update step. If model has PG capability, fill in the placeholders with empty values
if FLAGS.model_type[:
7] == "MALUUBA" and not FLAGS.policy_gradient:
rl_dict = {
model.lm_score:
[0 for b in range(curr_batch_size)],
model.qa_score:
[0 for b in range(curr_batch_size)],
model.disc_score:
[0 for b in range(curr_batch_size)],
model.rl_lm_enabled: False,
model.rl_qa_enabled: False,
model.rl_disc_enabled: False,
model.hide_answer_in_copy: False
}
else:
rl_dict = {}
# Perform a normal optimizer step
ops = [
model.optimizer, model.train_summary,
model.q_hat_string
]
if i % FLAGS.eval_freq == 0:
ops.extend([
model.q_hat_ids, model.question_ids,
model.copy_prob, model.question_raw,
model.question_length
])
res = sess.run(ops,
feed_dict={
model.input_batch: train_batch,
model.is_training: True,
**rl_dict
})
summary_writer.add_summary(res[1], global_step=(i))
# Dump some output periodically
if i > 0 and i % FLAGS.eval_freq == 0 and (
i > FLAGS.pg_burnin or not FLAGS.policy_gradient):
with open(FLAGS.log_dir + 'out.htm', 'w',
encoding='utf-8') as fp:
fp.write(
output_pretty(res[2].tolist(), res[3], res[4],
res[5], 0, i))
gold_batch = res[6]
gold_lens = res[7]
f1s = []
bleus = []
for b, pred in enumerate(res[2]):
pred_str = tokens_to_string(pred[:gold_lens[b] - 1])
gold_str = tokens_to_string(
gold_batch[b][:gold_lens[b] - 1])
f1s.append(metrics.f1(gold_str, pred_str))
bleus.append(metrics.bleu(gold_str, pred_str))
f1summary = tf.Summary(value=[
tf.Summary.Value(tag="train_perf/f1",
simple_value=sum(f1s) / len(f1s))
])
bleusummary = tf.Summary(value=[
tf.Summary.Value(tag="train_perf/bleu",
simple_value=sum(bleus) / len(bleus))
])
summary_writer.add_summary(f1summary, global_step=(i))
summary_writer.add_summary(bleusummary, global_step=(i))
# Evaluate against dev set
f1s = []
bleus = []
nlls = []
np.random.shuffle(dev_data)
dev_subset = dev_data[:num_dev_samples]
dev_data_source.initialise(dev_subset)
for j in tqdm(range(num_steps_dev), desc='Eval ' + str(i)):
dev_batch, curr_batch_size = dev_data_source.get_batch(
)
pred_batch, pred_ids, pred_lens, gold_batch, gold_lens, ctxt, ctxt_len, ans, ans_len, nll = sess.run(
[
model.q_hat_beam_string, model.q_hat_beam_ids,
model.q_hat_beam_lens, model.question_raw,
model.question_length, model.context_raw,
model.context_length, model.answer_locs,
model.answer_length, model.nll
],
feed_dict={
model.input_batch: dev_batch,
model.is_training: False
})
nlls.extend(nll.tolist())
# out_str="<h1>"+str(e)+' - '+str(datetime.datetime.now())+'</h1>'
for b, pred in enumerate(pred_batch):
pred_str = tokens_to_string(
pred[:pred_lens[b] - 1]).replace(
' </Sent>', "").replace(" <PAD>", "")
gold_str = tokens_to_string(
gold_batch[b][:gold_lens[b] - 1])
f1s.append(metrics.f1(gold_str, pred_str))
bleus.append(metrics.bleu(gold_str, pred_str))
# out_str+=pred_str.replace('>','>').replace('<','<')+"<br/>"+gold_str.replace('>','>').replace('<','<')+"<hr/>"
if j == 0:
title = chkpt_path
out_str = output_eval(title, pred_batch, pred_ids,
pred_lens, gold_batch,
gold_lens, ctxt, ctxt_len,
ans, ans_len)
with open(FLAGS.log_dir + 'out_eval_' +
FLAGS.model_type + '.htm',
'w',
encoding='utf-8') as fp:
fp.write(out_str)
f1summary = tf.Summary(value=[
tf.Summary.Value(tag="dev_perf/f1",
simple_value=sum(f1s) / len(f1s))
])
bleusummary = tf.Summary(value=[
tf.Summary.Value(tag="dev_perf/bleu",
simple_value=sum(bleus) / len(bleus))
])
nllsummary = tf.Summary(value=[
tf.Summary.Value(tag="dev_perf/nll",
simple_value=sum(nlls) / len(nlls))
])
summary_writer.add_summary(f1summary, global_step=i)
summary_writer.add_summary(bleusummary, global_step=i)
summary_writer.add_summary(nllsummary, global_step=i)
mean_nll = sum(nlls) / len(nlls)
if mean_nll < best_oos_nll:
print("New best NLL! ", mean_nll, " Saving...")
best_oos_nll = mean_nll
saver.save(sess,
chkpt_path + '/model.checkpoint',
global_step=i)
else:
print("NLL not improved ", mean_nll)
if FLAGS.policy_gradient:
print("Saving anyway")
saver.save(sess,
chkpt_path + '/model.checkpoint',
global_step=i)
if FLAGS.disc_train:
print("Saving disc")
discriminator.save_to_chkpt(FLAGS.model_dir, i)