def main():
dataloader = IMDB()
model = VRAE(dataloader.word2idx, dataloader.idx2word)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(args.num_epoch):
dataloader.update_word_dropout()
print("\nWord Dropout")
dataloader.shuffle()
print("Data Shuffled", end='\n\n')
for i, (enc_inp, dec_inp, dec_out) in enumerate(dataloader.next_batch()):
log = model.train_session(sess, enc_inp, dec_inp, dec_out)
if i % args.display_loss_step == 0:
print("Step %d | [%d/%d] | [%d/%d]" % (log['step'], epoch+1, args.num_epoch, i, len(dataloader.enc_inp)//args.batch_size), end='')
print(" | nll_loss:%.1f | kl_w:%.3f | kl_loss:%.2f \n" % (log['nll_loss'], log['kl_w'], log['kl_loss']))
model.reconstruct(sess, enc_inp[-1], dec_inp[-1])
#model.generate(sess)
model.customized_reconstruct(sess, 'i love this film and i think it is one of the best films')
model.customized_reconstruct(sess, 'this movie is a waste of time and there is no point to watch it')
save_path = model.saver.save(sess, model.model_path)
print("Model saved in file: %s" % save_path)
def main():
dataloader = IMDB()
model = VRAE(dataloader.word2idx)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(args.num_epoch):
dataloader.update_word_dropout()
dataloader.shuffle()
print("\nData Shuffled", end='\n\n')
for i, (seq, seq_dropped,
seq_len) in enumerate(dataloader.next_batch()):
log = model.train_session(sess, seq, seq_dropped, seq_len)
if i % args.display_loss_step == 0:
print("Step %d | [%d/%d] | [%d/%d]" %
(log['step'], epoch + 1, args.num_epoch, i,
len(dataloader._X) // args.batch_size),
end='')
"""
if args.mutinfo_loss:
print("\n\tnll_loss:%.1f | kl_w:%.3f | kl_loss:%.2f | temper:%.2f | mutinfo_loss:%.2f \n" % (
log['nll_loss'], log['kl_w'], log['kl_loss'], log['temperature'], log['mutinfo_loss']))
else:
"""
print(" | nll_loss:%.1f | kl_w:%.3f | kl_loss:%.2f \n" %
(log['nll_loss'], log['kl_w'], log['kl_loss']))
model.reconstruct(sess, seq[-1], seq_dropped[-1])
model.generate(sess)
save_path = model.saver.save(sess, model.model_path)
print("Model saved in file: %s" % save_path)
def main():
dataloader = IMDB()
params = {
'vocab_size': len(dataloader.word2idx),
'word2idx': dataloader.word2idx,
'idx2word': dataloader.idx2word,
}
print('Vocab Size:', params['vocab_size'])
model = VRAE(params)
saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
for epoch in range(args.num_epoch):
dataloader.update_word_dropout()
print("\nWord Dropout")
dataloader.shuffle()
print("Data Shuffled", end='\n\n')
for i, (enc_inp, dec_inp,
dec_out) in enumerate(dataloader.next_batch()):
log = model.train_session(sess, enc_inp, dec_inp, dec_out)
if i % args.display_loss_step == 0:
print("Step %d | [%d/%d] | [%d/%d]" %
(log['step'], epoch + 1, args.num_epoch, i,
len(dataloader.enc_inp) // args.batch_size),
end='')
print(" | nll_loss:%.1f | kl_w:%.3f | kl_loss:%.2f \n" %
(log['nll_loss'], log['kl_w'], log['kl_loss']))
model.generate(sess)
model.reconstruct(sess, enc_inp[-1], dec_inp[-1])
model.customized_reconstruct(
sess, 'i love this film and i think it is one of the best films')
model.customized_reconstruct(
sess,
'this movie is a waste of time and there is no point to watch it')
save_path = saver.save(sess, './saved/vrae.ckpt')
print("Model saved in file: %s" % save_path)
def main():
dataloader = IMDB()
model = VRAE(dataloader.word2idx)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(args.num_epoch):
dataloader.update_word_dropout()
dataloader.shuffle()
print("\nData Shuffled", end='\n\n')
for i, (seq, seq_dropped, seq_len) in enumerate(dataloader.next_batch()):
log = model.train_session(sess, seq, seq_dropped, seq_len)
if i % args.display_loss_step == 0:
bar = '[%d/%d] | [%d/%d] | nll_loss: %.1f | kl_w: %.3f | kl_loss: %.1f'
vars = (epoch+1, args.num_epoch, i+1, len(dataloader._X)//args.batch_size, log['nll_loss'],
log['kl_w'], log['kl_loss'])
print(bar % vars)
if i % args.display_text_step == 0:
model.reconstruct(sess, seq[-1], seq_dropped[-1])
model.generate(sess)
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
args.max_len = 100
args.batch_size = 64
args.max_dec_len = 100
args.display_info_step = 1000
args.isPointer = False
args.vocab_limit = 35000
train_data_len = 209940
args.diff_input = True
print(args)
dataloader = IWSLT(batch_size=args.batch_size, vocab_limit=args.vocab_limit, max_input_len=args.max_len, max_output_len=args.max_dec_len)
params = {
'vocab_size_encoder': len(dataloader.idx2token),
'vocab_size': len(dataloader.word2idx),
'word2idx': dataloader.word2idx,
'idx2word': dataloader.idx2word,
'idx2token': dataloader.idx2token}
print('Vocab Size:', params['vocab_size'])
model = VRAE(params)
saver = tf.train.Saver()
exp_path = "./saved/iwslt_seq2seq/"
model_name = "seq2seq.ckpt"
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
EPOCH_STEPS = (train_data_len-1)//args.batch_size+1
summary_writer = tf.summary.FileWriter(exp_path, sess.graph)
restore_path = tf.train.latest_checkpoint(exp_path)
if restore_path:
saver.restore(sess, restore_path)
last_train_step = int(restore_path.split("-")[-1]) % EPOCH_STEPS
print("Model restore from file: %s, last train step: %d" % (restore_path, last_train_step))
# summary_writer = tf.summary.FileWriter(exp_path)
# saver.restore(sess, exp_path+model_name)
for epoch in range(args.num_epoch):
# dataloader.update_word_dropout()
# print("\nWord Dropout")
# dataloader.shuffle()
# print("Data Shuffled", end='\n\n')
batcher = dataloader.load_data()
step = -1
while True:
try:
# enc_inp, dec_inp_full, dec_out = next(dataloader.data_loader)
(x_enc_inp, x_dec_inp_full, x_dec_out, y_enc_inp, y_dec_inp_full, y_dec_out), x_enc_inp_oovs, data_oovs, _ = next(batcher)
# enc_inp, dec_inp_full, dec_out = dataloader.next_batch()
enc_inp, dec_inp_full, dec_out = x_enc_inp, y_dec_inp_full, y_dec_out
dec_inp = dataloader.update_word_dropout(dec_inp_full)
step += 1
except StopIteration:
print("there are no more examples")
break
# print(step, "enc_inp.shape:", enc_inp.shape)
# print(step, "dec_inp_full.shape:", dec_inp_full.shape)
# print(step, "dec_out.shape:", dec_out.shape)
log = model.train_session(sess, enc_inp, dec_inp, dec_out)
# get the summaries and iteration number so we can write summaries to tensorboard
summaries, train_step = log['summaries'], log['step']
summary_writer.add_summary(summaries, train_step) # write the summaries
if train_step % 100 == 0: # flush the summary writer every so often
summary_writer.flush()
if step % args.display_loss_step == 0:
print("Step %d | [%d/%d] | [%d/%d]" % (log['step'], epoch+1, args.num_epoch, step, train_data_len//args.batch_size), end='')
print(" | loss:%.3f" % (log['loss']))
if step % args.display_info_step == 0:
model.reconstruct(sess, enc_inp[-1], dec_out[-1])
save_path = saver.save(sess, exp_path+model_name, global_step=train_step)
print("Model saved in file: %s" % save_path)
model.reconstruct(sess, enc_inp[-1], dec_out[-1])
save_path = saver.save(sess, exp_path+model_name, global_step=train_step)
print("Model saved in file: %s" % save_path)