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Updated sequence_gan to be Python 3.6 compatible #56

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Updated sequence_gan to be Python 3.6 compatible #56

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27 changes: 14 additions & 13 deletions sequence_gan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,9 @@
from discriminator import Discriminator
from rollout import ROLLOUT
from target_lstm import TARGET_LSTM
import cPickle
import _pickle as pickle

# encoding
#########################################################################################
# Generator Hyper-parameters
######################################################################################
Expand Down Expand Up @@ -57,7 +58,7 @@ def target_loss(sess, target_lstm, data_loader):
nll = []
data_loader.reset_pointer()

for it in xrange(data_loader.num_batch):
for it in range(data_loader.num_batch):
batch = data_loader.next_batch()
g_loss = sess.run(target_lstm.pretrain_loss, {target_lstm.x: batch})
nll.append(g_loss)
Expand All @@ -70,7 +71,7 @@ def pre_train_epoch(sess, trainable_model, data_loader):
supervised_g_losses = []
data_loader.reset_pointer()

for it in xrange(data_loader.num_batch):
for it in range(data_loader.num_batch):
batch = data_loader.next_batch()
_, g_loss = trainable_model.pretrain_step(sess, batch)
supervised_g_losses.append(g_loss)
Expand All @@ -89,7 +90,7 @@ def main():
dis_data_loader = Dis_dataloader(BATCH_SIZE)

generator = Generator(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN)
target_params = cPickle.load(open('save/target_params.pkl'))
target_params = pickle.load(open('save/target_params_py3.pkl', 'rb'))
target_lstm = TARGET_LSTM(vocab_size, BATCH_SIZE, EMB_DIM, HIDDEN_DIM, SEQ_LENGTH, START_TOKEN, target_params) # The oracle model

discriminator = Discriminator(sequence_length=20, num_classes=2, vocab_size=vocab_size, embedding_size=dis_embedding_dim,
Expand All @@ -106,26 +107,26 @@ def main():

log = open('save/experiment-log.txt', 'w')
# pre-train generator
print 'Start pre-training...'
print('Start pre-training...')
log.write('pre-training...\n')
for epoch in xrange(PRE_EPOCH_NUM):
for epoch in range(PRE_EPOCH_NUM):
loss = pre_train_epoch(sess, generator, gen_data_loader)
if epoch % 5 == 0:
generate_samples(sess, generator, BATCH_SIZE, generated_num, eval_file)
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
print 'pre-train epoch ', epoch, 'test_loss ', test_loss
print('pre-train epoch ', epoch, 'test_loss ', test_loss)
buffer = 'epoch:\t'+ str(epoch) + '\tnll:\t' + str(test_loss) + '\n'
log.write(buffer)

print 'Start pre-training discriminator...'
print('Start pre-training discriminator...')
# Train 3 epoch on the generated data and do this for 50 times
for _ in range(50):
generate_samples(sess, generator, BATCH_SIZE, generated_num, negative_file)
dis_data_loader.load_train_data(positive_file, negative_file)
for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
Expand All @@ -136,8 +137,8 @@ def main():

rollout = ROLLOUT(generator, 0.8)

print '#########################################################################'
print 'Start Adversarial Training...'
print('#########################################################################')
print('Start Adversarial Training...')
log.write('adversarial training...\n')
for total_batch in range(TOTAL_BATCH):
# Train the generator for one step
Expand All @@ -153,7 +154,7 @@ def main():
likelihood_data_loader.create_batches(eval_file)
test_loss = target_loss(sess, target_lstm, likelihood_data_loader)
buffer = 'epoch:\t' + str(total_batch) + '\tnll:\t' + str(test_loss) + '\n'
print 'total_batch: ', total_batch, 'test_loss: ', test_loss
print('total_batch: ', total_batch, 'test_loss: ', test_loss)
log.write(buffer)

# Update roll-out parameters
Expand All @@ -166,7 +167,7 @@ def main():

for _ in range(3):
dis_data_loader.reset_pointer()
for it in xrange(dis_data_loader.num_batch):
for it in range(dis_data_loader.num_batch):
x_batch, y_batch = dis_data_loader.next_batch()
feed = {
discriminator.input_x: x_batch,
Expand Down