The codes of paper "Long Text Generation via Adversarial Training with Leaked Information" on AAAI 2018. Text generation using GAN and Hierarchical Reinforcement Learning.
I am a little confused by your code synthetic data with sequence length 40.
The following code may replace the positive samples(real data) with LSTM generated ones.
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file, 0)
Since the oracle LSTM does not reload parameters for pkl file, how could you treat the random initialized LSTM generated samples as positive samples?
Many thanks!
At the beginning of the Main.py, I import 2 different oracles. You can see that length-20 oracle initializes by reloading pkl file and length-40 oracle will initialize variable from a random number.
I am a little confused by your code synthetic data with sequence length 40. The following code may replace the positive samples(real data) with LSTM generated ones.
generate_samples(sess, target_lstm, BATCH_SIZE, generated_num, positive_file, 0)
Since the oracle LSTM does not reload parameters for pkl file, how could you treat the random initialized LSTM generated samples as positive samples? Many thanks!