A Simple Sequence-to-Sequence Framework. It is based on TensorFlow's RRN and tf.contrib.seq2seq.Basic Decoder modules.
One data per row, two sequential file data correspond one by one. Refer specifically to the contents under the data file.
Sequnce file A like:
new jersey is sometimes quiet during autumn , and it is snowy in april .
the united states is usually chilly during july , and it is usually freezing in november .
california is usually quiet during march , and it is usually hot in june .
the united states is sometimes mild during june , and it is cold in september .
your least liked fruit is the grape , but my least liked is the apple .
his favorite fruit is the orange , but my favorite is the grape .
paris is relaxing during december , but it is usually chilly in july .
Sequnce file B like:
new jersey est parfois calme pendant l' automne , et il est neigeux en avril .
les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .
california est généralement calme en mars , et il est généralement chaud en juin .
les états-unis est parfois légère en juin , et il fait froid en septembre .
votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .
son fruit préféré est l'orange , mais mon préféré est le raisin .
paris est relaxant en décembre , mais il est généralement froid en juillet .
seq2seq_RNN_model.py and seq2seq_RNN_model_batch_size.py work the same way, but seq2seq_RNN_model_batch_size.py needs to determine the batch size first, seq2seq_RNN_model.py can dynamically adjust the batch size. But at present, they are quite different in the validation set. I have recorded them in file analysis_log.
TODO: Find out the reason why seq2seq_RNN_model.py does not work well on the verification set, and fix the code BUG.