def infer(sess, model, decoder, source_sent, id_to_vocab, end_id, temp):
""" Perform inferencing. In other words, generate a paraphrase
for the source sentence.
Args:
sess : Tensorflow session.
model : dict of tensor to value
decoder : 0 for greedy, 1 for sampling
source_sent : source sentence to generate a paraphrase for
id_to_vocab : dict of vocabulary index to word
end_id : the end token
temp : the sampling temperature to use when `decoder` is 1
Returns:
str : for the generated paraphrase
"""
seq_source_words, seq_source_ids = preprocess_batch([ source_sent ])
seq_source_len = [ len(seq_source) for seq_source in seq_source_ids ]
feed_dict = {
model['seq_source_ids']: seq_source_ids,
model['seq_source_lengths']: seq_source_len,
model['decoder_technique']: decoder,
model['sampling_temperature']: temp
}
feeds = [
model['predictions']
#model['final_sequence_lengths']
]
predictions = sess.run(feeds, feed_dict)[0][0]
print("PREDICTIONS ARE {}".format(predictions))
return translate(predictions, decoder, id_to_vocab, end_id)
def infer(sess, args, model, id_to_vocab, end_id):
"""Perform inference on a model. This is intended to be interactive.
A user will run this from the command line to provide an input sentence
and receive a paraphrase as output continuously within a loop.
Args:
sess: Tensorflow session
args: ArgumentParser object configuration
model: a dictionary containing the model tensors
id_to_vocab: vocabulary index of id_to_vocab
end_id: the end of sentence token
"""
from preprocess_data import preprocess_batch
while 1:
source_sent = input("Enter source sentence: ")
seq_source_words, seq_source_ids = preprocess_batch([source_sent])
seq_source_len = [len(seq_source) for seq_source in seq_source_ids]
if args.decoder == 'greedy':
decoder = 0
elif args.decoder == 'sample':
decoder = 1
feed_dict = {
model['seq_source_ids']: seq_source_ids,
model['seq_source_lengths']: seq_source_len,
model['decoder_technique']: decoder,
model['sampling_temperature']: args.sampling_temperature,
}
feeds = [model['predictions'], model['final_sequence_lengths']]
predictions, final_sequence_lengths = sess.run(feeds, feed_dict)
for sent_pred in predictions:
if sent_pred[-1] == end_id:
sent_pred = sent_pred[0:-1]
print("Paraphrase : {}".format(' '.join(
[id_to_vocab[pred] for pred in sent_pred])))
def infer(self, decoder, source_sent, id_to_vocab, temp, how_many):
""" Perform inferencing. In other words, generate a paraphrase
for the source sentence.
Args:
decoder : 0 for greedy, 1 for sampling
source_sent : source sentence to generate a paraphrase for
id_to_vocab : dict of vocabulary index to word
end_id : the end token
temp : the sampling temperature to use when `decoder` is 1
Returns:
str : for the generated paraphrase
"""
seq_source_words, seq_source_ids = preprocess_batch([source_sent] *
how_many)
#print(seq_source_words)
#print(seq_source_ids)
seq_source_len = [len(seq_source) for seq_source in seq_source_ids]
#print(seq_source_len)
feed_dict = {
self.model['seq_source_ids']: seq_source_ids,
self.model['seq_source_lengths']: seq_source_len,
self.model['decoder_technique']: decoder,
self.model['sampling_temperature']: temp
}
feeds = [
self.model['predictions']
#model['final_sequence_lengths']
]
predictions = self.sess.run(feeds, feed_dict)[0]
#print(predictions)
return self.translate(predictions, decoder, id_to_vocab,
seq_source_words[0])