if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Sample from a trained SeqGAN model.')
parser.add_argument('sample_len', metavar='N', type=int,
help='length of sample to generate')
parser.add_argument('-t', '--dictionary', default='dictionary.pkl',
type=str, help='path to dictionary file')
parser.add_argument('-d', '--logdir', default='model/', type=str,
help='directory of the trained model')
parser.add_argument('-c', '--only_cpu', default=True, action='store_true',
help='if set, only build weights on cpu')
args = parser.parse_args()
if not os.path.exists(args.dictionary):
raise ValueError('No dictionary file found: "%s". To build it, '
'run train.py' % args.dictionary)
_, rev_dict = utils.get_dictionary(None, dfile=args.dictionary)
num_classes = len(rev_dict)
sess = tf.Session()
model = SeqGAN(sess,
num_classes,
logdir=args.logdir,
only_cpu=args.only_cpu)
model.build()
model.load(ignore_missing=True)
g = model.generate(args.sample_len)
print('Generated text:', utils.detokenize(g, rev_dict))
type=int,
help='learning phase (None for synchronized)')
parser.add_argument('-d',
'--logdir',
default='model/',
type=str,
help='where to store the trained model')
args = parser.parse_args()
# Turns on logging.
import logging
root = logging.getLogger()
root.setLevel(logging.DEBUG)
dictionary, rev_dict = utils.get_dictionary(args.text)
num_classes = len(dictionary)
iterator = utils.tokenize(args.text,
dictionary,
batch_size=args.batch_size,
seq_len=args.seq_len)
sess = tf.Session()
model = SeqGAN(sess,
num_classes,
logdir=args.logdir,
learn_phase=args.learn_phase,
only_cpu=args.only_cpu)
model.build()
model.load(ignore_missing=True)
import logging
import gensim
import nltk
from gensim.models import Word2Vec
from nltk.data import load
import numpy as np
import utils
dictionary = utils.get_dictionary()
lemma_dictionary = utils.get_lemma_dictionary()
'''
A vector denoting a single feature of a word
'''
class FeatureVector(object):
def get_vector(self):
self.generate_vector()
return self.vector
def generate_vector(self):
self.vector = []
def __init__(self, entity, document=None):
self.entity = entity
self.vector = []
self.document = document
'''
