def glossolalia(self):
result = None
loop = 10
t = self.sine_temp()
min_length = DEF_MIN
if 'minimum' in self.cf:
min_length = self.cf['minimum']
while not result and loop > 0:
lines = torchrnn.generate_lines(n=self.cf['sample'],
temperature=t,
model=self.cf['model'],
max_length=self.api.char_limit,
min_length=min_length)
lines = self.clean_glosses(lines)
if 'logs' in self.cf:
log = os.path.join(self.cf['logs'], str(time.time())) + '.log'
with open(log, 'wt') as f:
for line in lines:
f.write(line)
f.write("\n")
f.write("Length = {}\n\n".format(len(line)))
print("Got {} lines on loop {}".format(len(lines), loop))
if len(lines) > 5:
result = random.choice(lines[2:-2])
loop = loop - 1
return result
def bombinan(self):
twhtmls = torchrnn.generate_lines(model=self.cf['model'], n=10)
for t in twhtmls:
tt = self.greptw(t)
if tt:
return tt
return None
def bombinan(self):
twhtmls = torchrnn.generate_lines(model=self.cf['model'], n=10)
for t in twhtmls:
tt = self.greptw(t)
if tt:
return tt
return None
def sample(self):
if 'sample_method' in self.cf and self.cf['sample_method'] == 'text':
print("Sampling using text")
self.raw_rnn = torchrnn.generate_text(temperature=self.temperature,
model=self.cf['model'],
length=self.cf['sample'],
opts=self.options)
else:
self.raw_rnn = torchrnn.generate_lines(
n=self.cf['sample'],
temperature=self.temperature,
model=self.cf['model'],
max_length=self.max_length,
min_length=self.min_length,
opts=self.options)
return self.raw_rnn
def glossolalia(self):
result = None
loop = 10
accept_re = re.compile(self.cf['accept'])
t = self.sine_temp()
print("Temperature = {}".format(t))
while not result and loop > 0:
lines = torchrnn.generate_lines(n=self.cf['sample'], temperature=t, model=self.cf['model'])
lines = [ l for l in lines if accept_re.match(l) ]
log = os.path.join(self.cf['logs'], str(time.time())) + '.log'
with open(log, 'wt') as f:
for line in lines:
f.write(line)
f.write("\n")
if len(lines) > 5:
result = random.choice(lines[2:-2])
loop = loop - 1
return result
#!/usr/bin/env python
import torchrnn, argparse
MODEL = '/Users/mike/torch/torch-rnn/cv_glossatory/checkpoint_215100.t7'
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument('-m', '--model', type=str, default=MODEL, help="torch-rnn checkpoint")
ap.add_argument('-t', '--temperature', type=float, default=0.8, help="sample temperature")
ap.add_argument('-n', '--number', type=int, default=10, help="number of tweets to sample")
args = ap.parse_args()
tweets = torchrnn.generate_lines(temperature=args.temperature, model=args.model, n=args.number)
for tweet in tweets:
print(tweet)
