def setUp(self):
fname = 'friends.txt'
f = open(fname, 'r')
self.txt = f.read()
f.close()
c = Corpus(self.txt)
c.brownInit(500)
rnn = RNN(100, c.V, 50)
rnn.load('rnn.save')
self.trainer = Trainer(c,rnn, nepochs=50, alpha = 1.8)
def tesT_TrainingOnSentances(self):
c = Corpus(self.txt)
rnn = RNN(100, c.V, 50)
trainer = Trainer(c,rnn, nepochs=50, alpha = 1.8)
trainer.train()
def search_for_parameter():
n_bis = [100000, 200000, 500000]
steps = [5e-2, 1e-1, 5e-1, 1]
n_epoch = 5
for n_bi in n_bis:
for step in steps:
print('-' * 20)
print(n_bi, ' ', step)
config['n_bigram'] = n_bi
config['smooth'] = step
corpus = Corpus(config['data_dir'])
pct = perceptron()
for cnt in range(n_epoch):
train(corpus.trainSet, pct)
test(corpus.testSet, pct)
from rnn import *
from dataLoader import Corpus
from trainer import Trainer
fname = 'shakespear.txt'
f = open(fname, 'r')
txt = f.read()
f.close()
c = Corpus(txt)
c.brownInit(10000)
rnn = RNN(100, c.V, 100)
# rnn = RNN.load('rnn.save')
rnn.load('rnn.save')
# rnn = RNN(100, c.V, 50)
trainer = Trainer(c, rnn, nepochs=50, alpha=0.9)
trainer.generate_sequence()
# trainer.train()
trainer.mainEventLoop()
steps = [5e-2, 1e-1, 5e-1, 1]
n_epoch = 5
for n_bi in n_bis:
for step in steps:
print('-' * 20)
print(n_bi, ' ', step)
config['n_bigram'] = n_bi
config['smooth'] = step
corpus = Corpus(config['data_dir'])
pct = perceptron()
for cnt in range(n_epoch):
train(corpus.trainSet, pct)
test(corpus.testSet, pct)
if __name__ == '__main__':
#search_for_parameter()
pct = perceptron()
corpus = Corpus(config['data_dir'])
n_epoch = 5
while True:
for cnt in range(n_epoch):
print('Happy training!')
train(corpus.trainSet, pct)
print('\nHappy testing!')
test(corpus.testSet, pct)
config['smooth'] /= 10
if config['smooth'] < 1.01e-10:
break
def tesT_saving_model(self):
c = Corpus(self.txt)
rnn = RNN(100, c.V, 50)
rnn.save()
def test_perplexicity(self):
c = Corpus("asdada asdaa asd adada dadada. asdas dasd.da ad.a d.sa da asd")
c.brownInit(500,0)
p = self.trainer.calcPerplexicity(c)
print Fore.CYAN, p, "\n"
from rnn import *
from dataLoader import Corpus
from trainer import Trainer
fname = 'shakespear.txt'
f = open(fname, 'r')
txt = f.read()
f.close()
c = Corpus(txt)
c.brownInit(10000)
rnn = RNN(100, c.V, 100)
# rnn = RNN.load('rnn.save')
rnn.load('rnn.save')
# rnn = RNN(100, c.V, 50)
trainer = Trainer(c,rnn, nepochs=50, alpha = 0.9)
trainer.generate_sequence()
# trainer.train()
trainer.mainEventLoop()
