def run(self, epochs=1, learning_rate=1.5, regularization=0.0, momentum=0.1):
processor = NN_process.unsortedPairProcessor('../data/pairs/sets/enwiki_pairs_20-train.txt', '../data/pairs/sets/enwiki_no_pairs_20-train.txt',
'../data/model/docfreq.npy', '../data/model/minimal', WORDS, EMBEDDING_DIM, BATCH_SIZE)
train_x1 = theano.shared(value=processor.x1, name='train_x1', borrow=False)
train_x2 = theano.shared(value=processor.x2, name='train_x2', borrow=False)
train_y = theano.shared(value=processor.y, name='train_y', borrow=False)
train_z = theano.shared(value=processor.z, name='train_z', borrow=False)
print 'Initializing train function...'
train = self.train_function_momentum(train_x1, train_x2, train_y, train_z)
t = Thread(target=processor.process)
t.start()
for e in xrange(epochs):
processor.new_epoch()
processor.lock.acquire()
while not processor.ready:
processor.lock.wait()
processor.lock.release()
train_x1.set_value(processor.x1, borrow=False)
train_x2.set_value(processor.x2, borrow=False)
train_y.set_value(processor.y, borrow=False)
train_z.set_value(processor.z, borrow=False)
processor.lock.acquire()
processor.cont = True
processor.ready = False
processor.lock.notifyAll()
processor.lock.release()
for b in xrange(BATCHES):
#c = []
cost = train(lr=learning_rate, reg=regularization, mom=momentum)
#c.append(cost)
print 'Training, batch %d, cost %.5f' % (b, cost)
print numpy.transpose(self.model1.W.get_value())
processor.lock.acquire()
while not processor.ready:
processor.lock.wait()
processor.lock.release()
train_x1.set_value(processor.x1, borrow=False)
train_x2.set_value(processor.x2, borrow=False)
train_y.set_value(processor.y, borrow=False)
train_z.set_value(processor.z, borrow=False)
processor.lock.acquire()
processor.cont = True
processor.ready = False
if b == BATCHES-1 and e == epochs-1:
processor.stop = True
processor.lock.notifyAll()
processor.lock.release()
#print 'Training, epoch %d, cost %.5f' % (e, numpy.mean(c))
self.save_me('run2.npy')
