import NN_process
from threading import Thread
import time
import copy
NO_DATA = 4900000
BATCH_SIZE = 100
BATCHES = NO_DATA / BATCH_SIZE
EMBEDDING_DIM = 400
WORDS = 20
INPUT_SHAPE = (400, 20)
OUTPUT_SHAPE = (400, 1)
processor = NN_process.PairProcessor('../data/pairs/enwiki_pairs_20.txt',
'../data/pairs/enwiki_no_pairs_20.txt',
'../data/model/docfreq.npy',
'../data/model/minimal', WORDS,
EMBEDDING_DIM, BATCH_SIZE)
t = Thread(target=processor.process)
t.start()
print 'Start processor thread'
processor.new_epoch()
processor.lock.acquire()
while not processor.ready:
processor.lock.wait()
processor.lock.release()
processor.lock.acquire()
processor.cont = True
processor.ready = False
def run(self, epochs=EPOCHS, learning_rate=LEARNING_RATE, regularization=REGULARIZATION, momentum=MOMENTUM):
processor = NN_process.PairProcessor('../data/wiki/pairs/sets/enwiki_pairs_' + str(WORDS) + '-train.txt',
'../data/wiki/pairs/sets/enwiki_no_pairs_' + str(WORDS) + '-train.txt',
'../data/wiki/model/docfreq.npy', '../data/wiki/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.daemon = True
t.start()
import signal
def signal_handler(signal, frame):
import os
os._exit(0)
signal.signal(signal.SIGINT, signal_handler)
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 repr(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()
if b < BATCHES-2:
processor.cont = True
processor.ready = False
if b == BATCHES-1 and e == epochs-1:
processor.stop = True
processor.cont = True
processor.lock.notifyAll()
processor.lock.release()
#print 'Training, epoch %d, cost %.5f' % (e, numpy.mean(c))
t.join()
self.save_me('run1.npy')
