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')