def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists("files/"):
os.mkdir("files/")
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(open("/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl"))
i2w = dict((v, k) for k, v in w2i.iteritems())
i2w[0] = "UNK"
senna = [i2w[i] for i in range(len(i2w.keys()))]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp["embedsize"], e_act=identity)
H = ae(i_size=hp["embedsize"] * hp["wsize"], h_size=hp["hsize"], e_act=T.tanh)
L = logistic(i_size=hp["hsize"], h_size=1, act=identity)
path = hp["loadpath"]
if path:
load(embedding, path + "/embedding.pkl")
load(H, path + "/hidden.pkl")
load(L, path + "/logistic.pkl")
hp["embedsize"] = embedding.params["e_weights"].get_value(borrow=True).shape[1]
hp["hsize"] = H.params["e_weights"].get_value(borrow=True).shape[1]
jobman.save()
del H.params["d_bias"]
del embedding.params["d_bias"]
del embedding.params["e_bias"]
lr = hp["lr"]
h_size = hp["hsize"]
bs = hp["bs"]
posit_embed = theano.sparse.dot(s_valid, embedding.params["e_weights"]).reshape(
(1 + hp["nneg"], hp["embedsize"] * hp["wsize"])
)
# posit_embed = T.dot(s_valid, embedding.params['e_weights']).reshape((1+hp['nneg'],hp['embedsize']*hp['wsize']))
# negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
# negat_score = L.encode(H.encode(negat_embed))
C = posit_score[1:] - posit_score[0] + hp["margin"]
CC = (rect(C)).mean()
opt = theano.function(
[s_valid], (rect(C)).mean(), updates=dict(L.update(CC, lr) + H.update(CC, lr) + embedding.update_norm(CC, lr))
)
def saveexp():
save(embedding, fname + "embedding.pkl")
save(H, fname + "hidden.pkl")
save(L, fname + "logistic.pkl")
delta = hp["wsize"] / 2
rest = hp["wsize"] % 2
wsimtester = wsim()
freq_idx = cPickle.load(open("/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl"))[:1000]
fname = ""
tseenwords = not debug
for e in range(hp["epoch"]):
hp["split"] = numpy.random.randint(285)
sentences = cPickle.load(
open("/mnt/scratch/bengio/bengio_group/data/gutenberg/small_ints_50000/split" + str(hp["split"]) + ".pkl")
)
nsent = len(sentences)
bigc = []
bigr = []
E = embedding.params["e_weights"].get_value(borrow=True)
seen_words = 0
for i, s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp["wsize"] + 2:
continue
c = []
r = []
if debug:
print " *** Processing document", i, "with", nword,
sys.stdout.flush()
for j in range(delta, nword - delta):
cstart = time.time()
pchunk = s[j - delta : j + delta + rest]
nchunk = []
st = s[j - delta : j]
en = s[j + 1 : j + delta + rest]
rndidx = numpy.random.randint(nsenna, size=(hp["nneg"],))
nchunk = []
for kk in range(hp["nneg"]):
nchunk += st + [rndidx[kk]] + en
# assert len(nchunk) == len(pchunk)*hp['nneg']
# p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
pn = idx2spmat(pchunk + nchunk, nsenna)
# assert pn[0,pchunk[0]] == 1
ctime = time.time() - cstart
tstart = time.time()
l = opt(pn)
c.append(l)
if debug:
print ".",
break
if debug:
print ""
bigc += [numpy.array(c).sum()]
if tseenwords > hp["wsimfreq"] or debug:
hp["wsim"] = wsimtester.score(embedding.params["e_weights"].get_value(borrow=True))
print i, "WordSim Sp Corr:", hp["wsim"]
sys.stdout.flush()
hp["score"] = numpy.array(bigc).mean()
hp["e"] = e
hp["i"] = i
print e, i, "NN Score:", hp["score"]
tseenwords = 0
jobman.save()
if seen_words > hp["freq"] or debug:
seen_words = 0
ne = knn(freq_idx, embedding.params["e_weights"].get_value(borrow=True))
open("files/" + fname + "nearest.txt", "w").write(display(ne, i2w))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
# Symbolic variables
s_bow = T.matrix()
s_posit = T.matrix()#theano.sparse.csr_matrix()
s_negat = T.matrix()#theano.sparse.csr_matrix()
sentences = cPickle.load(open('/scratch/rifaisal/data/guten/guten_subset_idx.pkl'))
senna = cPickle.load(open('/scratch/rifaisal/data/guten/senna.pkl'))
gsubset = cPickle.load(open('/scratch/rifaisal/data/guten/guten_vocab_subset.pkl')).flatten().tolist()
hashtab = dict( zip( gsubset, range( len( gsubset))))
senna = numpy.array(senna)[gsubset].tolist()
s_valid = theano.sparse.csr_matrix()
validsentence = sentences[-10:]
sentences = sentences[:-10]
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = T.nnet.sigmoid)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = rect, d_act = hardtanh)
L = logistic(i_size = hp['hsize'], h_size = 1)
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = T.nnet.sigmoid)
valid_embedding.params['weights'] = embedding.params['e_weights']
valid_embedding.params['bias'] = embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = embedding.encode(s_posit).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = embedding.encode(s_negat).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = valid_embedding.encode(s_valid).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
rec = embedding.reconstruct(s_bow, loss='ce')
CC = (rect(C)).mean() + hp['lambda'] * rec
opt = theano.function([s_posit, s_negat, s_bow],
[C.mean(),rec],
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update(CC,lr)) )
validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
print 'Saved successfully'
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/scratch/rifaisal/data/guten/gutten_sorted_vocab.pkl'))[:1000]
freq_idx = [ hashtab[idx] for idx in freq_idx ]
fname = sys.argv[0]+'_'
for e in range(hp['epoch']):
c = []
r = []
for i in range(nsent):
rsent = numpy.random.randint(nsent-1)
nword = len(sentences[rsent])
if nword < hp['wsize'] + 2:
continue
pidx = numpy.random.randint(low = delta, high = nword-delta)
pchunk = sentences[rsent][pidx-delta:pidx+delta+rest]
nchunk = []
st = sentences[rsent][pidx-delta:pidx]
en = sentences[rsent][pidx+1:pidx+delta+rest]
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for j in range(hp['nneg']):
nchunk += en + [rndidx[j]] + st
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n, b = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna), idx2vec(sentences[rsent],nsenna))
l,g = opt(p,n,b)
c.append(l)
r.append(g)
if (time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (i+1)%(50*hp['freq']) == 0:
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
hp['e'] = e
hp['i'] = i
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if i%hp['freq'] == 0:
hp['score'] = numpy.array(c).mean()
hp['rec'] = numpy.array(r).mean()
print e,i,'NN Score:', hp['score'], 'Reconstruction:', hp['rec']
ne = knn(freq_idx,embedding.params['e_weights'].get_value(borrow=True))
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
saveexp()
sys.stdout.flush()
jobman.save()
save()
def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
idx_start = T.lscalar()
idx_stop = T.lscalar()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'
))
i2w = dict((v, k) for k, v in w2i.iteritems())
i2w[0] = 'UNK'
senna = [i2w[i] for i in range(len(i2w.keys()))]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
minsize = hp['minsize']
maxsize = hp['maxsize']
dsize = maxsize - minsize + 1
H.params['e_bias'] = theano.shared(numpy.array(numpy.zeros(
(dsize, hp['hsize'])),
dtype=theano.config.floatX),
name='e_bias')
path = hp['loadpath']
if path:
load(embedding, path + '/embedding.pkl')
#load(H,path+'/hidden.pkl')
#load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(
borrow=True).shape[1]
#hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
H.params['e_bias'] = theano.shared(numpy.array(numpy.zeros(
(dsize, hp['hsize'])),
dtype=theano.config.floatX),
name='e_bias')
valid_embedding = sparse.supervised.logistic(i_size=nsenna,
h_size=hp['embedsize'],
act=identity)
valid_embedding.params['weights'] = sp.shared(
value=scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(
borrow=True)))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape(
(1, hp['embedsize'] * hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape(
(hp['nneg'], hp['embedsize'] * hp['wsize']))
valid_embed = sp.dot(s_valid, valid_embedding.params['weights']).reshape(
(nsenna, hp['embedsize'] * hp['wsize']))
posit_embed_left = T.concatenate([
posit_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(posit_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
negat_embed_left = T.concatenate([
negat_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(negat_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
posit_embed_right = T.concatenate([
T.zeros_like(posit_embed[:, :idx_start * hp['embedsize']]),
posit_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']]
],
axis=1)
negat_embed_right = T.concatenate([
T.zeros_like(negat_embed[:, :idx_start * hp['embedsize']]),
negat_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']]
],
axis=1)
posit_embed = T.concatenate([
T.zeros_like(posit_embed[:, :idx_start * hp['embedsize']]),
posit_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(posit_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
negat_embed = T.concatenate([
T.zeros_like(negat_embed[:, :idx_start * hp['embedsize']]),
negat_embed[:, idx_start * hp['embedsize']:idx_stop * hp['embedsize']],
T.zeros_like(negat_embed[:, idx_stop * hp['embedsize']:])
],
axis=1)
#posit_embed = ifelse(T.eq(idx_start, 0), posit_embed_left, posit_embed)
#posit_embed = ifelse(T.eq(idx_stop, hp['maxsize']), posit_embed_right, posit_embed)
#negat_embed = ifelse(T.eq(idx_start, 0), negat_embed_left, negat_embed)
#negat_embed = ifelse(T.eq(idx_stop, hp['maxsize']), negat_embed_right, negat_embed)
Hposit = T.tanh(
T.dot(posit_embed, H.params['e_weights']) +
H.params['e_bias'][idx_stop - idx_start - minsize, :])
Hnegat = T.tanh(
T.dot(negat_embed, H.params['e_weights']) +
H.params['e_bias'][idx_stop - idx_start - minsize, :])
posit_score = L.encode(Hposit)
negat_score = L.encode(Hnegat)
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat, idx_start, idx_stop],
(rect(C)).mean(),
updates=dict(
L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(CC, lr)))
validfct = theano.function([s_valid], valid_score)
def saveexp():
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
freq_idx = cPickle.load(
open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'
))[:2000]
fname = ''
validsentence = [
] # cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/valid_debug.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/ints_50000/split'
+ str(hp['split']) + '.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i, s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['maxsize'] + 2:
continue
rndsize = numpy.random.randint(low=hp['minsize'] + 1,
high=hp['maxsize'] - 1)
idxsta = numpy.random.randint(low=1, high=hp['maxsize'] - rndsize)
idxsto = idxsta + rndsize
print 'r', rndsize, 'b', idxsta, 'e', idxsto, 'shape', H.params[
'e_bias'].get_value().shape
c = []
r = []
if debug:
print ' *** Processing document', i, 'with', nword,
sys.stdout.flush()
for j in range(delta, nword - delta):
nd = rndsize / 2
rd = rndsize % 2
pchunk = s[j - delta:j + delta + rest]
nchunk = []
rndidx = numpy.random.randint(nsenna, size=(hp['nneg'], ))
nchunk = []
for kk in range(hp['nneg']):
tmpchunk = copy.copy(pchunk)
tmpchunk[idxsta + nd] = rndidx[kk]
nchunk += tmpchunk
assert len(nchunk) == len(pchunk) * hp['nneg']
p, n = (idx2mat(pchunk, nsenna), idx2mat(nchunk, nsenna))
l = opt(p, n, idxsta, idxsto)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0: #(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(
value=scipy.sparse.csr_matrix(
embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna,
hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank', mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e, i, 'NN Score:', hp['score']
if not debug:
ne = knn(
freq_idx,
embedding.params['e_weights'].get_value(borrow=True))
open('files/' + fname + 'nearest.txt',
'w').write(display(ne, senna))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def run(jobman, debug=False):
hp = jobman.state
# Symbolic variables
s_posit = T.matrix() #theano.sparse.csr_matrix()
s_negat = T.matrix() #theano.sparse.csr_matrix()
s_valid = theano.sparse.csr_matrix()
sentences = cPickle.load(
open('/data/lisatmp2/rifaisal/guten_subset_idx.pkl'))
validsentence = sentences[-10:]
sentences = sentences[:-10]
senna = cPickle.load(open('/data/lisatmp2/rifaisal/senna.pkl'))
gsubset = cPickle.load(
open('/data/lisatmp2/rifaisal/guten_vocab_subset.pkl')).flatten(
).tolist()
hashtab = dict(zip(gsubset, range(len(gsubset))))
senna = numpy.array(senna)[gsubset].tolist()
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = logistic(i_size=nsenna, h_size=hp['embedsize'], act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=rect,
d_act=hardtanh)
L = logistic(i_size=hp['hsize'], h_size=1) #, act = identity)
valid_embedding = sparse.supervised.logistic(i_size=nsenna,
h_size=hp['embedsize'],
act=identity)
#valid_embedding.params['weights'].set_value(embedding.params['weights'].get_value(borrow=True))
#valid_embedding.params['bias'].set_value(embedding.params['bias'].get_value(borrow=True))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = embedding.encode(s_posit).reshape(
(1, hp['embedsize'] * hp['wsize']))
negat_embed = embedding.encode(s_negat).reshape(
(hp['nneg'], hp['embedsize'] * hp['wsize']))
#valid_embed = valid_embedding.encode(s_valid).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
#valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat],
C.mean(),
updates=dict(
L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(CC, lr)))
#validfct = theano.function([s_valid],valid_score)
#print 'Random Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
#load(valid_embedding,'files/gutensubsetdense_exp.py_embedding.pkl')
load(embedding, 'files/gutensubsetdense_exp.py_embedding.pkl')
load(H, 'files/gutensubsetdense_exp.py_hidden.pkl')
load(L, 'files/gutensubsetdense_exp.py_logistic.pkl')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
freq_idx = cPickle.load(
open('/data/lisatmp2/rifaisal/gutten_sorted_vocab.pkl'))[:1000]
freq_idx = [hashtab[idx] for idx in freq_idx]
fname = sys.argv[0] + '_'
for e in range(hp['epoch']):
c = []
for i in range(nsent):
rsent = numpy.random.randint(nsent - 1)
nword = len(sentences[rsent])
if nword < hp['wsize'] + 2:
continue
pidx = numpy.random.randint(low=delta, high=nword - delta)
pchunk = sentences[rsent][pidx - delta:pidx + delta + rest]
nchunk = []
st = sentences[rsent][pidx - delta:pidx]
en = sentences[rsent][pidx + 1:pidx + delta + rest]
rndidx = numpy.random.randint(nsenna, size=(hp['nneg'], ))
nchunk = []
for j in range(hp['nneg']):
nchunk += en + [rndidx[j]] + st
assert len(nchunk) == len(pchunk) * hp['nneg']
#start = time.time()
p, n = (idx2mat(pchunk, nsenna), idx2mat(nchunk, nsenna))
#print 'Select row:',time.time()-start,
#start = time.time()
c.append(opt(p, n))
#print 'grad up:',time.time()-start
if i % hp['freq'] == 0:
print e, i, numpy.array(c).mean(0)
ne = knn(freq_idx,
embedding.params['weights'].get_value(borrow=True))
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
sys.stdout.flush()
open('files/' + fname + 'nearest.txt',
'w').write(display(ne, senna))
#print 'Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
idx_start = T.lscalar()
idx_stop = T.lscalar()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'))
i2w = dict( (v,k) for k,v in w2i.iteritems() )
i2w[0] = 'UNK'
senna = [ i2w[i] for i in range(len(i2w.keys())) ]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
minsize = hp['minsize']
maxsize = hp['maxsize']
dsize = maxsize - minsize +1
H.params['e_bias'] = theano.shared( numpy.array(numpy.zeros((dsize,hp['hsize'])),dtype=theano.config.floatX),name='e_bias')
path = hp['loadpath']
if path:
load(embedding,path+'/embedding.pkl')
#load(H,path+'/hidden.pkl')
#load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(borrow=True).shape[1]
#hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
H.params['e_bias'] = theano.shared( numpy.array(numpy.zeros((dsize,hp['hsize'])),dtype=theano.config.floatX),name='e_bias')
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = sp.dot(s_valid,valid_embedding.params['weights']).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_embed_left = T.concatenate([posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(posit_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
negat_embed_left = T.concatenate([negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(negat_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
posit_embed_right = T.concatenate([ T.zeros_like(posit_embed[:,:idx_start*hp['embedsize']]),
posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']]],axis=1)
negat_embed_right = T.concatenate([ T.zeros_like(negat_embed[:,:idx_start*hp['embedsize']]),
negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']]],axis=1)
posit_embed = T.concatenate([ T.zeros_like(posit_embed[:,:idx_start*hp['embedsize']]),
posit_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(posit_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
negat_embed = T.concatenate([ T.zeros_like(negat_embed[:,:idx_start*hp['embedsize']]),
negat_embed[:,idx_start*hp['embedsize']:idx_stop*hp['embedsize']],
T.zeros_like(negat_embed[:,idx_stop*hp['embedsize']:]) ],axis=1)
#posit_embed = ifelse(T.eq(idx_start, 0), posit_embed_left, posit_embed)
#posit_embed = ifelse(T.eq(idx_stop, hp['maxsize']), posit_embed_right, posit_embed)
#negat_embed = ifelse(T.eq(idx_start, 0), negat_embed_left, negat_embed)
#negat_embed = ifelse(T.eq(idx_stop, hp['maxsize']), negat_embed_right, negat_embed)
Hposit = T.tanh(T.dot(posit_embed,H.params['e_weights']) + H.params['e_bias'][idx_stop-idx_start-minsize,:])
Hnegat = T.tanh(T.dot(negat_embed,H.params['e_weights']) + H.params['e_bias'][idx_stop-idx_start-minsize,:])
posit_score = L.encode(Hposit)
negat_score = L.encode(Hnegat)
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat, idx_start, idx_stop],
(rect(C)).mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(CC,lr)) )
validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'))[:2000]
fname = ''
validsentence = []# cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/valid_debug.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(open('/mnt/scratch/bengio/bengio_group/data/gutenberg/ints_50000/split'+str(hp['split'])+'.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i,s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['maxsize'] + 2:
continue
rndsize = numpy.random.randint(low=hp['minsize']+1,high=hp['maxsize']-1)
idxsta = numpy.random.randint(low=1, high=hp['maxsize']-rndsize)
idxsto = idxsta+rndsize
print 'r',rndsize,'b',idxsta,'e',idxsto,'shape',H.params['e_bias'].get_value().shape
c =[]
r =[]
if debug:
print ' *** Processing document',i,'with',nword,
sys.stdout.flush()
for j in range(delta,nword-delta):
nd = rndsize/2
rd = rndsize%2
pchunk = s[j-delta:j+delta+rest]
nchunk = []
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for kk in range(hp['nneg']):
tmpchunk = copy.copy(pchunk)
tmpchunk[idxsta+nd] = rndidx[kk]
nchunk += tmpchunk
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
l = opt(p,n, idxsta, idxsto)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0:#(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e,i,'NN Score:', hp['score']
if not debug:
ne = knn(freq_idx,embedding.params['e_weights'].get_value(borrow=True))
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def run(jobman, debug=False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
s_valid = theano.sparse.csr_matrix()
w2i = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/merged_word2idx.pkl'
))
i2w = dict((v, k) for k, v in w2i.iteritems())
i2w[0] = 'UNK'
senna = [i2w[i] for i in range(len(i2w.keys()))]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act=identity)
H = ae(i_size=hp['embedsize'] * hp['wsize'],
h_size=hp['hsize'],
e_act=T.tanh)
L = logistic(i_size=hp['hsize'], h_size=1, act=identity)
path = hp['loadpath']
if path:
load(embedding, path + '/embedding.pkl')
load(H, path + '/hidden.pkl')
load(L, path + '/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(
borrow=True).shape[1]
hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
del H.params['d_bias']
del embedding.params['d_bias']
del embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = theano.sparse.dot(s_valid,
embedding.params['e_weights']).reshape(
(1 + hp['nneg'],
hp['embedsize'] * hp['wsize']))
#posit_embed = T.dot(s_valid, embedding.params['e_weights']).reshape((1+hp['nneg'],hp['embedsize']*hp['wsize']))
#negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
#negat_score = L.encode(H.encode(negat_embed))
C = (posit_score[1:] - posit_score[0] + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_valid], (rect(C)).mean(),
updates=dict(
L.update(CC, lr) + H.update(CC, lr) +
embedding.update_norm(CC, lr)))
def saveexp():
save(embedding, fname + 'embedding.pkl')
save(H, fname + 'hidden.pkl')
save(L, fname + 'logistic.pkl')
delta = hp['wsize'] / 2
rest = hp['wsize'] % 2
wsimtester = wsim()
freq_idx = cPickle.load(
open('/mnt/scratch/bengio/bengio_group/data/gutenberg/sorted_vocab.pkl'
))[:1000]
fname = ''
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(285)
sentences = cPickle.load(
open(
'/mnt/scratch/bengio/bengio_group/data/gutenberg/small_ints_50000/split'
+ str(hp['split']) + '.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
E = embedding.params['e_weights'].get_value(borrow=True)
seen_words = 0
for i, s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['wsize'] + 2:
continue
c = []
r = []
if debug:
print ' *** Processing document', i, 'with', nword,
sys.stdout.flush()
for j in range(delta, nword - delta):
cstart = time.time()
pchunk = s[j - delta:j + delta + rest]
nchunk = []
st = s[j - delta:j]
en = s[j + 1:j + delta + rest]
rndidx = numpy.random.randint(nsenna, size=(hp['nneg'], ))
nchunk = []
for kk in range(hp['nneg']):
nchunk += st + [rndidx[kk]] + en
#assert len(nchunk) == len(pchunk)*hp['nneg']
#p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
pn = idx2spmat(pchunk + nchunk, nsenna)
#assert pn[0,pchunk[0]] == 1
ctime = time.time() - cstart
tstart = time.time()
l = opt(pn)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if tseenwords > hp['wsimfreq'] or debug:
hp['wsim'] = wsimtester.score(
embedding.params['e_weights'].get_value(borrow=True))
print i, 'WordSim Sp Corr:', hp['wsim']
sys.stdout.flush()
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print e, i, 'NN Score:', hp['score']
tseenwords = 0
jobman.save()
if seen_words > hp['freq'] or debug:
seen_words = 0
ne = knn(freq_idx,
embedding.params['e_weights'].get_value(borrow=True))
open('files/' + fname + 'nearest.txt',
'w').write(display(ne, i2w))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def run(jobman,debug = False):
hp = jobman.state
# Symbolic variables
s_posit = T.matrix()#theano.sparse.csr_matrix()
s_negat = T.matrix()#theano.sparse.csr_matrix()
s_valid = theano.sparse.csr_matrix()
sentences = cPickle.load(open('/data/lisatmp2/rifaisal/guten_subset_idx.pkl'))
validsentence = sentences[-10:]
sentences = sentences[:-10]
senna = cPickle.load(open('/data/lisatmp2/rifaisal/senna.pkl'))
gsubset = cPickle.load(open('/data/lisatmp2/rifaisal/guten_vocab_subset.pkl')).flatten().tolist()
hashtab = dict( zip( gsubset, range( len( gsubset))))
senna = numpy.array(senna)[gsubset].tolist()
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = rect, d_act = hardtanh)
L = logistic(i_size = hp['hsize'], h_size = 1)#, act = identity)
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
#valid_embedding.params['weights'].set_value(embedding.params['weights'].get_value(borrow=True))
#valid_embedding.params['bias'].set_value(embedding.params['bias'].get_value(borrow=True))
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = embedding.encode(s_posit).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = embedding.encode(s_negat).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
#valid_embed = valid_embedding.encode(s_valid).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
#valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat],
C.mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(CC,lr)) )
#validfct = theano.function([s_valid],valid_score)
#print 'Random Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
#load(valid_embedding,'files/gutensubsetdense_exp.py_embedding.pkl')
load(embedding,'files/gutensubsetdense_exp.py_embedding.pkl')
load(H,'files/gutensubsetdense_exp.py_hidden.pkl')
load(L,'files/gutensubsetdense_exp.py_logistic.pkl')
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/data/lisatmp2/rifaisal/gutten_sorted_vocab.pkl'))[:1000]
freq_idx = [ hashtab[idx] for idx in freq_idx ]
fname = sys.argv[0]+'_'
for e in range(hp['epoch']):
c = []
for i in range(nsent):
rsent = numpy.random.randint(nsent-1)
nword = len(sentences[rsent])
if nword < hp['wsize'] + 2:
continue
pidx = numpy.random.randint(low = delta, high = nword-delta)
pchunk = sentences[rsent][pidx-delta:pidx+delta+rest]
nchunk = []
st = sentences[rsent][pidx-delta:pidx]
en = sentences[rsent][pidx+1:pidx+delta+rest]
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for j in range(hp['nneg']):
nchunk += en + [rndidx[j]] + st
assert len(nchunk) == len(pchunk)*hp['nneg']
#start = time.time()
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
#print 'Select row:',time.time()-start,
#start = time.time()
c.append(opt(p,n))
#print 'grad up:',time.time()-start
if i%hp['freq'] == 0:
print e,i, numpy.array(c).mean(0)
ne = knn(freq_idx,embedding.params['weights'].get_value(borrow=True))
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
sys.stdout.flush()
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
#print 'Valid Mean rank',evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_bow = T.matrix()
s_idx = T.iscalar()
s_tf = T.scalar()
s_posit = T.matrix()#theano.sparse.csr_matrix()
s_negat = T.matrix()#theano.sparse.csr_matrix()
sentences = cPickle.load(open('/scratch/rifaisal/data/guten/guten_subset_idx.pkl'))
senna = cPickle.load(open('/scratch/rifaisal/data/guten/senna.pkl'))
gsubset = cPickle.load(open('/scratch/rifaisal/data/guten/guten_vocab_subset.pkl')).flatten().tolist()
hashtab = dict( zip( gsubset, range( len( gsubset))))
tfidf_data = numpy.load('/scratch/rifaisal/data/guten/guten_tfidf.npy').item().tocsr().astype('float32')
#tfidf = cPickle.load(open('/scratch/rifaisal/repos/senna/gutentokenizer.pkl'))
senna = numpy.array(senna)[gsubset].tolist()
s_valid = theano.sparse.csr_matrix()
validsentence = sentences[10000:10010]
nsent = len(sentences)
nsenna = len(senna)
# Layers
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
S = logistic(i_size = hp['embedsize'], h_size = nsenna, act= T.nnet.softmax)
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
valid_embedding.params['bias'] = embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = sp.dot(s_valid,valid_embedding.params['weights']).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
s_bow_pred = S.encode(embedding.encode(s_bow))
pred = s_tf * nllsoft(s_bow_pred,s_idx)
CC = (rect(C)).mean() + hp['lambda'] * pred
opt = theano.function([s_posit, s_negat, s_bow, s_idx, s_tf],
[(rect(C)).mean(),pred],
updates = dict( S.update(CC,lr) + L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(CC,lr)) )
#validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
delta = hp['wsize']/2
rest = hp['wsize']%2
freq_idx = cPickle.load(open('/scratch/rifaisal/data/guten/gutten_sorted_vocab.pkl'))[:1000]
freq_idx = [ hashtab[idx] for idx in freq_idx ]
fname = ''
for e in range(hp['epoch']):
c = []
r = []
count = 1
for i in range(nsent):
rsent = numpy.random.randint(nsent-1)
nword = len(sentences[rsent])
if nword < hp['wsize'] + 2:
continue
pidx = numpy.random.randint(low = delta, high = nword-delta)
pchunk = sentences[rsent][pidx-delta:pidx+delta+rest]
nchunk = []
st = sentences[rsent][pidx-delta:pidx]
en = sentences[rsent][pidx+1:pidx+delta+rest]
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for j in range(hp['nneg']):
nchunk += en + [rndidx[j]] + st
assert len(nchunk) == len(pchunk)*hp['nneg']
tfidf_chunk = tfidf_data[rsent:rsent+1].toarray()
#pdb.set_trace()
tfidf_value = tfidf_chunk[0,sentences[rsent][pidx]]
tfidf_chunk[0,sentences[rsent][pidx]] = 0.
tfidx = sentences[rsent][pidx] # numpy.zeros(tfidf_chunk.shape).astype('float32')
#tfidx[0,sentences[rsent][pidx]] = 1.
p, n, b, iidx, tfval = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna), tfidf_chunk, tfidx, tfidf_value )
count += tfval!=0
l,g = opt(p,n,b, iidx, tfval)
c = c
c.append(l)
r.append(g)
"""
if (time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (i+1)%(20*hp['freq']) == 0 and debug==False:
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
"""
if (i+1)%hp['freq'] == 0 or debug:
hp['score'] = numpy.array(c).sum() / (numpy.array(c)>0).sum()
hp['pred'] = numpy.array(r).sum()/float(count)
hp['e'] = e
hp['i'] = i
print ''
print e,i,'NN Score:', hp['score'], 'Reconstruction:', hp['pred']
if debug != True:
ne = knn(freq_idx,embedding.params['e_weights'].get_value(borrow=True))
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
def run(jobman,debug = False):
expstart = time.time()
hp = jobman.state
if not os.path.exists('files/'): os.mkdir('files/')
# Symbolic variables
s_posit = T.matrix()
s_negat = T.matrix()
s_valid = theano.sparse.csr_matrix()
#vocab = cPickle.load(open('/scratch/rifaisal/data/guten/senna.pkl'))
#senna = cPickle.load(open('/scratch/rifaisal/data/wiki_april_2010/WestburyLab.wikicorp.201004_vocab30k.pkl'))
w2i = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/merged_word2idx.pkl'))
i2w = dict( (v,k) for k,v in w2i.iteritems() )
i2w[0] = 'UNK'
senna = [ i2w[i] for i in range(len(i2w.keys())) ]
nsenna = len(senna)
embedding = cae(i_size=nsenna, h_size=hp['embedsize'], e_act = identity)
H = ae(i_size = hp['embedsize']*hp['wsize'], h_size=hp['hsize'], e_act = T.tanh)
L = logistic(i_size = hp['hsize'], h_size = 1, act = identity)
path = hp['loadpath']
if path:
load(embedding,path+'/embedding.pkl')
load(H,path+'/hidden.pkl')
load(L,path+'/logistic.pkl')
hp['embedsize'] = embedding.params['e_weights'].get_value(borrow=True).shape[1]
hp['hsize'] = H.params['e_weights'].get_value(borrow=True).shape[1]
jobman.save()
valid_embedding = sparse.supervised.logistic(i_size=nsenna, h_size=hp['embedsize'], act = identity)
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
valid_embedding.params['bias'] = embedding.params['e_bias']
lr = hp['lr']
h_size = hp['hsize']
bs = hp['bs']
posit_embed = T.dot(s_posit, embedding.params['e_weights']).reshape((1,hp['embedsize']*hp['wsize']))
negat_embed = T.dot(s_negat, embedding.params['e_weights']).reshape((hp['nneg'],hp['embedsize']*hp['wsize']))
valid_embed = sp.dot(s_valid,valid_embedding.params['weights']).reshape((nsenna,hp['embedsize']*hp['wsize']))
posit_score = L.encode(H.encode(posit_embed))
negat_score = L.encode(H.encode(negat_embed))
valid_score = L.encode(H.encode(valid_embed))
C = (negat_score - posit_score.flatten() + hp['margin'])
CC = (rect(C)).mean()
opt = theano.function([s_posit, s_negat],
(rect(C)).mean(),
updates = dict( L.update(CC,lr) + H.update(CC,lr) + embedding.update_norm(CC,lr)) )
#validfct = theano.function([s_valid],valid_score)
def saveexp():
save(embedding,fname+'embedding.pkl')
save(H,fname+'hidden.pkl')
save(L,fname+'logistic.pkl')
delta = hp['wsize']/2
rest = hp['wsize']%2
#freq_idx = range(29000,30000)
freq_idx = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/sorted_vocab.pkl'))[:2000]
fname = ''
#validsentence = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/valid.pkl'))
tseenwords = not debug
for e in range(hp['epoch']):
hp['split'] = numpy.random.randint(45)
sentences = cPickle.load(open('/scratch/rifaisal/data/gutenberg_aistats/split'+str(hp['split'])+'.pkl'))
nsent = len(sentences)
bigc = []
bigr = []
seen_words = 0
for i,s in enumerate(sentences):
nword = len(s)
seen_words += nword
tseenwords += nword
if nword < hp['wsize'] + 2:
continue
c =[]
r =[]
if debug:
print ' *** Processing document',i,'with',nword,
sys.stdout.flush()
for j in range(delta,nword-delta):
pchunk = s[j-delta:j+delta+rest]
nchunk = []
st = s[j-delta:j]
en = s[j+1:j+delta+rest]
rndidx = numpy.random.randint(nsenna, size = (hp['nneg'],))
nchunk = []
for kk in range(hp['nneg']):
nchunk += st + [rndidx[kk]] + en
assert len(nchunk) == len(pchunk)*hp['nneg']
p, n = (idx2mat(pchunk,nsenna), idx2mat(nchunk,nsenna))
l = opt(p,n)
c.append(l)
if debug:
print '.',
break
if debug:
print ''
bigc += [numpy.array(c).sum()]
if 0:#(time.time() - expstart) > ( 3600 * 24 * 6 + 3600*20) or (tseenwords)>(10*hp['freq']):
tseenwords = 0
valid_embedding.params['weights'] = sp.shared(value = scipy.sparse.csr_matrix(embedding.params['e_weights'].get_value(borrow=True)))
mrk = evaluation.error(validsentence, validfct, nsenna, hp['wsize'])
hp['mrk'] = mrk
jobman.save()
saveexp()
print 'Random Valid Mean rank',mrk
if seen_words > hp['freq'] or debug:
seen_words = 0
hp['score'] = numpy.array(bigc).mean()
hp['e'] = e
hp['i'] = i
print ''
print e,i,'NN Score:', hp['score']
if not debug:
ne = knn(freq_idx,embedding.params['e_weights'].get_value(borrow=True))
open('files/'+fname+'nearest.txt','w').write(display(ne,senna))
saveexp()
sys.stdout.flush()
jobman.save()
saveexp()
