def load_model(load_states=True):
if utils.check_file(params):
model.load_params(params, ctx=ctxs)
logging.info("Loading parameters from : {}".format(params))
if load_states and utils.check_file(trainingfile):
trainer.set_learning_rate(float(
utils.read_kvstore(trainingfile)['lr']))
logging.info("Loading lr from : {}".format(trainingfile))
def gen_model(queue, rqueue, pid, model, options, k, normalize, word_idict,
sampling):
import theano
from theano import tensor
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
trng = RandomStreams(1234)
# this is zero indicate we are not using dropout in the graph
use_noise = theano.shared(numpy.float32(0.), name='use_noise')
# get the parameters
params = init_params(options)
params = load_params(model, params)
tparams = init_tparams(params)
# build the sampling computational graph
# see model.py for more detailed explanations
print "Starting to build sampler ..."
f_init, f_next = build_sampler(tparams,
options,
use_noise,
trng,
sampling=sampling)
def _gencap(cc0):
sample, score = gen_sample(tparams,
f_init,
f_next,
cc0,
options,
trng=trng,
k=k,
maxlen=200,
stochastic=False)
# adjust for length bias
if normalize:
lengths = numpy.array([len(s) for s in sample])
score = score / lengths
sidx = numpy.argmin(score)
return sample[sidx]
while True:
req = queue.get()
# exit signal
if req is None:
break
idx, context = req[0], req[1]
print pid, '-', idx
seq = _gencap(context)
rqueue.put((idx, seq))
return
def main(model, dictionary, dictionary_tag, source_file, target_file, saveto):
# load model model_options
with open('%s.pkl' % model, 'rb') as f:
options = pkl.load(f)
# load source dictionary and invert
with open(dictionary, 'rb') as f:
word_dict = pkl.load(f)
word_idict = dict()
for kk, vv in word_dict.iteritems():
word_idict[vv] = kk
word_idict[0] = '<eos>'
word_idict[1] = 'UNK'
# load tag dictionary and invert
with open(dictionary_tag, 'rb') as f:
tag_dict = pkl.load(f)
tag_idict = dict()
for kk, vv in tag_dict.iteritems():
tag_idict[vv] = kk
# allocate model parameters
params = init_params(options)
# load model parameters and set theano shared variables
params = load_params(model, params)
tparams = init_tparams(params)
trng, use_noise, \
x, x_mask, y, y_mask, \
opt_ret, \
cost, predicts = \
build_model(tparams, options)
print 'Building f_predicts...',
f_predicts = theano.function([x, x_mask], predicts)
print 'Done'
use_noise.set_value(0.)
valid_err = evaluation(f_predicts,
options,
tag_idict,
word_dict,
source_file,
saveto,
target_file,
0,
options['n_words_src'],
back_file=target_file + ".back")
print 'Test ', valid_err
def main(data_path, model_path, dict_path, save_path):
print("Preparing Data...")
# Load data and dictionary
X = []
with io.open(data_path, 'r', encoding='utf-8') as f:
for line in f:
X.append(line.rstrip('\n'))
with open(dict_path, 'rb') as f:
chardict = pkl.load(f)
n_char = len(chardict.keys()) + 1
# Prepare data for encoding
batches = Batch(X)
# Load model
print("Loading model params...")
params = load_params(model_path)
# Build encoder
print("Building encoder...")
# Theano variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# Embeddings
emb_t = tweet2vec(tweet, t_mask, params, n_char)[0]
# Theano function
f_enc = theano.function([tweet, t_mask], emb_t)
# Encode
print("Encoding data...")
print("Input data {} samples".format(len(X)))
features = np.zeros((len(X), WDIM), dtype='float32')
it = 0
for x, i in batches:
if it % 100 == 0:
print("Minibatch {}".format(it))
it += 1
xp, x_mask = prepare_data(x, chardict)
ff = f_enc(xp, x_mask)
for ind, idx in enumerate(i):
features[idx] = ff[ind]
# Save
with open(save_path, 'w') as o:
np.save(o, features)
def main(data_path, model_path, dict_path, save_path):
print("Preparing Data...")
# Load data and dictionary
X = []
with io.open(data_path,'r',encoding='utf-8') as f:
for line in f:
X.append(line.rstrip('\n'))
with open(dict_path, 'rb') as f:
chardict = pkl.load(f)
n_char = len(chardict.keys()) + 1
# Prepare data for encoding
batches = Batch(X)
# Load model
print("Loading model params...")
params = load_params(model_path)
# Build encoder
print("Building encoder...")
# Theano variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# Embeddings
emb_t = tweet2vec(tweet, t_mask, params, n_char)[0]
# Theano function
f_enc = theano.function([tweet, t_mask], emb_t)
# Encode
print("Encoding data...")
print("Input data {} samples".format(len(X)))
features = np.zeros((len(X),WDIM), dtype='float32')
it = 0
for x,i in batches:
if it % 100 == 0:
print("Minibatch {}".format(it))
it += 1
xp, x_mask = prepare_data(x, chardict)
ff = f_enc(xp, x_mask)
for ind, idx in enumerate(i):
features[idx] = ff[ind]
# Save
with open(save_path, 'w') as o:
np.save(o, features)
def gen_model(queue, rqueue, pid, model, options, k, normalize, word_idict, sampling):
import theano
from theano import tensor
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
trng = RandomStreams(1234)
# this is zero indicate we are not using dropout in the graph
use_noise = theano.shared(numpy.float32(0.), name='use_noise')
# get the parameters
params = init_params(options)
params = load_params(model, params)
tparams = init_tparams(params)
# build the sampling computational graph
# see model.py for more detailed explanations
print "Starting to build sampler ..."
f_init, f_next = build_sampler(tparams, options, use_noise, trng, sampling=sampling)
def _gencap(cc0):
sample, score = gen_sample(tparams, f_init, f_next, cc0, options,
trng=trng, k=k, maxlen=200, stochastic=False)
# adjust for length bias
if normalize:
lengths = numpy.array([len(s) for s in sample])
score = score / lengths
sidx = numpy.argmin(score)
return sample[sidx]
while True:
req = queue.get()
# exit signal
if req is None:
break
idx, context = req[0], req[1]
print pid, '-', idx
seq = _gencap(context)
rqueue.put((idx, seq))
return
def train(
dim_word=100, # word vector dimensionality
dim=1000, # the number of LSTM units
encoder='gru',
decoder='gru_cond',
patience=10,
max_epochs=5000,
dispFreq=100,
decay_c=0.,
alpha_c=0.,
diag_c=0.,
clip_c=-1.,
lrate=0.01,
n_words_src=100000,
n_words=100000,
maxlen=100, # maximum length of the description
optimizer='rmsprop',
batch_size=16,
valid_batch_size=16,
saveto='model.npz',
validFreq=1000,
saveFreq=1000, # save the parameters after every saveFreq updates
sampleFreq=100, # generate some samples after every sampleFreq updates
datasets=[
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok'
],
valid_datasets=[
'../data/dev/newstest2011.en.tok',
'../data/dev/newstest2011.fr.tok'
],
dictionaries=[
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok.pkl',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok.pkl'
],
use_dropout=False,
reload_=False):
# Model options
model_options = locals().copy()
worddicts = [None] * len(dictionaries)
worddicts_r = [None] * len(dictionaries)
for ii, dd in enumerate(dictionaries):
with open(dd, 'rb') as f:
worddicts[ii] = pkl.load(f)
worddicts_r[ii] = dict()
for kk, vv in worddicts[ii].iteritems():
worddicts_r[ii][vv] = kk
# reload options
if reload_ and os.path.exists(saveto):
with open('%s.pkl' % saveto, 'rb') as f:
models_options = pkl.load(f)
print 'Loading data'
train = TextIterator(datasets[0],
datasets[1],
dictionaries[0],
dictionaries[1],
n_words_source=n_words_src,
n_words_target=n_words,
batch_size=batch_size,
maxlen=maxlen)
valid = TextIterator(valid_datasets[0],
valid_datasets[1],
dictionaries[0],
dictionaries[1],
n_words_source=n_words_src,
n_words_target=n_words,
batch_size=valid_batch_size,
maxlen=maxlen)
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, use_noise, \
x, x_mask, y, y_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask]
print 'Buliding sampler'
f_init, f_next = build_sampler(tparams, model_options, trng)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=profile)
print 'Done'
cost = cost.mean()
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv**2).sum()
weight_decay *= decay_c
cost += weight_decay
if alpha_c > 0. and not model_options['decoder'].endswith('simple'):
alpha_c = theano.shared(numpy.float32(alpha_c), name='alpha_c')
alpha_reg = alpha_c * (
(tensor.cast(y_mask.sum(0) // x_mask.sum(0), 'float32')[:, None] -
opt_ret['dec_alphas'].sum(0))**2).sum(1).mean()
cost += alpha_reg
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=profile)
print 'Done'
print 'Computing gradient...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
print 'Done'
print 'Building f_grad...',
f_grad = theano.function(inps, grads, profile=profile)
print 'Done'
if clip_c > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(
tensor.switch(g2 > (clip_c**2), g / tensor.sqrt(g2) * clip_c,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Done'
print 'Optimization'
history_errs = []
# reload history
if reload_ and os.path.exists(saveto):
history_errs = list(numpy.load(saveto)['history_errs'])
best_p = None
bad_count = 0
if validFreq == -1:
validFreq = len(train[0]) / batch_size
if saveFreq == -1:
saveFreq = len(train[0]) / batch_size
if sampleFreq == -1:
sampleFreq = len(train[0]) / batch_size
uidx = 0
estop = False
for eidx in xrange(max_epochs):
n_samples = 0
for x, y in train:
n_samples += len(x)
uidx += 1
use_noise.set_value(1.)
x, x_mask, y, y_mask = prepare_data(x,
y,
maxlen=maxlen,
n_words_src=n_words_src,
n_words=n_words)
if x is None:
print 'Minibatch with zero sample under length ', maxlen
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, x_mask, y, y_mask)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
if best_p is not None:
params = best_p
else:
params = unzip(tparams)
numpy.savez(saveto, history_errs=history_errs, **params)
pkl.dump(model_options, open('%s.pkl' % saveto, 'wb'))
print 'Done'
if numpy.mod(uidx, sampleFreq) == 0:
# FIXME: random selection?
for jj in xrange(numpy.minimum(5, x.shape[1])):
stochastic = True
sample, score = gen_sample(tparams,
f_init,
f_next,
x[:, jj][:, None],
model_options,
trng=trng,
k=1,
maxlen=30,
stochastic=stochastic,
argmax=False)
print 'Source ', jj, ': ',
for vv in x[:, jj]:
if vv == 0:
break
if vv in worddicts_r[0]:
print worddicts_r[0][vv],
else:
print 'UNK',
print
print 'Truth ', jj, ' : ',
for vv in y[:, jj]:
if vv == 0:
break
if vv in worddicts_r[1]:
print worddicts_r[1][vv],
else:
print 'UNK',
print
print 'Sample ', jj, ': ',
if stochastic:
ss = sample
else:
score = score / numpy.array([len(s) for s in sample])
ss = sample[score.argmin()]
for vv in ss:
if vv == 0:
break
if vv in worddicts_r[1]:
print worddicts_r[1][vv],
else:
print 'UNK',
print
if numpy.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
valid_errs = pred_probs(f_log_probs, prepare_data,
model_options, valid)
valid_err = valid_errs.mean()
history_errs.append(valid_err)
if uidx == 0 or valid_err <= numpy.array(history_errs).min():
best_p = unzip(tparams)
bad_counter = 0
if len(history_errs) > patience and valid_err >= numpy.array(
history_errs)[:-patience].min():
bad_counter += 1
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
if numpy.isnan(valid_err):
import ipdb
ipdb.set_trace()
print 'Valid ', valid_err
print 'Seen %d samples' % n_samples
if estop:
break
if best_p is not None:
zipp(best_p, tparams)
use_noise.set_value(0.)
valid_err = pred_probs(f_log_probs, prepare_data, model_options,
valid).mean()
print 'Valid ', valid_err
params = copy.copy(best_p)
numpy.savez(saveto,
zipped_params=best_p,
history_errs=history_errs,
**params)
return valid_err
def main(data_path, model_path):
print("Loading data...")
with open(data_path, 'r') as f:
valX = pkl.load(f)
print("Preparing data...")
val_iter = batched_tweets.BatchedTweets(valX,
batch_size=1024,
maxlen=MAX_LENGTH)
print("Loading dictionary...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
n_char = len(chardict.keys()) + 1
# check for model files
files = sorted(glob.glob(model_path + 'model_*.npz'))
print("Found {} model files".format(len(files)))
for modelf in files:
print("Computing validation cost on {}".format(modelf))
print("Loading params...")
params = load_params(modelf)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
ptweet = T.itensor3()
ntweet = T.itensor3()
# masks
t_mask = T.fmatrix()
tp_mask = T.fmatrix()
tn_mask = T.fmatrix()
# Embeddings
emb_t = tweet2vec(tweet, t_mask, params, n_char)[0]
emb_tp = tweet2vec(ptweet, tp_mask, params, n_char)[0]
emb_tn = tweet2vec(ntweet, tn_mask, params, n_char)[0]
# batch cost
D1 = 1 - T.batched_dot(emb_t, emb_tp) / (tnorm(emb_t) * tnorm(emb_tp))
D2 = 1 - T.batched_dot(emb_t, emb_tn) / (tnorm(emb_t) * tnorm(emb_tn))
gap = D1 - D2 + M
loss = gap * (gap > 0)
cost = T.mean(loss)
# Theano function
print("Compiling theano function...")
inps = [tweet, t_mask, ptweet, tp_mask, ntweet, tn_mask]
cost_val = theano.function(inps, cost)
print("Testing...")
uidx = 0
try:
validation_cost = 0.
n_val_samples = 0
for x, y, z in val_iter:
if not x:
print("Validation: Minibatch with no valid triples")
continue
n_val_samples += len(x)
x, x_m, y, y_m, z, z_m = batched_tweets.prepare_data(
x, y, z, chardict, maxlen=MAX_LENGTH, n_chars=n_char)
if x == None:
print(
"Validation: Minibatch with zero samples under maxlength"
)
continue
curr_cost = cost_val(x, x_m, y, y_m, z, z_m)
validation_cost += curr_cost * len(x)
print("Model {} Validation Cost {}".format(
modelf, validation_cost / n_val_samples))
print("Seen {} samples.".format(n_val_samples))
except KeyboardInterrupt:
pass
def main(data_path, model_path):
print("Loading data...")
with open(data_path,'r') as f:
valX = pkl.load(f)
print("Preparing data...")
val_iter = batched_tweets.BatchedTweets(valX, batch_size=512, maxlen=MAX_LENGTH)
print("Loading dictionary...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
n_char = len(chardict.keys()) + 1
# check for model files
files = sorted(glob.glob(model_path+'model_*.npz'))
print("Found {} model files".format(len(files)))
for modelf in files:
print("Computing validation cost on {}".format(modelf))
print("Loading params...")
params = load_params(modelf)
print("Building network...")
# Tweet variables
tweet = T.itensor4()
ptweet = T.itensor4()
ntweet = T.itensor4()
# masks
t_mask = T.ftensor3()
tp_mask = T.ftensor3()
tn_mask = T.ftensor3()
# Embeddings
emb_t = char2word2vec(tweet, t_mask, params, n_char)[0]
emb_tp = char2word2vec(ptweet, tp_mask, params, n_char)[0]
emb_tn = char2word2vec(ntweet, tn_mask, params, n_char)[0]
# batch cost
D1 = 1 - T.batched_dot(emb_t, emb_tp)/(tnorm(emb_t)*tnorm(emb_tp))
D2 = 1 - T.batched_dot(emb_t, emb_tn)/(tnorm(emb_t)*tnorm(emb_tn))
gap = D1-D2+M
loss = gap*(gap>0)
cost = T.mean(loss)
reg = REGULARIZATION*lasagne.regularization.regularize_network_params(char2word2vec(tweet, t_mask, params, n_char)[1], lasagne.regularization.l2) + REGULARIZATION*lasagne.regularization.regularize_network_params(char2word2vec(tweet, t_mask, params, n_char)[2], lasagne.regularization.l2)
# Theano function
print("Compiling theano function...")
inps = [tweet,t_mask,ptweet,tp_mask,ntweet,tn_mask]
cost_val = theano.function(inps,cost)
reg_val = theano.function([], reg)
print("Testing...")
uidx = 0
try:
validation_cost = 0.
reg_cost = 0.
n_val_samples = 0
for x,y,z in val_iter:
if not x:
print("Validation: Minibatch with no valid triples")
continue
n_val_samples += len(x)
x, x_m, y, y_m, z, z_m = batched_tweets.prepare_data_c2w2s(x, y, z, chardict, maxwordlen=MAX_WORD_LENGTH, maxseqlen=MAX_SEQ_LENGTH, n_chars=n_char)
if x==None:
print("Validation: Minibatch with zero samples under maxlength")
continue
curr_cost = cost_val(x,x_m,y,y_m,z,z_m)
validation_cost += curr_cost*len(x)
reg_cost = reg_val()
print("Model {} Validation Cost {} Regularization Cost {}".format(modelf, validation_cost/n_val_samples, reg_cost))
print("Seen {} samples.".format(n_val_samples))
except KeyboardInterrupt:
pass
def train(dim_word=100, # word vector dimensionality
dim=1000, # the number of LSTM units
encoder='gru',
decoder='gru_cond',
patience=10,
max_epochs=5000,
dispFreq=100,
decay_c=0.,
alpha_c=0.,
diag_c=0.,
clip_c=-1.,
lrate=0.01,
n_words_src=100000,
n_words=100000,
maxlen=100, # maximum length of the description
optimizer='rmsprop',
batch_size = 16,
valid_batch_size = 16,
saveto='model.npz',
validFreq=1000,
saveFreq=1000, # save the parameters after every saveFreq updates
sampleFreq=100, # generate some samples after every sampleFreq updates
datasets=['/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok'],
valid_datasets=['../data/dev/newstest2011.en.tok', '../data/dev/newstest2011.fr.tok'],
dictionaries=['/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok.pkl',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok.pkl'],
use_dropout=False,
reload_=False):
# Model options
model_options = locals().copy()
worddicts = [None] * len(dictionaries)
worddicts_r = [None] * len(dictionaries)
for ii, dd in enumerate(dictionaries):
with open(dd, 'rb') as f:
worddicts[ii] = pkl.load(f)
worddicts_r[ii] = dict()
for kk, vv in worddicts[ii].iteritems():
worddicts_r[ii][vv] = kk
# reload options
if reload_ and os.path.exists(saveto):
with open('%s.pkl' % saveto, 'rb') as f:
models_options = pkl.load(f)
print 'Loading data'
train = TextIterator(datasets[0], datasets[1],
dictionaries[0], dictionaries[1],
n_words_source=n_words_src, n_words_target=n_words,
batch_size=batch_size,
maxlen=maxlen)
valid = TextIterator(valid_datasets[0], valid_datasets[1],
dictionaries[0], dictionaries[1],
n_words_source=n_words_src, n_words_target=n_words,
batch_size=valid_batch_size,
maxlen=maxlen)
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(saveto):
params = load_params(saveto, params)
tparams = init_tparams(params)
trng, use_noise, \
x, x_mask, y, y_mask, \
opt_ret, \
cost = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask]
print 'Buliding sampler'
f_init, f_next = build_sampler(tparams, model_options, trng)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=profile)
print 'Done'
cost = cost.mean()
if decay_c > 0.:
decay_c = theano.shared(numpy.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
if alpha_c > 0. and not model_options['decoder'].endswith('simple'):
alpha_c = theano.shared(numpy.float32(alpha_c), name='alpha_c')
alpha_reg = alpha_c * ((tensor.cast(y_mask.sum(0)//x_mask.sum(0), 'float32')[:,None]-
opt_ret['dec_alphas'].sum(0))**2).sum(1).mean()
cost += alpha_reg
# after any regularizer
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=profile)
print 'Done'
print 'Computing gradient...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
print 'Done'
print 'Building f_grad...',
f_grad = theano.function(inps, grads, profile=profile)
print 'Done'
if clip_c > 0.:
g2 = 0.
for g in grads:
g2 += (g**2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (clip_c**2),
g / tensor.sqrt(g2) * clip_c,
g))
grads = new_grads
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Done'
print 'Optimization'
history_errs = []
# reload history
if reload_ and os.path.exists(saveto):
history_errs = list(numpy.load(saveto)['history_errs'])
best_p = None
bad_count = 0
if validFreq == -1:
validFreq = len(train[0])/batch_size
if saveFreq == -1:
saveFreq = len(train[0])/batch_size
if sampleFreq == -1:
sampleFreq = len(train[0])/batch_size
uidx = 0
estop = False
for eidx in xrange(max_epochs):
n_samples = 0
for x, y in train:
n_samples += len(x)
uidx += 1
use_noise.set_value(1.)
x, x_mask, y, y_mask = prepare_data(x, y, maxlen=maxlen,
n_words_src=n_words_src,
n_words=n_words)
if x is None:
print 'Minibatch with zero sample under length ', maxlen
uidx -= 1
continue
ud_start = time.time()
cost = f_grad_shared(x, x_mask, y, y_mask)
f_update(lrate)
ud = time.time() - ud_start
if numpy.isnan(cost) or numpy.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
if numpy.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
if numpy.mod(uidx, saveFreq) == 0:
print 'Saving...',
if best_p is not None:
params = best_p
else:
params = unzip(tparams)
numpy.savez(saveto, history_errs=history_errs, **params)
pkl.dump(model_options, open('%s.pkl'%saveto, 'wb'))
print 'Done'
if numpy.mod(uidx, sampleFreq) == 0:
# FIXME: random selection?
for jj in xrange(numpy.minimum(5,x.shape[1])):
stochastic = True
sample, score = gen_sample(tparams, f_init, f_next, x[:,jj][:,None],
model_options, trng=trng, k=1, maxlen=30,
stochastic=stochastic, argmax=False)
print 'Source ', jj, ': ',
for vv in x[:, jj]:
if vv == 0:
break
if vv in worddicts_r[0]:
print worddicts_r[0][vv],
else:
print 'UNK',
print
print 'Truth ', jj, ' : ',
for vv in y[:, jj]:
if vv == 0:
break
if vv in worddicts_r[1]:
print worddicts_r[1][vv],
else:
print 'UNK',
print
print 'Sample ', jj, ': ',
if stochastic:
ss = sample
else:
score = score / numpy.array([len(s) for s in sample])
ss = sample[score.argmin()]
for vv in ss:
if vv == 0:
break
if vv in worddicts_r[1]:
print worddicts_r[1][vv],
else:
print 'UNK',
print
if numpy.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
valid_errs = pred_probs(f_log_probs, prepare_data, model_options, valid)
valid_err = valid_errs.mean()
history_errs.append(valid_err)
if uidx == 0 or valid_err <= numpy.array(history_errs).min():
best_p = unzip(tparams)
bad_counter = 0
if len(history_errs) > patience and valid_err >= numpy.array(history_errs)[:-patience].min():
bad_counter += 1
if bad_counter > patience:
print 'Early Stop!'
estop = True
break
if numpy.isnan(valid_err):
import ipdb; ipdb.set_trace()
print 'Valid ', valid_err
print 'Seen %d samples' % n_samples
if estop:
break
if best_p is not None:
zipp(best_p, tparams)
use_noise.set_value(0.)
valid_err = pred_probs(f_log_probs, prepare_data, model_options, valid).mean()
print 'Valid ', valid_err
params = copy.copy(best_p)
numpy.savez(saveto, zipped_params=best_p,
history_errs=history_errs,
**params)
return valid_err
trainer.step(1)
total_L += mx.nd.sum(L).asscalar()
if i % args.log_interval == 0 and i > 0:
cur_L = total_L / args.log_interval
print('[Epoch %d Batch %d] loss %.2f, ppl %.2f' %
(epoch, i, cur_L, math.exp(cur_L)))
total_L = 0.0
val_L = eval(val_data)
print('[Epoch %d] time cost %.2fs, valid loss %.2f, valid ppl %.2f' %
(epoch, time.time() - start_time, val_L, math.exp(val_L)))
if val_L < best_val:
best_val = val_L
test_L = eval(test_data)
model.save_params(args.save)
print('test loss %.2f, test ppl %.2f' % (test_L, math.exp(test_L)))
else:
args.lr = args.lr * 0.25
trainer.set_learning_rate(args.lr)
if __name__ == '__main__':
train()
model.load_params(args.save, context)
test_L = eval(test_data)
print('Best test loss %.2f, test ppl %.2f' % (test_L, math.exp(test_L)))
def __init__(self,
model_dir,
num_channels,
shape_z,
shape_y,
shape_scale=5,
num_maps=1,
batch_size=1,
tf_graph=None,
tf_sess=None,
debug_plot=False):
"""
Setup model for inference
Args:
model_dir: Directory with model files
num_channels: Number of channels for input data
shape_z: Shape of input data in Z
shape_y: Shape of input data in Y
shape_scale: Scale data with center k-space data
num_maps: Number of sets of sensitivity maps
"""
self.debug_plot = debug_plot
self.tf_graph = tf_graph
if self.tf_graph is None:
self.tf_graph = tf.Graph()
self.tf_sess = tf_sess
if self.tf_sess is None:
session_config = tf.ConfigProto()
session_config.gpu_options.allow_growth = True # pylint: disable=E1101
session_config.allow_soft_placement = True
self.tf_sess = tf.Session(graph=self.tf_graph,
config=session_config)
params = model.load_params(model_dir)
with self.tf_graph.as_default():
self.batch_size = batch_size
self.tf_kspace_input = tf.placeholder(
tf.complex64,
(self.batch_size, shape_z, shape_y, num_channels))
self.tf_sensemap_input = tf.placeholder(
tf.complex64,
(self.batch_size, shape_z, shape_y, num_maps, num_channels))
if shape_scale > 0:
scale = tf.image.resize_image_with_crop_or_pad(
self.tf_kspace_input, shape_scale, shape_scale)
scale = tf.reduce_mean(tf.square(tf.abs(scale)))
scale *= shape_scale * shape_scale / shape_y / shape_z
else:
logger.info('Turning off scaling...')
scale = 1.0
scale = tf.cast(1.0 / tf.sqrt(scale), dtype=tf.complex64)
tf_kspace_input_scaled = self.tf_kspace_input * scale
tf_image_output_scaled, tf_kspace_output_scaled, self.iter_out = model.unrolled_prox(
tf_kspace_input_scaled,
self.tf_sensemap_input,
num_grad_steps=params['unrolled_steps'],
resblock_num_features=params['unrolled_num_features'],
resblock_num_blocks=params['unrolled_num_resblocks'],
resblock_share=params['unrolled_share'],
training=False,
hard_projection=params['hard_projection'],
scope=params['recon_scope'])
self.tf_image_output = tf_image_output_scaled / scale
self.tf_kspace_output = tf_kspace_output_scaled / scale
if params['loss_adv'] > 0:
adv_scope = 'Adversarial'
tf_image_input_scaled = tfmri.model_transpose(
tf_kspace_input_scaled, self.tf_sensemap_input)
self.adv_output = model.adversarial(tf_image_input_scaled,
training=False,
scope=adv_scope)
else:
self.adv_output = None
filename_latest_model = tf.train.latest_checkpoint(model_dir)
logger.info('Loading model ({})...'.format(filename_latest_model))
saver = tf.train.Saver()
saver.restore(self.tf_sess, filename_latest_model)
