def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args) > 3:
m_num = int(args[3])
print ("Preparing Data...")
# Test data
Xt = []
yt = []
with io.open(data_path, "r", encoding="utf-8") as f:
for line in f:
(yc, Xc) = line.rstrip("\n").split("\t")
Xt.append(Xc)
yt.append(yc.split(","))
# Model
print ("Loading model params...")
if len(args) > 3:
print "Loading %s/model_%d.npz" % (model_path, m_num)
params = load_params("%s/model_%d.npz" % (model_path, m_num))
else:
print "Loading %s/best_model.npz" % model_path
params = load_params("%s/best_model.npz" % model_path)
print ("Loading dictionaries...")
with open("%s/dict.pkl" % model_path, "rb") as f:
chardict = pkl.load(f)
with open("%s/label_dict.pkl" % model_path, "rb") as f:
labeldict = pkl.load(f)
n_char = min(len(chardict.keys()) + 1, N_WORD)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# iterators
test_iter = batch.BatchTweets(Xt, yt, labeldict, batch_size=N_BATCH, max_classes=MAX_CLASSES, test=True)
print ("Building network...")
# Tweet variables
tweet = T.itensor3()
targets = T.imatrix()
# masks
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes, n_char)
# Theano function
print ("Compiling theano functions...")
predict = theano.function([tweet, t_mask], predictions)
encode = theano.function([tweet, t_mask], embeddings)
# Test
print ("Testing...")
out_data = []
out_pred = []
out_emb = []
out_target = []
for xr, y in test_iter:
x, x_m = batch.prepare_data(xr, chardict, n_tokens=n_char)
p = predict(x, x_m)
e = encode(x, x_m)
ranks = np.argsort(p)[:, ::-1]
for idx, item in enumerate(xr):
out_data.append(item)
out_pred.append(ranks[idx, :])
out_emb.append(e[idx, :])
out_target.append(y[idx])
# Save
print ("Saving...")
with open("%s/data.pkl" % save_path, "w") as f:
pkl.dump(out_data, f)
with open("%s/predictions.npy" % save_path, "w") as f:
np.save(f, np.asarray(out_pred))
with open("%s/embeddings.npy" % save_path, "w") as f:
np.save(f, np.asarray(out_emb))
with open("%s/targets.pkl" % save_path, "w") as f:
pkl.dump(out_target, f)
def main(train_path,val_path,save_path,wordvec=None,num_epochs=NUM_EPOCHS):
global T1
# save settings
shutil.copyfile('src/settings_word.py','%s/settings_word.txt'%save_path)
print("Preparing Data...")
# Training data
Xt = []
yt = []
with io.open(train_path,'r',encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xt.append(Xc)
yt.append(yc)
# Validation data
Xv = []
yv = []
with io.open(val_path,'r',encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xv.append(Xc)
yv.append(yc.split(','))
print("Preparing Model...")
if not RELOAD_MODEL:
# Build dictionaries from training data
tokendict, tokencount = batch.build_dictionary(Xt)
n_token = min(len(tokendict.keys()) + 1, N_WORD)
batch.save_dictionary(tokendict,tokencount,'%s/dict.pkl' % save_path)
# params
if wordvec is None:
params = init_params(n_chars=n_token)
else:
params = init_params_add_wordvec(tokendict, n_chars=n_token, vector_bin=wordvec)
labeldict, labelcount = batch.build_label_dictionary(yt)
batch.save_dictionary(labeldict, labelcount, '%s/label_dict.pkl' % save_path)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# classification params
params['W_cl'] = theano.shared(np.random.normal(loc=0., scale=SCALE, size=(2*WDIM,n_classes)).astype('float32'), name='W_cl')
params['b_cl'] = theano.shared(np.zeros((n_classes)).astype('float32'), name='b_cl')
else:
print("Loading model params...")
params = load_params_shared('%s/best_model.npz' % save_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % save_path, 'rb') as f:
tokendict = pkl.load(f)
with open('%s/label_dict.pkl' % save_path, 'rb') as f:
labeldict = pkl.load(f)
n_token = min(len(tokendict.keys()) + 1, N_WORD)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# iterators
train_iter = batch.BatchTweets(Xt, yt, labeldict, batch_size=N_BATCH, max_classes=MAX_CLASSES)
val_iter = batch.BatchTweets(Xv, yv, labeldict, batch_size=N_BATCH, max_classes=MAX_CLASSES, test=True)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
targets = T.ivector()
# masks
t_mask = T.fmatrix()
# network for prediction
predictions, net, emb = classify(tweet, t_mask, params, n_classes, n_token)
# batch loss
loss = lasagne.objectives.categorical_crossentropy(predictions, targets)
cost = T.mean(loss) + REGULARIZATION*lasagne.regularization.regularize_network_params(net, lasagne.regularization.l2)
cost_only = T.mean(loss)
reg_only = REGULARIZATION*lasagne.regularization.regularize_network_params(net, lasagne.regularization.l2)
# params and updates
print("Computing updates...")
global SCHEDULE
lr = LEARNING_RATE
if ADADELTA:
SCHEDULE = False
updates = lasagne.updates.adadelta(cost, lasagne.layers.get_all_params(net), learning_rate=lr)
else:
mu = MOMENTUM
updates = lasagne.updates.nesterov_momentum(cost, lasagne.layers.get_all_params(net), lr, momentum=mu)
# Theano function
print("Compiling theano functions...")
inps = [tweet,t_mask,targets]
predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet,t_mask],emb)
cost_val = theano.function(inps,[cost_only,emb])
train = theano.function(inps,cost,updates=updates)
reg_val = theano.function([],reg_only)
# Training
print("Training...")
uidx = 0
maxp = 0.
start = time.time()
valcosts = []
try:
for epoch in range(num_epochs):
n_samples = 0
train_cost = 0.
print("Epoch {}".format(epoch))
# learning schedule
if len(valcosts) > 1 and SCHEDULE:
change = (valcosts[-1]-valcosts[-2])/abs(valcosts[-2])
if change < T1:
lr, mu = schedule(lr, mu)
updates = lasagne.updates.nesterov_momentum(cost, lasagne.layers.get_all_params(net), lr, momentum=mu)
train = theano.function(inps,cost,updates=updates)
T1 = T1/2
# stopping criterion
if len(valcosts) > 6:
deltas = []
for i in range(5):
deltas.append((valcosts[-i-1]-valcosts[-i-2])/abs(valcosts[-i-2]))
if sum(deltas)/len(deltas) < T2:
break
ud_start = time.time()
for xr,y in train_iter:
n_samples +=len(xr)
uidx += 1
x, x_m = batch.prepare_data(xr, tokendict, n_tokens=n_token)
if x is None:
print("Minibatch with zero samples under maxlength.")
uidx -= 1
continue
curr_cost = train(x,x_m,y)
train_cost += curr_cost*len(xr)
ud = time.time() - ud_start
if np.isnan(curr_cost) or np.isinf(curr_cost):
print("Nan detected.")
return
if np.mod(uidx, DISPF) == 0:
print("Epoch {} Update {} Cost {} Time {}".format(epoch,uidx,curr_cost,ud))
sys.stdout.flush()
if np.mod(uidx,SAVEF) == 0:
print("Saving...")
saveparams = OrderedDict()
for kk,vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/model.npz' % save_path,**saveparams)
print("Done.")
print("Testing on Validation set...")
preds = []
targs = []
for xr,y in val_iter:
x, x_m = batch.prepare_data(xr, tokendict, n_tokens=n_token)
if x is None:
print("Validation: Minibatch with zero samples under maxlength.")
continue
vp = predict(x,x_m)
ranks = np.argsort(vp)[:,::-1]
for idx,item in enumerate(xr):
preds.append(ranks[idx,:])
targs.append(y[idx])
validation_cost = precision(np.asarray(preds),targs,1)
regularization_cost = reg_val()
if validation_cost > maxp:
maxp = validation_cost
saveparams = OrderedDict()
for kk,vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/best_model.npz' % (save_path),**saveparams)
print("Epoch {} Training Cost {} Validation Precision {} Regularization Cost {} Max Precision {}".format(epoch, train_cost/n_samples, validation_cost, regularization_cost, maxp))
print("Seen {} samples.".format(n_samples))
valcosts.append(validation_cost)
print("Saving...")
saveparams = OrderedDict()
for kk,vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/model_%d.npz' % (save_path,epoch),**saveparams)
print("Done.")
except KeyboardInterrupt:
pass
print("Total training time = {}".format(time.time()-start))
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args)>3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
with io.open(data_path,'r',encoding='utf-8') as f:
for line in f:
Xc = line.rstrip('\n')
Xt.append(Xc)
# Model
print("Loading model params...")
if len(args)>3:
params = load_params('%s/model_%d.npz' % (model_path,m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = min(len(chardict.keys()) + 1, N_WORD)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes, n_char)
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet,t_mask],predictions)
encode = theano.function([tweet,t_mask],embeddings)
# Test
print("Encoding...")
out_pred = []
out_emb = []
numbatches = len(Xt)/N_BATCH + 1
for i in range(numbatches):
xr = Xt[N_BATCH*i:N_BATCH*(i+1)]
x, x_m = batch.prepare_data(xr, chardict, n_tokens=n_char)
p = predict(x,x_m)
e = encode(x,x_m)
ranks = np.argsort(p)[:,::-1]
for idx, item in enumerate(xr):
out_pred.append(' '.join([inverse_labeldict[r] for r in ranks[idx,:5]]))
out_emb.append(e[idx,:])
# Save
print("Saving...")
with io.open('%s/predicted_tags.txt'%save_path,'w') as f:
for item in out_pred:
f.write(item + '\n')
with open('%s/embeddings.npy'%save_path,'w') as f:
np.save(f,np.asarray(out_emb))
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args) > 3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
with io.open(data_path, 'r', encoding='utf-8') as f:
for line in f:
Xc = line.rstrip('\n')
Xt.append(Xc)
# Model
print("Loading model params...")
if len(args) > 3:
params = load_params('%s/model_%d.npz' % (model_path, m_num))
else:
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = min(len(chardict.keys()) + 1, N_WORD)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
inverse_labeldict = invert(labeldict)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes,
n_char)
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet, t_mask], predictions)
encode = theano.function([tweet, t_mask], embeddings)
# Test
print("Encoding...")
out_pred = []
out_emb = []
numbatches = len(Xt) / N_BATCH + 1
for i in range(numbatches):
xr = Xt[N_BATCH * i:N_BATCH * (i + 1)]
x, x_m = batch.prepare_data(xr, chardict, n_tokens=n_char)
p = predict(x, x_m)
e = encode(x, x_m)
ranks = np.argsort(p)[:, ::-1]
for idx, item in enumerate(xr):
out_pred.append(' '.join(
[inverse_labeldict[r] for r in ranks[idx, :5]]))
out_emb.append(e[idx, :])
# Save
print("Saving...")
with io.open('%s/predicted_tags.txt' % save_path, 'w') as f:
for item in out_pred:
f.write(item + '\n')
with open('%s/embeddings.npy' % save_path, 'w') as f:
np.save(f, np.asarray(out_emb))
def main(args):
data_path = args[0]
model_path = args[1]
save_path = args[2]
if len(args) > 3:
m_num = int(args[3])
print("Preparing Data...")
# Test data
Xt = []
yt = []
with io.open(data_path, 'r', encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xt.append(Xc)
yt.append(yc.split(','))
# Model
print("Loading model params...")
if len(args) > 3:
print 'Loading %s/model_%d.npz' % (model_path, m_num)
params = load_params('%s/model_%d.npz' % (model_path, m_num))
else:
print 'Loading %s/best_model.npz' % model_path
params = load_params('%s/best_model.npz' % model_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % model_path, 'rb') as f:
chardict = pkl.load(f)
with open('%s/label_dict.pkl' % model_path, 'rb') as f:
labeldict = pkl.load(f)
n_char = min(len(chardict.keys()) + 1, N_WORD)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# iterators
test_iter = batch.BatchTweets(Xt,
yt,
labeldict,
batch_size=N_BATCH,
max_classes=MAX_CLASSES,
test=True)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
targets = T.imatrix()
# masks
t_mask = T.fmatrix()
# network for prediction
predictions, embeddings = classify(tweet, t_mask, params, n_classes,
n_char)
# Theano function
print("Compiling theano functions...")
predict = theano.function([tweet, t_mask], predictions)
encode = theano.function([tweet, t_mask], embeddings)
# Test
print("Testing...")
out_data = []
out_pred = []
out_emb = []
out_target = []
for xr, y in test_iter:
x, x_m = batch.prepare_data(xr, chardict, n_tokens=n_char)
p = predict(x, x_m)
e = encode(x, x_m)
ranks = np.argsort(p)[:, ::-1]
for idx, item in enumerate(xr):
out_data.append(item)
out_pred.append(ranks[idx, :])
out_emb.append(e[idx, :])
out_target.append(y[idx])
# Save
print("Saving...")
with open('%s/data.pkl' % save_path, 'w') as f:
pkl.dump(out_data, f)
with open('%s/predictions.npy' % save_path, 'w') as f:
np.save(f, np.asarray(out_pred))
with open('%s/embeddings.npy' % save_path, 'w') as f:
np.save(f, np.asarray(out_emb))
with open('%s/targets.pkl' % save_path, 'w') as f:
pkl.dump(out_target, f)
def main(train_path, val_path, save_path, num_epochs=NUM_EPOCHS):
global T1
# save settings
shutil.copyfile('settings_word.py', '%s/settings_word.txt' % save_path)
print("Preparing Data...")
# Training data
Xt = []
yt = []
with io.open(train_path, 'r', encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xt.append(Xc)
yt.append(yc)
# Validation data
Xv = []
yv = []
with io.open(val_path, 'r', encoding='utf-8') as f:
for line in f:
(yc, Xc) = line.rstrip('\n').split('\t')
Xv.append(Xc)
yv.append(yc.split(','))
print("Preparing Model...")
if not RELOAD_MODEL:
# Build dictionaries from training data
tokendict, tokencount = batch.build_dictionary(Xt)
n_token = min(len(tokendict.keys()) + 1, N_WORD)
batch.save_dictionary(tokendict, tokencount, '%s/dict.pkl' % save_path)
# params
params = init_params(n_chars=n_token)
labeldict, labelcount = batch.build_label_dictionary(yt)
batch.save_dictionary(labeldict, labelcount,
'%s/label_dict.pkl' % save_path)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# classification params
params['W_cl'] = theano.shared(np.random.normal(
loc=0., scale=SCALE, size=(WDIM, n_classes)).astype('float32'),
name='W_cl')
params['b_cl'] = theano.shared(np.zeros((n_classes)).astype('float32'),
name='b_cl')
else:
print("Loading model params...")
params = load_params_shared('%s/best_model.npz' % save_path)
print("Loading dictionaries...")
with open('%s/dict.pkl' % save_path, 'rb') as f:
tokendict = pkl.load(f)
with open('%s/label_dict.pkl' % save_path, 'rb') as f:
labeldict = pkl.load(f)
n_token = min(len(tokendict.keys()) + 1, N_WORD)
n_classes = min(len(labeldict.keys()) + 1, MAX_CLASSES)
# iterators
train_iter = batch.BatchTweets(Xt,
yt,
labeldict,
batch_size=N_BATCH,
max_classes=MAX_CLASSES)
val_iter = batch.BatchTweets(Xv,
yv,
labeldict,
batch_size=N_BATCH,
max_classes=MAX_CLASSES,
test=True)
print("Building network...")
# Tweet variables
tweet = T.itensor3()
targets = T.ivector()
# masks
t_mask = T.fmatrix()
# network for prediction
predictions, net, emb = classify(tweet, t_mask, params, n_classes, n_token)
# batch loss
loss = lasagne.objectives.categorical_crossentropy(predictions, targets)
cost = T.mean(
loss
) + REGULARIZATION * lasagne.regularization.regularize_network_params(
net, lasagne.regularization.l2)
cost_only = T.mean(loss)
reg_only = REGULARIZATION * lasagne.regularization.regularize_network_params(
net, lasagne.regularization.l2)
# params and updates
print("Computing updates...")
lr = LEARNING_RATE
mu = MOMENTUM
updates = lasagne.updates.nesterov_momentum(
cost, lasagne.layers.get_all_params(net), lr, momentum=mu)
# Theano function
print("Compiling theano functions...")
inps = [tweet, t_mask, targets]
predict = theano.function([tweet, t_mask], predictions)
encode = theano.function([tweet, t_mask], emb)
cost_val = theano.function(inps, [cost_only, emb])
train = theano.function(inps, cost, updates=updates)
reg_val = theano.function([], reg_only)
# Training
print("Training...")
uidx = 0
maxp = 0.
start = time.time()
valcosts = []
try:
for epoch in range(num_epochs):
n_samples = 0
train_cost = 0.
print("Epoch {}".format(epoch))
# learning schedule
if len(valcosts) > 1 and SCHEDULE:
change = (valcosts[-1] - valcosts[-2]) / abs(valcosts[-2])
if change < T1:
lr, mu = schedule(lr, mu)
updates = lasagne.updates.nesterov_momentum(
cost,
lasagne.layers.get_all_params(net),
lr,
momentum=mu)
train = theano.function(inps, cost, updates=updates)
T1 = T1 / 2
# stopping criterion
if len(valcosts) > 6:
deltas = []
for i in range(5):
deltas.append((valcosts[-i - 1] - valcosts[-i - 2]) /
abs(valcosts[-i - 2]))
if sum(deltas) / len(deltas) < T2:
break
ud_start = time.time()
for xr, y in train_iter:
n_samples += len(xr)
uidx += 1
x, x_m = batch.prepare_data(xr, tokendict, n_tokens=n_token)
if x == None:
print("Minibatch with zero samples under maxlength.")
uidx -= 1
continue
curr_cost = train(x, x_m, y)
train_cost += curr_cost * len(xr)
ud = time.time() - ud_start
if np.isnan(curr_cost) or np.isinf(curr_cost):
print("Nan detected.")
return
if np.mod(uidx, DISPF) == 0:
print("Epoch {} Update {} Cost {} Time {}".format(
epoch, uidx, curr_cost, ud))
if np.mod(uidx, SAVEF) == 0:
print("Saving...")
saveparams = OrderedDict()
for kk, vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/model.npz' % save_path, **saveparams)
print("Done.")
print("Testing on Validation set...")
preds = []
targs = []
for xr, y in val_iter:
x, x_m = batch.prepare_data(xr, tokendict, n_tokens=n_token)
if x == None:
print(
"Validation: Minibatch with zero samples under maxlength."
)
continue
vp = predict(x, x_m)
ranks = np.argsort(vp)[:, ::-1]
for idx, item in enumerate(xr):
preds.append(ranks[idx, :])
targs.append(y[idx])
validation_cost = precision(np.asarray(preds), targs, 1)
regularization_cost = reg_val()
if validation_cost > maxp:
maxp = validation_cost
saveparams = OrderedDict()
for kk, vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/best_model.npz' % (save_path), **saveparams)
print(
"Epoch {} Training Cost {} Validation Precision {} Regularization Cost {} Max Precision {}"
.format(epoch, train_cost / n_samples, validation_cost,
regularization_cost, maxp))
print("Seen {} samples.".format(n_samples))
valcosts.append(validation_cost)
print("Saving...")
saveparams = OrderedDict()
for kk, vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/model_%d.npz' % (save_path, epoch), **saveparams)
print("Done.")
except KeyboardInterrupt:
pass
print("Total training time = {}".format(time.time() - start))
