def main(data_path,save_path,num_epochs=NUM_EPOCHS):
print("Preparing Data...")
# Training data
with open(data_path,'r') as f:
X = f.read().splitlines()
# Build dictionary
chardict, charcount = batched_tweets.build_dictionary(X)
batched_tweets.save_dictionary(chardict,charcount,'%s/dict.pkl' % save_path)
trainX = batched_tweets.grouper(X)
train_iter = batched_tweets.BatchedTweets(trainX, validation_size=N_VAL, batch_size=N_BATCH, maxlen=MAX_LENGTH)
# Validation set
t_val, tp_val, tn_val = train_iter.validation_set()
t_val, t_val_m, tp_val, tp_val_m, tn_val, tn_val_m = batched_tweets.prepare_data(t_val, tp_val, tn_val, chardict, maxlen=MAX_LENGTH)
print("Building network...")
# params
n_char = len(chardict.keys()) + 1
params = init_params(n_chars=n_char)
# 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)
emb_tp = tweet2vec(ptweet, tp_mask, params)
emb_tn = tweet2vec(ntweet, tn_mask, params)
# batch loss
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.sum(loss)
# params and updates
print("Computing updates...")
updates = lasagne.updates.adagrad(cost, params.values(), LEARNING_RATE)
# Theano function
print("Compiling theano functions...")
inps = [tweet,t_mask,ptweet,tp_mask,ntweet,tn_mask]
dist = theano.function(inps,[D1,D2])
l = theano.function(inps,loss)
t2v = theano.function(inps,[emb_t,emb_tp,emb_tn])
cost_val = theano.function(inps,cost)
train = theano.function(inps,cost,updates=updates)
# Training
print("Training...")
uidx = 0
try:
for epoch in range(num_epochs):
n_samples = 0
print("Epoch {}".format(epoch))
for x,y,z in train_iter:
n_samples +=len(x)
uidx += 1
x, x_m, y, y_m, z, z_m = batched_tweets.prepare_data(x, y, z, chardict, maxlen=MAX_LENGTH)
if x==None:
print("Minibatch with zero samples under maxlength.")
uidx -= 1
continue
ud_start = time.time()
curr_cost = train(x,x_m,y,y_m,z,z_m)
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.")
validation_cost = cost_val(t_val,t_val_m,tp_val,tp_val_m,tn_val,tn_val_m)
print("Epoch {} Validation Cost {}".format(epoch, validation_cost))
print("Seen {} samples.".format(n_samples))
except KeyboardInterrupt:
pass
def main(train_path,val_path,save_path,num_epochs=NUM_EPOCHS):
shutil.copyfile('settings.py','%s/settings.txt'%save_path)
print("Preparing Data...")
# Training data
if not RELOAD_DATA:
print("Creating Pairs...")
trainX = batched_tweets.create_pairs(train_path)
valX = batched_tweets.create_pairs(val_path)
print("Number of training pairs = {}".format(len(trainX[0])))
print("Number of validation pairs = {}".format(len(valX[0])))
with open('%s/train_pairs.pkl'%(save_path),'w') as f:
pkl.dump(trainX, f)
with open('%s/val_pairs.pkl'%(save_path),'w') as f:
pkl.dump(valX, f)
else:
print("Loading Pairs...")
with open(train_path,'r') as f:
trainX = pkl.load(f)
with open(val_path,'r') as f:
valX = pkl.load(f)
if not RELOAD_MODEL:
# Build dictionary
chardict, charcount = batched_tweets.build_dictionary(trainX[0] + trainX[1])
n_char = len(chardict.keys()) + 1
batched_tweets.save_dictionary(chardict,charcount,'%s/dict.pkl' % save_path)
# params
n_char = len(chardict.keys()) + 1
params = init_params(n_chars=n_char)
else:
print("Loading model params...")
params = load_params_shared('%s/model.npz' % save_path)
print("Loading dictionary...")
with open('%s/dict.pkl' % save_path, 'rb') as f:
chardict = pkl.load(f)
n_char = len(chardict.keys()) + 1
train_iter = batched_tweets.BatchedTweets(trainX, batch_size=N_BATCH, maxlen=MAX_LENGTH)
val_iter = batched_tweets.BatchedTweets(valX, batch_size=512, maxlen=MAX_LENGTH)
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, net = tweet2vec(tweet, t_mask, params, n_char)
emb_tp, net = tweet2vec(ptweet, tp_mask, params, n_char)
emb_tn, net = tweet2vec(ntweet, tn_mask, params, n_char)
# batch loss
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) + 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, trainable=True), lr, momentum=mu)
# Theano function
print("Compiling theano functions...")
inps = [tweet,t_mask,ptweet,tp_mask,ntweet,tn_mask]
#dist = theano.function(inps,[D1,D2])
#l = theano.function(inps,loss)
#t2v = theano.function(inps,[emb_t,emb_tp,emb_tn])
cost_val = theano.function(inps,[cost_only, emb_t, emb_tp, emb_tn])
reg_val = theano.function([],reg_only)
train = theano.function(inps,cost,updates=updates)
# Training
print("Training...")
uidx = 0
try:
for epoch in range(num_epochs):
n_samples = 0
train_cost = 0.
print("Epoch {}".format(epoch))
if USE_SCHEDULE:
# schedule
if epoch > 0 and epoch % 5 == 0:
print("Updating Schedule...")
lr = max(1e-5,lr/2)
mu = mu - 0.05
updates = lasagne.updates.nesterov_momentum(cost, lasagne.layers.get_all_params(net), lr, momentum=mu)
train = theano.function(inps,cost,updates=updates)
ud_start = time.time()
for x,y,z in train_iter:
if not x:
print("Minibatch with no valid triples")
continue
n_samples +=len(x)
uidx += 1
if DEBUG and uidx > 3:
sys.exit()
if DEBUG:
print("Tweets = {}".format(x[:5]))
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("Minibatch with zero samples under maxlength.")
uidx -= 1
continue
if DEBUG:
print("Params before update...")
print_params(params)
display_actv(x,x_m,y,y_m,z,z_m,inps,net,'before')
cb, embb, embb_p, embb_n = cost_val(x,x_m,y,y_m,z,z_m)
curr_cost = train(x,x_m,y,y_m,z,z_m)
ud = time.time() - ud_start
train_cost += curr_cost*len(x)
if DEBUG:
print("Params after update...")
print_params(params)
display_actv(x,x_m,y,y_m,z,z_m,inps,net,'after')
ca, emba, emba_p, emba_n = cost_val(x,x_m,y,y_m,z,z_m)
print("Embeddings before = {}".format(embb[:5]))
print("Embeddings after = {}".format(emba[:5]))
print("Cost before update = {} \nCost after update = {}".format(cb, ca))
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 {} Samples {}".format(epoch,uidx,curr_cost,ud,len(x)))
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("Computing Validation Cost...")
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)
regularization_cost = reg_val()
print("Epoch {} Training Cost {} Validation Cost {} Regularization Cost {}".format(epoch, train_cost/n_samples, validation_cost/n_val_samples, regularization_cost))
print("Seen {} samples.".format(n_samples))
for kk,vv in params.iteritems():
print("Param {} Epoch {} Max {} Min {}".format(kk, epoch, np.max(vv.get_value()), np.min(vv.get_value())))
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.")
if False:
# store embeddings and data
features = np.zeros((len(train_iter.data[0]),3*WDIM))
distances = np.zeros((len(train_iter.data[0]),2))
for idx, triple in enumerate(zip(train_iter.data[0],train_iter.data[1],train_iter.data[2])):
x, x_m, y, y_m, z, z_m = batched_tweets.prepare_data([triple[0]], [triple[1]], [triple[2]], chardict, maxlen=MAX_LENGTH, n_chars=n_char)
if x==None:
continue
emb1, emb2, emb3 = t2v(x,x_m,y,y_m,z,z_m)
emb1 = np.reshape(emb1, (WDIM))
emb2 = np.reshape(emb2, (WDIM))
emb3 = np.reshape(emb3, (WDIM))
features[idx,:] = np.concatenate((emb1,emb2,emb3),axis=0)
distances[idx,0] = 1-np.dot(emb1,emb2)/(np.linalg.norm(emb1)*np.linalg.norm(emb2))
distances[idx,1] = 1-np.dot(emb1,emb3)/(np.linalg.norm(emb1)*np.linalg.norm(emb3))
with open('debug/feat_%d.npy'%epoch,'w') as df:
np.save(df,features)
with open('debug/dist_%d.npy'%epoch,'w') as ds:
np.save(ds,distances)
if False:
with open('debug/data.txt','w') as dd:
for triple in zip(train_iter.data[0],train_iter.data[1],train_iter.data[2]):
dd.write('%s\t%s\t%s\n' % (triple[0],triple[1],triple[2]))
except KeyboardInterrupt:
pass
def main(train_path, val_path, save_path, num_epochs=NUM_EPOCHS):
# save settings
shutil.copyfile('settings.py', '%s/settings.txt' % save_path)
print("Preparing Data...")
# Training data
if not RELOAD_DATA:
print("Creating Pairs...")
trainX = batched_tweets.create_pairs(train_path)
valX = batched_tweets.create_pairs(val_path)
print("Number of training pairs = {}".format(len(trainX[0])))
print("Number of validation pairs = {}".format(len(valX[0])))
with open('%s/train_pairs.pkl' % (save_path), 'w') as f:
pkl.dump(trainX, f)
with open('%s/val_pairs.pkl' % (save_path), 'w') as f:
pkl.dump(valX, f)
else:
print("Loading Pairs...")
with open(train_path, 'r') as f:
trainX = pkl.load(f)
with open(val_path, 'r') as f:
valX = pkl.load(f)
if not RELOAD_MODEL:
# Build dictionary
chardict, charcount = batched_tweets.build_dictionary(trainX[0] +
trainX[1])
n_char = len(chardict.keys()) + 1
batched_tweets.save_dictionary(chardict, charcount,
'%s/dict.pkl' % save_path)
# params
params = init_params_c2w2s(n_chars=n_char)
else:
print("Loading model params...")
params = load_params_shared('%s/model.npz' % save_path)
print("Loading dictionary...")
with open('%s/dict.pkl' % save_path, 'rb') as f:
chardict = pkl.load(f)
n_char = len(chardict.keys()) + 1
train_iter = batched_tweets.BatchedTweets(trainX,
batch_size=N_BATCH,
maxlen=MAX_LENGTH)
val_iter = batched_tweets.BatchedTweets(valX,
batch_size=512,
maxlen=MAX_LENGTH)
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, c2w, w2s = char2word2vec(tweet, t_mask, params, n_char)
emb_tp, c2w, w2s = char2word2vec(ptweet, tp_mask, params, n_char)
emb_tn, c2w, w2s = char2word2vec(ntweet, tn_mask, params, n_char)
# batch loss
D1 = 1. - T.batched_dot(emb_t,
emb_tp) / (tnorm(emb_t) * tnorm(emb_tp) + 1e-6)
D2 = 1. - T.batched_dot(emb_t,
emb_tn) / (tnorm(emb_t) * tnorm(emb_tn) + 1e-6)
gap = D1 - D2 + M
loss = gap * (gap > 0)
cost = T.mean(loss)
cost_only = T.mean(loss)
# params and updates
print("Computing updates...")
lr = LEARNING_RATE
mu = MOMENTUM
updates = lasagne.updates.nesterov_momentum(cost,
params.values(),
lr,
momentum=mu)
# Theano function
print("Compiling theano functions...")
inps = [tweet, t_mask, ptweet, tp_mask, ntweet, tn_mask]
dist = theano.function(inps, [D1, D2])
#l = theano.function(inps,loss)
cost_val = theano.function(inps, [cost_only, emb_t, emb_tp, emb_tn])
train = theano.function(inps, cost, updates=updates)
# Training
print("Training...")
uidx = 0
nan_flag = False
try:
for epoch in range(num_epochs):
n_samples = 0
train_cost = 0.
print("Epoch {}".format(epoch))
if USE_SCHEDULE:
# schedule
if epoch > 0 and (epoch + 1) % 10 == 0:
print("Updating Schedule...")
lr = max(1e-5, lr / 10)
mu = mu - 0.1
updates = lasagne.updates.nesterov_momentum(
cost, params.values(), lr, momentum=mu)
train = theano.function(inps, cost, updates=updates)
if epoch >= 10:
cost = T.mean(
loss
) + REGULARIZATION * lasagne.regularization.regularize_network_params(
c2w, lasagne.regularization.l2
) + REGULARIZATION * lasagne.regularization.regularize_network_params(
w2s, lasagne.regularization.l2)
reg_only = REGULARIZATION * lasagne.regularization.regularize_network_params(
c2w, lasagne.regularization.l2
) + REGULARIZATION * lasagne.regularization.regularize_network_params(
w2s, lasagne.regularization.l2)
reg_val = theano.function([], reg_only)
train = theano.function(inps, cost, updates=updates)
ud_start = time.time()
for xt, yt, zt in train_iter:
if not xt:
print("Minibatch with no valid triples")
continue
n_samples += len(xt)
uidx += 1
if DEBUG and uidx > 20:
sys.exit()
if DEBUG:
print("Tweets = {}".format(xt[:5]))
print("Tweets = {}".format(yt[:5]))
print("Tweets = {}".format(zt[:5]))
x, x_m, y, y_m, z, z_m = batched_tweets.prepare_data_c2w2s(
xt,
yt,
zt,
chardict,
maxwordlen=MAX_WORD_LENGTH,
maxseqlen=MAX_SEQ_LENGTH,
n_chars=n_char)
if x == None:
print("Minibatch with zero samples under maxlength.")
uidx -= 1
continue
if DEBUG:
print("Params before update...")
print_params(params)
display_actv(x, x_m, y, y_m, z, z_m, inps, w2s, 'before')
cb, embb, embb_p, embb_n = cost_val(x, x_m, y, y_m, z, z_m)
d1, d2 = dist(x, x_m, y, y_m, z, z_m)
curr_cost = train(x, x_m, y, y_m, z, z_m)
train_cost += curr_cost * len(x)
if DEBUG:
print("Params after update...")
print_params(params)
display_actv(x, x_m, y, y_m, z, z_m, inps, w2s, 'after')
ca, emba, emba_p, emba_n = cost_val(x, x_m, y, y_m, z, z_m)
d1a, d2a = dist(x, x_m, y, y_m, z, z_m)
print("Embeddings before = {}".format(embb[:5]))
print("Embeddings after = {}".format(emba[:5]))
print("Distances1 before = {}".format(d1))
print("Distances2 before = {}".format(d2))
print("Distances1 after = {}".format(d1a))
print("Distances2 after = {}".format(d2a))
print("Cost before update = {} \nCost after update = {}".
format(cb, ca))
if np.isnan(curr_cost) or np.isinf(curr_cost):
print("Nan detected.")
if not nan_flag:
print("Saving...")
saveparams = OrderedDict()
for kk, vv in params.iteritems():
saveparams[kk] = vv.get_value()
np.savez('%s/model_nan.npz' % save_path,
**saveparams)
with open('%s/tweets_nan.pkl' % save_path, 'w') as f:
pkl.dump(xt, f)
pkl.dump(yt, f)
pkl.dump(zt, f)
with open('%s/functions_nan.pkl' % save_path,
'w') as f:
pkl.dump(train, f)
pkl.dump(cost_val, f)
with open('%s/updates_nan.pkl' % save_path, 'w') as f:
pkl.dump(updates, f)
print("Done.")
nan_flag = True
continue
ud = time.time() - ud_start
if np.mod(uidx, DISPF) == 0:
print(
"Epoch {} Update {} Cost {} Time {} Samples {}".format(
epoch, uidx, curr_cost, ud, len(x)))
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("Computing Validation Cost...")
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_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)
if epoch >= 10:
regularization_cost = reg_val()
else:
regularization_cost = 0
print(
"Epoch {} Training Cost {} Validation Cost {} Regularization Cost {}"
.format(epoch, train_cost / n_samples,
validation_cost / n_val_samples, regularization_cost))
print("Seen {} samples.".format(n_samples))
for kk, vv in params.iteritems():
print("Param {} Epoch {} Max {} Min {}".format(
kk, epoch, np.max(vv.get_value()), np.min(vv.get_value())))
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.")
if False:
# store embeddings and data
features = np.zeros((len(train_iter.data[0]), 3 * WDIM))
distances = np.zeros((len(train_iter.data[0]), 2))
for idx, triple in enumerate(
zip(train_iter.data[0], train_iter.data[1],
train_iter.data[2])):
x, x_m, y, y_m, z, z_m = batched_tweets.prepare_data(
[triple[0]], [triple[1]], [triple[2]],
chardict,
maxlen=MAX_LENGTH,
n_chars=n_char)
if x == None:
continue
emb1, emb2, emb3 = t2v(x, x_m, y, y_m, z, z_m)
emb1 = np.reshape(emb1, (WDIM))
emb2 = np.reshape(emb2, (WDIM))
emb3 = np.reshape(emb3, (WDIM))
features[idx, :] = np.concatenate((emb1, emb2, emb3),
axis=0)
distances[idx, 0] = 1 - np.dot(emb1, emb2) / (
np.linalg.norm(emb1) * np.linalg.norm(emb2))
distances[idx, 1] = 1 - np.dot(emb1, emb3) / (
np.linalg.norm(emb1) * np.linalg.norm(emb3))
with open('debug/feat_%d.npy' % epoch, 'w') as df:
np.save(df, features)
with open('debug/dist_%d.npy' % epoch, 'w') as ds:
np.save(ds, distances)
if False:
with open('debug/data.txt', 'w') as dd:
for triple in zip(train_iter.data[0], train_iter.data[1],
train_iter.data[2]):
dd.write('%s\t%s\t%s\n' %
(triple[0], triple[1], triple[2]))
except KeyboardInterrupt:
pass