def train(args):
tf.set_random_seed(19)
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
model = SkipGram(sess=sess, **args['dataset'], **args['model'], **args)
model.train()
def train_skipgram():
losses = []
loss_fn = nn.MSELoss()
model = SkipGram(vocab_size, embed_size)
print(model)
print('vocab_size:', vocab_size)
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
skipgram_train_data = create_skipgram_dataset(text)
model.train()
for epoch in range(n_epoch):
total_loss = .0
for in_w, out_w, target in skipgram_train_data:
in_w_var = Variable(torch.LongTensor([w2i[in_w]]))
out_w_var = Variable(torch.LongTensor([w2i[out_w]]))
model.zero_grad()
log_probs = model(in_w_var, out_w_var)
loss = loss_fn(log_probs[0], Variable(torch.Tensor([target])))
loss.backward()
optimizer.step()
total_loss += float(loss)
losses.append(total_loss)
return model, losses
def main(args):
LongTensor = torch.cuda.LongTensor if args.gpu else torch.LongTensor
data = get_pickle('assets/dataset.pkl')
i2s = get_pickle('assets/i2s.pkl')
dataset = skipDataset(data)
model = SkipGram(len(i2s), 300)
if args.gpu:
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
losses = []
epoch_losses = [np.inf, np.inf, np.inf]
total_n = len(dataset)
tmplt = "E:{:2d} - i:{:5d}({:4.2f}%) - L:{:5.5f}"
for epoch in range(args.epoch):
dataloader = DataLoader(dataset,
batch_size=args.bs,
collate_fn=collate_fn,
shuffle=True)
model.train()
losses = []
for i, batch in enumerate(dataloader):
center, target = batch
center = LongTensor(center)
target = LongTensor(target)
loss = model(center, target)
loss.backward()
optimizer.step()
model.zero_grad()
losses.append(loss.data)
if i % 100 == 0:
ml = np.mean(losses)
t = tmplt.format(epoch, i, i * args.bs / total_n * 100, ml)
print(t)
losses = []
model.eval()
dataloader = DataLoader(dataset,
batch_size=args.bs,
collate_fn=collate_fn,
shuffle=True)
losses = []
for i, batch in enumerate(dataloader):
center, target = batch
center = torch.LongTensor(center)
target = torch.LongTensor(target)
loss = model(center, target)
losses.append(loss.data)
epoch_losses.append(np.mean(losses))
print('Epoch loss {}'.format(epoch_losses[-1]))
if epoch_losses[-1] > epoch_losses[-4]:
break
else:
filename = 'assets/model/model_skip.torch'
state = dict(state_dict=model.state_dict(),
loss=epoch_losses,
args=args)
torch.save(state, filename)
def train(self):
if self.model_name == 'SkipGram':
model = SkipGram(self.vocabulary_size, self.embedding_dim)
elif self.model_name == 'CBOW':
return
if torch.cuda.is_available():
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=0.2)
for epoch in range(self.epoch):
start = time.time()
self.op.process = True
batch_num = 0
batch_new = 0
while self.op.process:
pos_u, pos_v, neg_v = self.op.generate_batch(
self.windows_size, self.batch_size, self.neg_sample_size)
pos_u = Variable(torch.LongTensor(pos_u))
pos_v = Variable(torch.LongTensor(pos_v))
neg_v = Variable(torch.LongTensor(neg_v))
if torch.cuda.is_available():
pos_u = pos_u.cuda()
pos_v = pos_v.cuda()
neg_v = neg_v.cuda()
optimizer.zero_grad()
loss = model(pos_u, pos_v, neg_v, self.batch_size)
loss.backward()
optimizer.step()
if batch_num % 3000 == 0:
end = time.time()
print(
'epoch,batch = %2d %5d: pair/sec = %4.2f loss = %4.3f\r'
% (epoch, batch_num,
(batch_num - batch_new) * self.batch_size /
(end - start), loss.data[0]),
end="\n")
batch_new = batch_num
start = time.time()
batch_num += 1
model.save_embeddings(self.op.idx2word, 'word_embdding.txt',
torch.cuda.is_available())
def main():
args = docopt(__doc__)
embedding_dim = int(args['--dim'])
max_context = int(args['--max-context'])
neg_sample_factor = int(args['--neg-sample-factor'])
batch_size = int(args['--batch'])
lr = float(args['--lr'])
epochs = int(args['--epochs'])
np.random.seed(int(args['--seed']))
torch.manual_seed(int(args['--seed']))
torch.cuda.manual_seed_all(int(args['--seed']))
device = torch.device(int(args['--device']))
print(f"{device} will be used")
num_workers = int(args['--num-workers'])
fpath = args['--file']
backup_interval = int(args['--backup-interval'])
dname = args['--dirname']
dset = FixedLengthContextDataset(fpath, max_context, neg_sample_factor)
vocabulary_size = dset.num_authors
# Symmetric vectors are used to compute cosine similarity
if args['symmetric']:
model = SymmetricEmbedding(vocabulary_size, embedding_dim)
# Word2Vec Skip-gram. Unsymmetric vectors are used to compute cosine similarity
elif args['skipgram']:
model = SkipGram(vocabulary_size, embedding_dim)
if dname == None:
tmp = 'symmetric' if args['symmetric'] else 'skipgram'
dname = get_dirname(f'embedding_{tmp}')
else:
os.makedirs(dname)
if torch.cuda.is_available():
model = model.to(device)
loader = DataLoader(dset, batch_size, num_workers=num_workers)
train(model, loader, dname, epochs, lr, backup_interval, device)
def train(self, report=True):
model = SkipGram(self.vocabulary_size, self.embedding_dim)
loss_list = list()
if torch.cuda.is_available():
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=0.2)
for epoch in range(self.epoch):
start = time.time()
self.data.process = True
batch_num = 0
batch_new = 0
for data_word, data_sentence in self.data_loader():
optimizer.zero_grad()
loss = model(data_word) / self.batch_size
# loss = model(pos_u, pos_v, neg_v, self.batch_size, target, contex, labels)
loss_list.append(loss)
loss.backward()
optimizer.step()
if report and batch_num % 7 == 0: # 3000
end = time.time()
print(
'epoch,batch = %2d %5d: batch_size = %5d loss = %4.3f\r'
% (epoch, batch_num, self.batch_size, loss.item()),
end="\n")
batch_new = batch_num
start = time.time()
batch_num += 1
self.showPlot(loss_list, 'Losses')
model.save_embeddings(self.data.idx2word, 'word_embdding.txt')
def main():
train_set = PTBDataSet()
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
device = torch.device('cuda')
model = SkipGram(train_set.get_token_num(), embedding_dim).to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(epochs):
total_loss = 0
for batch_idx, (center, context_negative, mask, label) in enumerate(train_loader):
center, context_negative, mask, label = center.to(device), context_negative.to(device), mask.to(
device), label.to(device)
criteon = nn.BCEWithLogitsLoss(weight=mask.double(), reduction='none').to(device)
# pred: [batch_size, max_len]
pred = model(center, context_negative)
loss = torch.sum(torch.sum(criteon(pred.double(), label.double()), dim=1) / torch.sum(mask.double(), dim=1))
total_loss += loss.item()
if batch_idx % 200 == 0:
print(f'epoch {epoch+1} batch {batch_idx} loss {loss.item()/pred.shape[0]}')
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(f'-->epoch {epoch+1} average loss {total_loss/train_set.__len__()}')
model.get_topk_similar_tokens('chip', train_set.index_to_token, train_set.token_to_index, device, show_top_k)
print("Load corpus from pickle file...")
with open('nltk_reuters_corpus.pkl', 'rb') as f:
corpus = pickle.load(f)
print("Building model...")
vocab_size, vocab = get_count_distinct(corpus)
word2idx, idx2word = get_vocab_dicts(vocab_size, vocab)
window_size = 2
# declare a Tensorflow graph
graph = tf.Graph()
with graph.as_default() as g:
# create an instance of SkipGram model
model = SkipGram(vocab_size=vocab_size,
embedding_dim=128,
window_size=window_size,
batch_size=16,
graph=g)
# configure GPU options
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
# start the Tensorflow session
with tf.Session(config=sess_config) as sess:
# writer and saver objects
writer = tf.summary.FileWriter("./logs")
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
# check for already present checkpoint files
s2i = get_pickle('assets/s2i.pkl')
i2s = get_pickle('assets/i2s.pkl')
holdings = pd.read_csv('assets/holdings.csv', index_col=6)
glove_cor_checkpoint = torch.load('assets/model/model_glove_cor.torch')
model_glove = GloVeCor(len(s2i), 300)
model_glove.load_state_dict(glove_cor_checkpoint['state_dict'])
weights = model_glove.embeddings.weight.detach()
np.savetxt('embeddings/glove_cor_tensors.tsv', weights, delimiter='\t')
glove_cov_checkpoint = torch.load('assets/model/model_glove_cov.torch')
model_glove = GloVeCov(len(s2i), 300)
model_glove.load_state_dict(glove_cov_checkpoint['state_dict'])
weights = model_glove.embeddings.weight.detach()
np.savetxt('embeddings/glove_cov_tensors.tsv', weights, delimiter='\t')
skip_checkpoint = torch.load('assets/model/model_skip.torch')
model_skip = SkipGram(len(s2i), 300)
model_skip.load_state_dict(skip_checkpoint['state_dict'])
weights = model_skip.embeddings.weight.detach()
np.savetxt('embeddings/skip_tensors.tsv', weights, delimiter='\t')
selector = [i2s[e] for e in range(len(weights))]
cols = ['Name', 'Sector', 'Industry Group', 'Country', 'Currency']
metadata = holdings.loc[selector, cols]
metadata.to_csv('assets/metadata.tsv', sep='\t')
cols = ['Name', 'Currency']
metadata = holdings.loc[selector, cols]
metadata.to_csv('embeddings/metadata.tsv', sep='\t', index=False)
args = parser.parse_args() tokenize_data = loadData() int_text, word2idx, idx2word, freq, vocab = prepareData(tokenize_data, args.min_freq) if args.mode == "train": vocab_size = sum([freq[k] for k in freq]) subsampled_words = subsampling(freq, args.sampling_threshold, vocab, vocab_size, word2idx) neg_sample = negativeSampling(freq) #print(neg_sample.shape) device='cpu' model = SkipGram(len(word2idx), args.embed_size, neg_sample).to(device) optimizer = optim.Adam(model.parameters(), args.lr) epoch = args.epochs steps = 0 for i in range(epoch): for input_words, target_words in loadBatches(subsampled_words, args.batch_size, args.window_size): steps = steps + 1 inputs = torch.LongTensor(input_words) targets = torch.LongTensor(target_words) #inputs, targets = inputs.to(device), targets.to(device) loss = model.forward(inputs, targets, inputs.shape[0], 2)
def create_model(self):
print("Initialize model")
vocab_size = len(self.word2idx)
self.model = SkipGram(vocab_size=vocab_size, emb_dim=self.embed_dim).to(self.device)
self.optimizer = optim.SGD(self.model.parameters(), lr=self.lr)
with open(f'{FILENAME}.vocab') as f:
vocab = sorted(f.readline().split())
VOCAB_SIZE = len(vocab)
# String2int conversion
words_to_idx = {i: j for j, i in enumerate(vocab)}
# Create dataset
train_dataset = WikiDataset(f'{FILENAME}.parsed', 1, words_to_idx)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS)
# Get random sampler
sampler = NegativeSampler(f'{FILENAME}.parsed.count', words_to_idx)
for emb_dim in EMBEDDING_DIMS:
model = SkipGram(VOCAB_SIZE, emb_dim)
model.to(DEVICE)
model.device = DEVICE
optimizer = torch.optim.SparseAdam(model.parameters())
train(model,
train_dataloader,
optimizer,
sampler,
VOCAB_SIZE,
epochs=EPOCHS,
save_path=f'{SAVE_FOLDER}{emb_dim}_')
def __init__(self, mode, vocab_dim, embed_dim, sparse):
self.mode = mode
if self.mode == 'cbow':
self.model = CBOW(vocab_dim, embed_dim, sparse)
elif self.mode == 'skip-gram':
self.model = SkipGram(vocab_dim, embed_dim, sparse)