def main(args):
# load textfile
train_dataset, dev_dataset, test_dataset, vocab, vocab_inv = read_data(
args.text_filename,
train_split_ratio=args.train_split,
dev_split_ratio=args.dev_split,
seed=args.seed)
save_vocab(args.model_dir, vocab, vocab_inv)
vocab_size = len(vocab)
print_bold("data # hash")
print("train {} {}".format(len(train_dataset), hash(str(train_dataset))))
print("dev {} {}".format(len(dev_dataset), hash(str(dev_dataset))))
print("test {} {}".format(len(test_dataset), hash(str(test_dataset))))
print("vocab {}".format(vocab_size))
# split into buckets
train_buckets = make_buckets(train_dataset)
print_bold("buckets #data (train)")
if args.buckets_limit is not None:
train_buckets = train_buckets[:args.buckets_limit + 1]
for size, data in zip(bucket_sizes, train_buckets):
print("{} {}".format(size, len(data)))
print_bold("buckets #data (dev)")
dev_buckets = make_buckets(dev_dataset)
if args.buckets_limit is not None:
dev_buckets = dev_buckets[:args.buckets_limit + 1]
for size, data in zip(bucket_sizes, dev_buckets):
print("{} {}".format(size, len(data)))
print_bold("buckets #data (test)")
test_buckets = make_buckets(test_dataset)
for size, data in zip(bucket_sizes, test_buckets):
print("{} {}".format(size, len(data)))
# to maintain equilibrium
min_num_data = 0
for data in train_buckets:
if min_num_data == 0 or len(data) < min_num_data:
min_num_data = len(data)
repeats = []
for data in train_buckets:
repeat = len(data) // min_num_data
repeat = repeat + 1 if repeat == 0 else repeat
repeats.append(repeat)
num_updates_per_iteration = 0
for repeat, data in zip(repeats, train_buckets):
num_updates_per_iteration += repeat * args.batchsize
num_iteration = len(train_dataset) // num_updates_per_iteration + 1
# init
model = load_model(args.model_dir)
if model is None:
model = RNNModel(vocab_size,
args.ndim_embedding,
args.num_layers,
ndim_h=args.ndim_h,
kernel_size=args.kernel_size,
pooling=args.pooling,
zoneout=args.zoneout,
dropout=args.dropout,
wgain=args.wgain,
densely_connected=args.densely_connected,
ignore_label=ID_PAD)
if args.gpu_device >= 0:
chainer.cuda.get_device(args.gpu_device).use()
model.to_gpu()
# setup an optimizer
if args.eve:
optimizer = Eve(alpha=args.learning_rate, beta1=0.9)
else:
optimizer = optimizers.Adam(alpha=args.learning_rate, beta1=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.grad_clip))
optimizer.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
min_learning_rate = 1e-7
prev_ppl = None
total_time = 0
def mean(l):
return sum(l) / len(l)
# training
for epoch in xrange(1, args.epoch + 1):
print("Epoch", epoch)
start_time = time.time()
for itr in xrange(1, num_iteration + 1):
sys.stdout.write("\r{} / {}".format(itr, num_iteration))
sys.stdout.flush()
for repeat, dataset in zip(repeats, train_buckets):
for r in xrange(repeat):
batch = sample_batch_from_bucket(dataset, args.batchsize)
source, target = make_source_target_pair(batch)
if model.xp is cuda.cupy:
source = cuda.to_gpu(source)
target = cuda.to_gpu(target)
model.reset_state()
Y = model(source)
loss = softmax_cross_entropy(Y,
target,
ignore_label=ID_PAD)
optimizer.update(lossfun=lambda: loss)
if itr % args.interval == 0 or itr == num_iteration:
save_model(args.model_dir, model)
# show log
sys.stdout.write("\r" + stdout.CLEAR)
sys.stdout.flush()
print_bold(" accuracy (sampled train)")
acc_train = compute_random_accuracy(model, train_buckets,
args.batchsize)
print(" ", mean(acc_train), acc_train)
print_bold(" accuracy (dev)")
acc_dev = compute_accuracy(model, dev_buckets, args.batchsize)
print(" ", mean(acc_dev), acc_dev)
print_bold(" ppl (sampled train)")
ppl_train = compute_random_perplexity(model, train_buckets,
args.batchsize)
print(" ", mean(ppl_train), ppl_train)
print_bold(" ppl (dev)")
ppl_dev = compute_perplexity(model, dev_buckets, args.batchsize)
ppl_dev_mean = mean(ppl_dev)
print(" ", ppl_dev_mean, ppl_dev)
elapsed_time = (time.time() - start_time) / 60.
total_time += elapsed_time
print(" done in {} min, lr = {}, total {} min".format(
int(elapsed_time), optimizer.alpha, int(total_time)))
# decay learning rate
if prev_ppl is not None and ppl_dev_mean >= prev_ppl and optimizer.alpha > min_learning_rate:
optimizer.alpha *= 0.5
prev_ppl = ppl_dev_mean
def create_vocabs(ents, rel_vocab):
ent_vocab = Vocab(Counter(ents))
vectors = get_pbg(ents, '../../embeddings', 'unified_embs.txt')
ent_vocab.set_vectors(vectors.stoi, vectors.vectors, vectors.dim)
save_vocab(os.path.join(BASE_PATH, 'vocab.pkl'), ent_vocab, rel_vocab)
def main(args):
source_dataset, target_dataset, vocab, vocab_inv = read_data_and_vocab(
args.source_train,
args.target_train,
args.source_dev,
args.target_dev,
args.source_test,
args.target_test,
reverse_source=True)
save_vocab(args.model_dir, vocab, vocab_inv)
source_dataset_train, source_dataset_dev, source_dataset_test = source_dataset
target_dataset_train, target_dataset_dev, target_dataset_test = target_dataset
vocab_source, vocab_target = vocab
vocab_inv_source, vocab_inv_target = vocab_inv
# split into buckets
source_buckets_train, target_buckets_train = make_buckets(
source_dataset_train, target_dataset_train)
if args.buckets_slice is not None:
source_buckets_train = source_buckets_train[:args.buckets_slice + 1]
target_buckets_train = target_buckets_train[:args.buckets_slice + 1]
# development dataset
source_buckets_dev = None
if len(source_dataset_dev) > 0:
source_buckets_dev, target_buckets_dev = make_buckets(
source_dataset_dev, target_dataset_dev)
if args.buckets_slice is not None:
source_buckets_dev = source_buckets_dev[:args.buckets_slice + 1]
target_buckets_dev = target_buckets_dev[:args.buckets_slice + 1]
# test dataset
source_buckets_test = None
if len(source_dataset_test) > 0:
source_buckets_test, target_buckets_test = make_buckets(
source_dataset_test, target_dataset_test)
if args.buckets_slice is not None:
source_buckets_test = source_buckets_test[:args.buckets_slice + 1]
target_buckets_test = target_buckets_test[:args.buckets_slice + 1]
# show log
dump_dataset(
source_dataset, vocab,
(source_buckets_train, source_buckets_dev, source_buckets_test))
# to maintain equilibrium
required_interations = []
for data in source_buckets_train:
itr = len(data) // args.batchsize + 1
required_interations.append(itr)
total_iterations = sum(required_interations)
buckets_distribution = np.asarray(required_interations,
dtype=float) / total_iterations
# init
model = load_model(args.model_dir)
if model is None:
model = seq2seq(len(vocab_source),
len(vocab_target),
args.ndim_embedding,
args.ndim_h,
args.num_layers,
pooling=args.pooling,
dropout=args.dropout,
zoneout=args.zoneout,
weightnorm=args.weightnorm,
wgain=args.wgain,
densely_connected=args.densely_connected,
attention=args.attention)
if args.gpu_device >= 0:
cuda.get_device(args.gpu_device).use()
model.to_gpu()
# setup an optimizer
optimizer = get_optimizer(args.optimizer, args.learning_rate,
args.momentum)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.grad_clip))
optimizer.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
final_learning_rate = 1e-5
total_time = 0
indices_train = []
for bucket_idx, bucket in enumerate(source_buckets_train):
indices = np.arange(len(bucket))
np.random.shuffle(indices)
indices_train.append(indices)
def mean(l):
return sum(l) / len(l)
# training
for epoch in range(1, args.epoch + 1):
print("Epoch", epoch)
start_time = time.time()
with chainer.using_config("train", True):
for itr in range(total_iterations):
bucket_idx = int(
np.random.choice(np.arange(len(source_buckets_train)),
size=1,
p=buckets_distribution))
source_bucket = source_buckets_train[bucket_idx]
target_bucket = target_buckets_train[bucket_idx]
# sample minibatch
source_batch = source_bucket[:args.batchsize]
target_batch = target_bucket[:args.batchsize]
skip_mask = source_batch != ID_PAD
target_batch_input, target_batch_output = make_source_target_pair(
target_batch)
# to gpu
if args.gpu_device >= 0:
skip_mask = cuda.to_gpu(skip_mask)
source_batch = cuda.to_gpu(source_batch)
target_batch_input = cuda.to_gpu(target_batch_input)
target_batch_output = cuda.to_gpu(target_batch_output)
# compute loss
model.reset_state()
if args.attention:
last_hidden_states, last_layer_outputs = model.encode(
source_batch, skip_mask)
y_batch = model.decode(target_batch_input,
last_hidden_states,
last_layer_outputs, skip_mask)
else:
last_hidden_states = model.encode(source_batch, skip_mask)
y_batch = model.decode(target_batch_input,
last_hidden_states)
loss = softmax_cross_entropy(y_batch,
target_batch_output,
ignore_label=ID_PAD)
# update parameters
optimizer.update(lossfun=lambda: loss)
# show log
printr("iteration {}/{}".format(itr + 1, total_iterations))
source_buckets_train[bucket_idx] = np.roll(source_bucket,
-args.batchsize,
axis=0) # shift
target_buckets_train[bucket_idx] = np.roll(target_bucket,
-args.batchsize,
axis=0) # shift
# shuffle
for bucket_idx in range(len(source_buckets_train)):
indices = indices_train[bucket_idx]
np.random.shuffle(indices)
source_buckets_train[bucket_idx] = source_buckets_train[
bucket_idx][indices]
target_buckets_train[bucket_idx] = target_buckets_train[
bucket_idx][indices]
# serialize
save_model(args.model_dir, model)
# clear console
printr("")
# show log
with chainer.using_config("train", False):
if epoch % args.interval == 0:
printb("translate (train)")
dump_random_source_target_translation(model,
source_buckets_train,
target_buckets_train,
vocab_inv_source,
vocab_inv_target,
num_translate=5,
beam_width=1)
if source_buckets_dev is not None:
printb("translate (dev)")
dump_random_source_target_translation(model,
source_buckets_dev,
target_buckets_dev,
vocab_inv_source,
vocab_inv_target,
num_translate=5,
beam_width=1)
if source_buckets_dev is not None:
printb("WER (dev)")
wer_dev = compute_error_rate_buckets(model,
source_buckets_dev,
target_buckets_dev,
len(vocab_inv_target),
beam_width=1)
print(mean(wer_dev), wer_dev)
elapsed_time = (time.time() - start_time) / 60.
total_time += elapsed_time
print("done in {} min, lr = {:.4f}, total {} min".format(
int(elapsed_time), get_current_learning_rate(optimizer),
int(total_time)))
# decay learning rate
decay_learning_rate(optimizer, args.lr_decay_factor,
final_learning_rate)
def main():
# load textfile
dataset_train, dataset_dev, _, vocab, vocab_inv = read_data(args.train_filename, args.dev_filename)
vocab_size = len(vocab)
save_vocab(args.model_dir, vocab, vocab_inv)
# split into buckets
train_buckets = make_buckets(dataset_train)
if args.buckets_slice is not None:
train_buckets = train_buckets[:args.buckets_slice + 1]
dev_buckets = None
if len(dataset_dev) > 0:
dev_buckets = make_buckets(dataset_dev)
if args.buckets_slice is not None:
dev_buckets = dev_buckets[:args.buckets_slice + 1]
# print
dump_dataset(dataset_train, dataset_dev, train_buckets, dev_buckets, vocab_size)
# to maintain equilibrium
required_interations = []
for data in train_buckets:
itr = math.ceil(len(data) / args.batchsize)
required_interations.append(itr)
total_iterations = sum(required_interations)
buckets_distribution = np.asarray(required_interations, dtype=float) / total_iterations
# init
model = load_model(args.model_dir)
if model is None:
model = RNNModel(vocab_size, args.ndim_embedding, args.num_layers, ndim_h=args.ndim_h, kernel_size=args.kernel_size, pooling=args.pooling, zoneout=args.zoneout, dropout=args.dropout, weightnorm=args.weightnorm, wgain=args.wgain, densely_connected=args.densely_connected, ignore_label=ID_PAD)
if args.gpu_device >= 0:
chainer.cuda.get_device(args.gpu_device).use()
model.to_gpu()
# setup an optimizer
optimizer = get_optimizer(args.optimizer, args.learning_rate, args.momentum)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.grad_clip))
optimizer.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
final_learning_rate = 1e-4
total_time = 0
def mean(l):
return sum(l) / len(l)
# training
for epoch in range(1, args.epoch + 1):
print("Epoch", epoch)
start_time = time.time()
with chainer.using_config("train", True):
for itr in range(total_iterations):
bucket_idx = int(np.random.choice(np.arange(len(train_buckets)), size=1, p=buckets_distribution))
dataset = train_buckets[bucket_idx]
np.random.shuffle(dataset)
data_batch = dataset[:args.batchsize]
source_batch, target_batch = make_source_target_pair(data_batch)
if args.gpu_device >= 0:
source_batch = cuda.to_gpu(source_batch)
target_batch = cuda.to_gpu(target_batch)
# update params
model.reset_state()
y_batch = model(source_batch)
loss = F.softmax_cross_entropy(y_batch, target_batch, ignore_label=ID_PAD)
optimizer.update(lossfun=lambda: loss)
# show log
printr("iteration {}/{}".format(itr + 1, total_iterations))
save_model(args.model_dir, model)
# clear console
printr("")
# compute perplexity
with chainer.using_config("train", False):
if dev_buckets is not None:
printb(" ppl (dev)")
ppl_dev = compute_perplexity(model, dev_buckets, args.batchsize)
print(" ", mean(ppl_dev), ppl_dev)
# show log
elapsed_time = (time.time() - start_time) / 60.
total_time += elapsed_time
print(" done in {} min, lr = {}, total {} min".format(int(elapsed_time), get_current_learning_rate(optimizer), int(total_time)))
# decay learning rate
decay_learning_rate(optimizer, args.lr_decay_factor, final_learning_rate)
def main(args):
# load textfile
source_dataset, target_dataset, vocab, vocab_inv = read_data(args.source_filename, args.target_filename, train_split_ratio=args.train_split, dev_split_ratio=args.dev_split, seed=args.seed)
save_vocab(args.model_dir, vocab, vocab_inv)
source_dataset_train, source_dataset_dev, source_dataset_test = source_dataset
target_dataset_train, target_dataset_dev, target_dataset_test = target_dataset
print_bold("data #")
print("train {}".format(len(source_dataset_train)))
print("dev {}".format(len(source_dataset_dev)))
print("test {}".format(len(source_dataset_test)))
vocab_source, vocab_target = vocab
vocab_inv_source, vocab_inv_target = vocab_inv
print("vocab {} (source)".format(len(vocab_source)))
print("vocab {} (target)".format(len(vocab_target)))
# split into buckets
source_buckets_train, target_buckets_train = make_buckets(source_dataset_train, target_dataset_train)
if args.buckets_limit is not None:
source_buckets_train = source_buckets_train[:args.buckets_limit+1]
target_buckets_train = target_buckets_train[:args.buckets_limit+1]
print_bold("buckets #data (train)")
for size, data in zip(bucket_sizes, source_buckets_train):
print("{} {}".format(size, len(data)))
print_bold("buckets #data (dev)")
source_buckets_dev, target_buckets_dev = make_buckets(source_dataset_dev, target_dataset_dev)
if args.buckets_limit is not None:
source_buckets_dev = source_buckets_dev[:args.buckets_limit+1]
target_buckets_dev = target_buckets_dev[:args.buckets_limit+1]
for size, data in zip(bucket_sizes, source_buckets_dev):
print("{} {}".format(size, len(data)))
print_bold("buckets #data (test)")
source_buckets_test, target_buckets_test = make_buckets(source_dataset_test, target_dataset_test)
if args.buckets_limit is not None:
source_buckets_test = source_buckets_test[:args.buckets_limit+1]
target_buckets_test = target_buckets_test[:args.buckets_limit+1]
for size, data in zip(bucket_sizes, source_buckets_test):
print("{} {}".format(size, len(data)))
# to maintain equilibrium
min_num_data = 0
for data in source_buckets_train:
if min_num_data == 0 or len(data) < min_num_data:
min_num_data = len(data)
repeats = []
for data in source_buckets_train:
repeats.append(len(data) // min_num_data + 1)
num_updates_per_iteration = 0
for repeat, data in zip(repeats, source_buckets_train):
num_updates_per_iteration += repeat * args.batchsize
num_iteration = len(source_dataset_train) // num_updates_per_iteration + 1
# init
model = load_model(args.model_dir)
if model is None:
model = seq2seq(len(vocab_source), len(vocab_target), args.ndim_embedding, args.num_layers, ndim_h=args.ndim_h, pooling=args.pooling, dropout=args.dropout, zoneout=args.zoneout, wgain=args.wgain, densely_connected=args.densely_connected, attention=args.attention)
if args.gpu_device >= 0:
cuda.get_device(args.gpu_device).use()
model.to_gpu()
# setup an optimizer
if args.eve:
optimizer = Eve(alpha=args.learning_rate, beta1=0.9)
else:
optimizer = optimizers.Adam(alpha=args.learning_rate, beta1=0.9)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.grad_clip))
optimizer.add_hook(chainer.optimizer.WeightDecay(args.weight_decay))
min_learning_rate = 1e-7
prev_wer = None
total_time = 0
def mean(l):
return sum(l) / len(l)
# training
for epoch in xrange(1, args.epoch + 1):
print("Epoch", epoch)
start_time = time.time()
for itr in xrange(1, num_iteration + 1):
for repeat, source_bucket, target_bucket in zip(repeats, source_buckets_train, target_buckets_train):
for r in xrange(repeat):
# sample minibatch
source_batch, target_batch = sample_batch_from_bucket(source_bucket, target_bucket, args.batchsize)
skip_mask = source_batch != ID_PAD
target_batch_input, target_batch_output = make_source_target_pair(target_batch)
# to gpu
if model.xp is cuda.cupy:
skip_mask = cuda.to_gpu(skip_mask)
source_batch = cuda.to_gpu(source_batch)
target_batch_input = cuda.to_gpu(target_batch_input)
target_batch_output = cuda.to_gpu(target_batch_output)
# compute loss
model.reset_state()
if args.attention:
last_hidden_states, last_layer_outputs = model.encode(source_batch, skip_mask)
Y = model.decode(target_batch_input, last_hidden_states, last_layer_outputs, skip_mask)
else:
last_hidden_states = model.encode(source_batch, skip_mask)
Y = model.decode(target_batch_input, last_hidden_states)
loss = softmax_cross_entropy(Y, target_batch_output, ignore_label=ID_PAD)
optimizer.update(lossfun=lambda: loss)
sys.stdout.write("\r{} / {}".format(itr, num_iteration))
sys.stdout.flush()
if itr % args.interval == 0 or itr == num_iteration:
save_model(args.model_dir, model)
# show log
sys.stdout.write("\r" + stdout.CLEAR)
sys.stdout.flush()
print_bold("translate (train)")
show_random_source_target_translation(model, source_buckets_train, target_buckets_train, vocab_inv_source, vocab_inv_target, num_translate=5, argmax=True)
print_bold("translate (dev)")
show_random_source_target_translation(model, source_buckets_dev, target_buckets_dev, vocab_inv_source, vocab_inv_target, num_translate=5, argmax=True)
print_bold("WER (sampled train)")
wer_train = compute_random_mean_wer(model, source_buckets_train, target_buckets_train, len(vocab_inv_target), sample_size=args.batchsize, argmax=True)
print(mean(wer_train), wer_train)
print_bold("WER (dev)")
wer_dev = compute_mean_wer(model, source_buckets_dev, target_buckets_dev, len(vocab_inv_target), batchsize=args.batchsize, argmax=True)
mean_wer_dev = mean(wer_dev)
print(mean_wer_dev, wer_dev)
elapsed_time = (time.time() - start_time) / 60.
total_time += elapsed_time
print("done in {} min, lr = {}, total {} min".format(int(elapsed_time), optimizer.alpha, int(total_time)))
# decay learning rate
if prev_wer is not None and mean_wer_dev >= prev_wer and optimizer.alpha > min_learning_rate:
optimizer.alpha *= 0.5
prev_wer = mean_wer_dev