def train(self, training_data: TrainingData) -> None:
x_train, y_train, x_val, y_val, vocab, class_to_i, i_to_class = preprocess_dataset(
training_data)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
log.info('Batchifying data')
train_batches = batchify(x_train, y_train, shuffle=True)
val_batches = batchify(x_val, y_val, shuffle=False)
self.model = ElmoModel(len(i_to_class), dropout=self.dropout)
if CUDA:
self.model = self.model.cuda()
log.info(f'Parameters:\n{self.parameters()}')
log.info(f'Model:\n{self.model}')
parameters = list(self.model.classifier.parameters())
for mix in self.model.elmo._scalar_mixes:
parameters.extend(list(mix.parameters()))
self.optimizer = Adam(parameters)
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
patience=5,
verbose=True,
mode='max')
temp_prefix = get_tmp_filename()
self.model_file = f'{temp_prefix}.pt'
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
self.model_file,
monitor='test_acc')
])
log.info('Starting training')
epoch = 0
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_batches)
random.shuffle(train_batches)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(val_batches,
train=False)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
epoch += 1
def train(self, training_data):
log.info('Loading Quiz Bowl dataset')
train_iter, val_iter, dev_iter = QuizBowl.iters(
batch_size=self.batch_size,
lower=self.lowercase,
use_wiki=self.use_wiki,
n_wiki_sentences=self.n_wiki_sentences,
replace_title_mentions=self.wiki_title_replace_token,
sort_within_batch=True)
log.info(f'Training Data={len(training_data[0])}')
log.info(f'N Train={len(train_iter.dataset.examples)}')
log.info(f'N Test={len(val_iter.dataset.examples)}')
fields: Dict[str, Field] = train_iter.dataset.fields
self.page_field = fields['page']
self.n_classes = len(self.ans_to_i)
self.qanta_id_field = fields['qanta_id']
self.emb_dim = 300
self.text_field = fields['text']
log.info(f'Text Vocab={len(self.text_field.vocab)}')
log.info('Initializing Model')
self.model = RnnModel(self.n_classes,
text_field=self.text_field,
emb_dim=self.emb_dim,
n_hidden_units=self.n_hidden_units,
n_hidden_layers=self.n_hidden_layers,
nn_dropout=self.nn_dropout)
if CUDA:
self.model = self.model.cuda()
log.info(f'Parameters:\n{self.parameters()}')
log.info(f'Model:\n{self.model}')
self.optimizer = Adam(self.model.parameters())
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
patience=5,
verbose=True,
mode='max')
temp_prefix = get_tmp_filename()
self.model_file = f'{temp_prefix}.pt'
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
self.model_file,
monitor='test_acc')
])
log.info('Starting training')
epoch = 0
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_iter)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(val_iter)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
epoch += 1
def train(self, training_data: TrainingData) -> None:
if self.use_qb:
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
training_data)
if self.use_wiki:
wiki_dataset = WikipediaDataset(set(training_data[1]))
wiki_train_data = wiki_dataset.training_data()
w_x_train_text, w_train_y, _, _, _, _, _ = preprocess_dataset(
wiki_train_data,
train_size=1,
vocab=vocab,
class_to_i=class_to_i,
i_to_class=i_to_class)
x_train_text.extend(w_x_train_text)
y_train.extend(w_train_y)
else:
if self.use_wiki:
wiki_dataset = WikipediaDataset(set(training_data[1]))
wiki_train_data = wiki_dataset.training_data()
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
wiki_train_data)
else:
raise ValueError(
'use_wiki and use_qb cannot both be false, otherwise there is no training data'
)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.vocab = vocab
embeddings, embedding_lookup = load_embeddings(vocab=vocab,
expand_glove=True)
self.embeddings = embeddings
self.embedding_lookup = embedding_lookup
x_train = [
convert_text_to_embeddings_indices(q, embedding_lookup)
for q in x_train_text
]
for r in x_train:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = [
convert_text_to_embeddings_indices(q, embedding_lookup)
for q in x_test_text
]
for r in x_test:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.array(y_test)
self.n_classes = compute_n_classes(training_data[1])
n_batches_train, t_x_train, t_offset_train, t_y_train = batchify(
self.batch_size, x_train, y_train, truncate=True)
n_batches_test, t_x_test, t_offset_test, t_y_test = batchify(
self.batch_size, x_test, y_test, truncate=False)
self.vocab_size = embeddings.shape[0]
self.model = DanModel(self.vocab_size,
self.n_classes,
dropout_prob=self.dropout_prob,
k_softmaxes=self.k_softmaxes,
n_hidden_units=self.n_hidden_units,
non_linearity=self.non_linearity)
log.info(f'Parameters:\n{pformat(self.parameters())}')
log.info(f'Torch Model:\n{self.model}')
self.model.init_weights(initial_embeddings=embeddings)
if CUDA:
self.model = self.model.cuda()
self.optimizer = Adam(self.model.parameters(), lr=self.learning_rate)
self.criterion = nn.NLLLoss()
# self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
patience=5,
verbose=True)
tb_experiment = ' '.join(
f'{param}={value}' for param, value in
[('model',
'dan'), ('n_hidden_units',
self.n_hidden_units), ('dropout_prob',
self.dropout_prob),
('k_softmaxes',
self.k_softmaxes), ('non_linearity', self.non_linearity),
('learning_rate',
self.learning_rate), ('batch_size', self.batch_size)])
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor='test_loss', patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
'/tmp/dan.pt',
monitor='test_loss'),
Tensorboard(tb_experiment)
])
log.info('Starting training...')
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(n_batches_train,
t_x_train,
t_offset_train,
t_y_train,
evaluate=False)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(n_batches_test,
t_x_test,
t_offset_test,
t_y_test,
evaluate=True)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_loss)
log.info('Done training')
def train(self, training_data):
log.info('Loading Quiz Bowl dataset')
train_iter, val_iter, dev_iter = QuizBowl.iters(
batch_size=self.batch_size, lower=self.lowercase,
use_wiki=self.use_wiki, n_wiki_sentences=self.n_wiki_sentences,
replace_title_mentions=self.wiki_title_replace_token
)
log.info(f'N Train={len(train_iter.dataset.examples)}')
log.info(f'N Test={len(val_iter.dataset.examples)}')
fields: Dict[str, Field] = train_iter.dataset.fields
self.page_field = fields['page']
self.n_classes = len(self.ans_to_i)
self.qnum_field = fields['qnum']
self.text_field = fields['text']
self.emb_dim = self.text_field.vocab.vectors.shape[1]
log.info(f'Vocab={len(self.text_field.vocab)}')
log.info('Initializing Model')
self.model = TiedModel(
self.text_field, self.n_classes, emb_dim=self.emb_dim,
n_hidden_units=self.n_hidden_units, n_hidden_layers=self.n_hidden_layers,
nn_dropout=self.nn_dropout, sm_dropout=self.sm_dropout
)
if CUDA:
self.model = self.model.cuda()
log.info(f'Parameters:\n{self.parameters()}')
log.info(f'Model:\n{self.model}')
self.optimizer = Adam(self.model.parameters(), lr=self.lr)
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=5, verbose=True, mode='max')
manager = TrainingManager([
BaseLogger(log_func=log.info), TerminateOnNaN(), EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100), ModelCheckpoint(create_save_model(self.model), '/tmp/tied.pt', monitor='test_acc')
])
log.info('Starting training')
try:
import socket
from kuro import Worker
worker = Worker(socket.gethostname())
experiment = worker.experiment(
'guesser', 'Tied', hyper_parameters=conf['guessers']['Tied'],
metrics=[
'train_acc', 'train_loss', 'test_acc', 'test_loss'
], n_trials=5
)
trial = experiment.trial()
if trial is not None:
self.kuro_trial_id = trial.id
except ModuleNotFoundError:
trial = None
epoch = 0
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_iter)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(val_iter)
stop_training, reasons = manager.instruct(
train_time, train_loss, train_acc,
test_time, test_loss, test_acc
)
if trial is not None:
trial.report_metric('test_acc', test_acc, step=epoch)
trial.report_metric('test_loss', test_loss, step=epoch)
trial.report_metric('train_acc', train_acc, step=epoch)
trial.report_metric('train_loss', train_loss, step=epoch)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
epoch += 1
def train(self, training_data: TrainingData) -> None:
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
training_data
)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.vocab = vocab
embeddings, embedding_lookup = load_embeddings(vocab=vocab, expand_glove=True)
self.embeddings = embeddings
self.embedding_lookup = embedding_lookup
x_train = [convert_text_to_embeddings_indices(q, embedding_lookup) for q in x_train_text]
for r in x_train:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = [convert_text_to_embeddings_indices(q, embedding_lookup) for q in x_test_text]
for r in x_test:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.array(y_test)
self.n_classes = compute_n_classes(training_data[1])
n_batches_train, t_x_train, t_offset_train, t_y_train = batchify(
self.batch_size, x_train, y_train, truncate=True)
n_batches_test, t_x_test, t_offset_test, t_y_test = batchify(
self.batch_size, x_test, y_test, truncate=False)
self.model = DanModel(embeddings.shape[0], self.n_classes)
self.model.init_weights(initial_embeddings=embeddings)
self.model.cuda()
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate)
self.criterion = nn.CrossEntropyLoss()
manager = TrainingManager([
BaseLogger(log_func=log.info), TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1), MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model), '/tmp/dan.pt', monitor='test_acc')
# Tensorboard('dan', log_dir='tb-logs')
])
log.info('Starting training...')
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(
n_batches_train,
t_x_train, t_offset_train, t_y_train, evaluate=False
)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(
n_batches_test,
t_x_test, t_offset_test, t_y_test, evaluate=True
)
stop_training, reasons = manager.instruct(
train_time, train_loss, train_acc,
test_time, test_loss, test_acc
)
if stop_training:
log.info(' '.join(reasons))
break
log.info('Done training')
def train(self, training_data):
log.info("Loading Quiz Bowl dataset")
train_iter, val_iter, dev_iter = QuizBowl.iters(
batch_size=self.batch_size,
lower=self.lowercase,
use_wiki=self.use_wiki,
n_wiki_sentences=self.n_wiki_sentences,
replace_title_mentions=self.wiki_title_replace_token,
combined_ngrams=self.combined_ngrams,
unigrams=self.unigrams,
bigrams=self.bigrams,
trigrams=self.trigrams,
combined_max_vocab_size=self.combined_max_vocab_size,
unigram_max_vocab_size=self.unigram_max_vocab_size,
bigram_max_vocab_size=self.bigram_max_vocab_size,
trigram_max_vocab_size=self.trigram_max_vocab_size,
)
log.info(f"N Train={len(train_iter.dataset.examples)}")
log.info(f"N Test={len(val_iter.dataset.examples)}")
fields: Dict[str, Field] = train_iter.dataset.fields
self.page_field = fields["page"]
self.n_classes = len(self.ans_to_i)
self.qanta_id_field = fields["qanta_id"]
self.emb_dim = 300
if "text" in fields:
self.text_field = fields["text"]
log.info(f"Text Vocab={len(self.text_field.vocab)}")
if "unigram" in fields:
self.unigram_field = fields["unigram"]
log.info(f"Unigram Vocab={len(self.unigram_field.vocab)}")
if "bigram" in fields:
self.bigram_field = fields["bigram"]
log.info(f"Bigram Vocab={len(self.bigram_field.vocab)}")
if "trigram" in fields:
self.trigram_field = fields["trigram"]
log.info(f"Trigram Vocab={len(self.trigram_field.vocab)}")
log.info("Initializing Model")
self.model = DanModel(
self.n_classes,
text_field=self.text_field,
unigram_field=self.unigram_field,
bigram_field=self.bigram_field,
trigram_field=self.trigram_field,
emb_dim=self.emb_dim,
n_hidden_units=self.n_hidden_units,
n_hidden_layers=self.n_hidden_layers,
nn_dropout=self.nn_dropout,
pooling=self.pooling,
)
if CUDA:
self.model = self.model.cuda()
log.info(f"Parameters:\n{self.parameters()}")
log.info(f"Model:\n{self.model}")
self.optimizer = Adam(self.model.parameters())
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
patience=5,
verbose=True,
mode="max")
temp_prefix = get_tmp_filename()
self.model_file = f"{temp_prefix}.pt"
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor="test_acc", patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
self.model_file,
monitor="test_acc"),
])
log.info("Starting training")
epoch = 0
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_iter)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(val_iter)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(" ".join(reasons))
break
else:
self.scheduler.step(test_acc)
epoch += 1
def train(self, training_data: TrainingData):
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
training_data)
if self.use_wiki:
wiki_dataset = FilteredWikipediaDataset()
wiki_train_data = wiki_dataset.training_data()
w_x_train_text, w_train_y, _, _, _, _, _ = preprocess_dataset(
wiki_train_data,
train_size=1,
vocab=vocab,
class_to_i=class_to_i,
i_to_class=i_to_class)
x_train_text.extend(w_x_train_text)
y_train.extend(w_train_y)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.vocab = vocab
embeddings, embedding_lookup = load_embeddings(vocab=vocab,
expand_glove=True,
mask_zero=True)
self.embeddings = embeddings
self.embedding_lookup = embedding_lookup
x_train = [
convert_text_to_embeddings_indices(q,
embedding_lookup,
random_unk_prob=.05)
for q in x_train_text
]
for r in x_train:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = [
convert_text_to_embeddings_indices(q,
embedding_lookup,
random_unk_prob=.05)
for q in x_test_text
]
for r in x_test:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.array(y_test)
self.n_classes = compute_n_classes(training_data[1])
n_batches_train, t_x_train, lengths_train, t_y_train, _ = batchify(
self.batch_size, x_train, y_train, truncate=True)
n_batches_test, t_x_test, lengths_test, t_y_test, _ = batchify(
self.batch_size, x_test, y_test, truncate=False)
self.model = RnnModel(embeddings.shape[0], self.n_classes)
self.model.init_weights(embeddings=embeddings)
self.model.cuda()
self.optimizer = Adam(self.model.parameters(), lr=self.learning_rate)
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
'max',
patience=5,
verbose=True)
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
'/tmp/rnn.pt',
monitor='test_acc')
#Tensorboard('rnn', log_dir='tb-logs')
])
log.info('Starting training...')
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(n_batches_train,
t_x_train,
lengths_train,
t_y_train,
evaluate=False)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(n_batches_test,
t_x_test,
lengths_test,
t_y_test,
evaluate=True)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
log.info('Done training')
def train(self, training_data: TrainingData) -> None:
if self.use_qb:
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
training_data
)
if self.use_wiki:
wiki_dataset = WikipediaDataset(set(training_data[1]))
wiki_train_data = wiki_dataset.training_data()
w_x_train_text, w_train_y, _, _, _, _, _ = preprocess_dataset(
wiki_train_data, train_size=1, vocab=vocab, class_to_i=class_to_i, i_to_class=i_to_class
)
x_train_text.extend(w_x_train_text)
y_train.extend(w_train_y)
else:
if self.use_wiki:
wiki_dataset = WikipediaDataset(set(training_data[1]))
wiki_train_data = wiki_dataset.training_data()
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
wiki_train_data
)
else:
raise ValueError('use_wiki and use_qb cannot both be false, otherwise there is no training data')
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.vocab = vocab
embeddings, embedding_lookup = load_embeddings(vocab=vocab, expand_glove=True)
self.embeddings = embeddings
self.embedding_lookup = embedding_lookup
x_train = [convert_text_to_embeddings_indices(q, embedding_lookup) for q in x_train_text]
for r in x_train:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = [convert_text_to_embeddings_indices(q, embedding_lookup) for q in x_test_text]
for r in x_test:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.array(y_test)
self.n_classes = compute_n_classes(training_data[1])
log.info(f'Batching: {len(x_train)} train questions and {len(x_test)} test questions')
n_batches_train, t_x_train, t_offset_train, t_y_train = batchify(
self.batch_size, x_train, y_train, truncate=True)
n_batches_test, t_x_test, t_offset_test, t_y_test = batchify(
self.batch_size, x_test, y_test, truncate=False)
self.vocab_size = embeddings.shape[0]
self.model = DanModel(self.vocab_size, self.n_classes, embeddings=embeddings)
if CUDA:
self.model = self.model.cuda()
log.info(f'Model:\n{self.model}')
self.optimizer = Adam(self.model.parameters(), lr=self.learning_rate)
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer, patience=5, verbose=True, mode='max')
manager = TrainingManager([
BaseLogger(log_func=log.info), TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1), MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model), '/tmp/dan.pt', monitor='test_acc')
])
log.info('Starting training...')
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(
n_batches_train,
t_x_train, t_offset_train, t_y_train, evaluate=False
)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(
n_batches_test,
t_x_test, t_offset_test, t_y_test, evaluate=True
)
stop_training, reasons = manager.instruct(
train_time, train_loss, train_acc,
test_time, test_loss, test_acc
)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
log.info('Done training')
def train(self, training_data: TrainingData):
log.info('Preprocessing the dataset')
self.nlp = spacy.load('en', create_pipeline=custom_spacy_pipeline)
x_train_tokens, y_train, x_test_tokens, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
self.nlp, training_data)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.vocab = vocab
log.info('Loading word embeddings')
word_embeddings, multi_embedding_lookup = load_multi_embeddings(
multi_vocab=vocab)
self.word_embeddings = word_embeddings
self.multi_embedding_lookup = multi_embedding_lookup
log.info('Batching the dataset')
train_dataset = BatchedDataset(self.batch_size, multi_embedding_lookup,
x_train_tokens, y_train)
test_dataset = BatchedDataset(self.batch_size, multi_embedding_lookup,
x_test_tokens, y_test)
self.n_classes = compute_n_classes(training_data[1])
log.info('Initializing neural model')
self.model = RnnEntityModel(len(multi_embedding_lookup.word),
len(multi_embedding_lookup.pos),
len(multi_embedding_lookup.iob),
len(multi_embedding_lookup.ent_type),
self.n_classes)
self.model.init_weights(word_embeddings=word_embeddings)
self.model.cuda()
self.optimizer = Adam(self.model.parameters(), lr=self.learning_rate)
self.criterion = nn.CrossEntropyLoss()
self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optimizer,
'max',
patience=5,
verbose=True)
manager = TrainingManager([
BaseLogger(log_func=log.info),
TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1),
MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model),
'/tmp/rnn_entity.pt',
monitor='test_acc')
#Tensorboard('rnn_entity', log_dir='tb-logs')
])
log.info('Starting training...')
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(train_dataset,
evaluate=False)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(test_dataset,
evaluate=True)
stop_training, reasons = manager.instruct(train_time, train_loss,
train_acc, test_time,
test_loss, test_acc)
if stop_training:
log.info(' '.join(reasons))
break
else:
self.scheduler.step(test_acc)
log.info('Done training')
def train(self, training_data) -> None:
log.info('Preprocessing data')
x_train_text, y_train, x_test_text, y_test, vocab, class_to_i, i_to_class = preprocess_dataset(
training_data
)
self.class_to_i = class_to_i
self.i_to_class = i_to_class
self.vocab = vocab
embeddings, embedding_lookup = load_embeddings(vocab=vocab, expand_glove=True, mask_zero=True)
self.embeddings = embeddings
self.embedding_lookup = embedding_lookup
x_train = [convert_text_to_embeddings_indices(q, embedding_lookup) for q in x_train_text]
for r in x_train:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = [convert_text_to_embeddings_indices(q, embedding_lookup) for q in x_test_text]
for r in x_test:
if len(r) == 0:
r.append(embedding_lookup['UNK'])
x_test = np.array(x_test)
y_test = np.array(y_test)
log.info('Batching data')
n_batches_train, t_x_train, lengths_train, masks_train, t_y_train = batchify(
self.batch_size, x_train, y_train, truncate=True
)
n_batches_test, t_x_test, lengths_test, masks_test, t_y_test = batchify(
self.batch_size, x_test, y_test, truncate=False, shuffle=False
)
self.n_classes = compute_n_classes(training_data[1])
log.info('Creating model')
self.model = BCN(
300, 500, embeddings.shape[0], self.n_classes,
We=torch.from_numpy(embeddings)
).cuda()
self.optimizer = Adam(self.model.parameters())
self.criterion = nn.NLLLoss()
log.info(f'Model:\n{self.model}')
manager = TrainingManager([
BaseLogger(log_func=log.info), TerminateOnNaN(),
EarlyStopping(monitor='test_acc', patience=10, verbose=1), MaxEpochStopping(100),
ModelCheckpoint(create_save_model(self.model), '/tmp/bcn.pt', monitor='test_acc'),
Tensorboard('bcn')
])
log.info('Starting training...')
while True:
self.model.train()
train_acc, train_loss, train_time = self.run_epoch(
n_batches_train,
t_x_train, lengths_train, masks_train, t_y_train, evaluate=False
)
self.model.eval()
test_acc, test_loss, test_time = self.run_epoch(
n_batches_test,
t_x_test, lengths_test, masks_test, t_y_test, evaluate=True
)
stop_training, reasons = manager.instruct(
train_time, train_loss, train_acc,
test_time, test_loss, test_acc
)
if stop_training:
log.info(' '.join(reasons))
break