def test_fastqa():
tf.reset_default_graph()
data = load_jack('tests/test_data/squad/snippet_jtr.json')
# fast qa must be initialized with existing embeddings, so we create some
embeddings = load_embeddings('./tests/test_data/glove.840B.300d_top256.txt', 'glove')
# we need a vocabulary (with embeddings for our fastqa_reader, but this is not always necessary)
vocab = Vocab(emb=embeddings, init_from_embeddings=True)
# ... and a config
config = {
"batch_size": 1,
"repr_dim": 10,
"repr_dim_input": embeddings.lookup.shape[1],
"with_char_embeddings": True
}
# create/setup reader
shared_resources = SharedResources(vocab, config)
input_module = XQAInputModule(shared_resources)
model_module = FastQAModule(shared_resources)
output_module = XQAOutputModule()
reader = TFReader(shared_resources, input_module, model_module, output_module)
reader.setup_from_data(data, is_training=True)
loss = reader.model_module.tensors[Ports.loss]
optimizer = tf.train.AdagradOptimizer(learning_rate=0.01)
min_op = optimizer.minimize(loss)
session = model_module.tf_session
session.run(tf.global_variables_initializer())
for epoch in range(0, 10):
for batch in reader.input_module.batch_generator(data, 1, False):
feed_dict = reader.model_module.convert_to_feed_dict(batch)
loss_value, _ = session.run((loss, min_op), feed_dict=feed_dict)
print(loss_value)
def main(config,
loader,
debug,
debug_examples,
embedding_file,
embedding_format,
experiments_db,
reader,
train,
num_train_examples,
dev,
num_dev_examples,
test,
vocab_from_embeddings):
logger.info("TRAINING")
if 'JACK_TEMP' not in os.environ:
jack_temp = os.path.join(tempfile.gettempdir(), 'jack', str(uuid.uuid4()))
os.environ['JACK_TEMP'] = jack_temp
logger.info("JACK_TEMP not set, setting it to %s. Might be used for caching." % jack_temp)
else:
jack_temp = os.environ['JACK_TEMP']
if not os.path.exists(jack_temp):
os.makedirs(jack_temp)
if experiments_db is not None:
ex.observers.append(SqlObserver.create('sqlite:///%s' % experiments_db))
if debug:
train_data = loaders[loader](train, debug_examples)
logger.info('loaded {} samples as debug train/dev/test dataset '.format(debug_examples))
dev_data = train_data
test_data = train_data
if embedding_file is not None and embedding_format is not None:
emb_file = 'glove.6B.50d.txt'
embeddings = load_embeddings(path.join('data', 'GloVe', emb_file), 'glove')
logger.info('loaded pre-trained embeddings ({})'.format(emb_file))
ex.current_run.config["repr_dim_input"] = 50
else:
embeddings = Embeddings(None, None)
else:
train_data = loaders[loader](train, num_train_examples)
dev_data = loaders[loader](dev, num_dev_examples)
test_data = loaders[loader](test) if test else None
logger.info('loaded train/dev/test data')
if embedding_file is not None and embedding_format is not None:
embeddings = load_embeddings(embedding_file, embedding_format)
logger.info('loaded pre-trained embeddings ({})'.format(embedding_file))
ex.current_run.config["repr_dim_input"] = embeddings.lookup[0].shape[0]
else:
embeddings = None
if ex.current_run.config["vocab_from_embeddings"]:
raise RuntimeError("If you want to create vocab from embeddings, embeddings have to be provided")
vocab = Vocab(emb=embeddings, init_from_embeddings=vocab_from_embeddings)
# build JTReader
checkpoint()
parsed_config = ex.current_run.config
ex.run('print_config', config_updates=parsed_config)
# name defaults to name of the model
if 'name' not in parsed_config or parsed_config['name'] is None:
parsed_config['name'] = reader
shared_resources = SharedResources(vocab, parsed_config)
jtreader = readers.readers[reader](shared_resources)
checkpoint()
try:
jtrain(jtreader, train_data, test_data, dev_data, parsed_config, debug=debug)
finally: # clean up temporary dir
if os.path.exists(jack_temp):
shutil.rmtree(jack_temp)
def main(batch_size, clip_value, config, loader, debug, debug_examples, dev,
embedding_file, embedding_format, experiments_db, epochs, l2,
optimizer, learning_rate, learning_rate_decay, log_interval,
validation_interval, model, model_dir, seed, tensorboard_folder, test,
train, vocab_from_embeddings, write_metrics_to):
logger.info("TRAINING")
if experiments_db is not None:
ex.observers.append(SqlObserver.create('sqlite:///%s' %
experiments_db))
if debug:
train_data = loaders[loader](train, debug_examples)
logger.info(
'loaded {} samples as debug train/dev/test dataset '.format(
debug_examples))
dev_data = train_data
test_data = train_data
if embedding_file is not None and embedding_format is not None:
emb_file = 'glove.6B.50d.txt'
embeddings = load_embeddings(path.join('data', 'GloVe', emb_file),
'glove')
logger.info('loaded pre-trained embeddings ({})'.format(emb_file))
ex.current_run.config["repr_dim_input"] = 50
else:
embeddings = Embeddings(None, None)
else:
train_data = loaders[loader](train)
dev_data = loaders[loader](dev)
test_data = loaders[loader](test) if test else None
logger.info('loaded train/dev/test data')
if embedding_file is not None and embedding_format is not None:
embeddings = load_embeddings(embedding_file, embedding_format)
logger.info(
'loaded pre-trained embeddings ({})'.format(embedding_file))
ex.current_run.config["repr_dim_input"] = embeddings.lookup[
0].shape[0]
else:
embeddings = None
if ex.current_run.config["vocab_from_embeddings"]:
raise RuntimeError(
"If you want to create vocab from embeddings, embeddings have to be provided"
)
vocab = Vocab(emb=embeddings, init_from_embeddings=vocab_from_embeddings)
# build JTReader
checkpoint()
parsed_config = ex.current_run.config
ex.run('print_config', config_updates=parsed_config)
# name defaults to name of the model
if 'name' not in parsed_config or parsed_config['name'] is None:
parsed_config['name'] = model
shared_resources = SharedResources(vocab, parsed_config)
reader = readers.readers[model](shared_resources)
checkpoint()
configuration = {
'seed': seed,
'clip_value': clip_value,
'batch_size': batch_size,
'epochs': epochs,
'l2': l2,
'optimizer': optimizer,
'learning_rate': learning_rate,
'learning_rate_decay': learning_rate_decay,
'log_interval': log_interval,
'validation_interval': validation_interval,
'tensorboard_folder': tensorboard_folder,
'model': model,
'model_dir': model_dir,
'write_metrics_to': write_metrics_to
}
jtrain(reader, train_data, test_data, dev_data, configuration, debug=debug)
def run(loader, debug, debug_examples, embedding_file, embedding_format,
repr_dim_task_embedding, reader, train, num_train_examples, dev,
num_dev_examples, test, vocab_from_embeddings, **kwargs):
logger.info("TRAINING")
# build JTReader
parsed_config = ex.current_run.config
ex.run('print_config', config_updates=parsed_config)
if 'JACK_TEMP' not in os.environ:
jack_temp = os.path.join(tempfile.gettempdir(), 'jack',
str(uuid.uuid4()))
os.environ['JACK_TEMP'] = jack_temp
logger.info(
"JACK_TEMP not set, setting it to %s. Might be used for caching." %
jack_temp)
else:
jack_temp = os.environ['JACK_TEMP']
if not os.path.exists(jack_temp):
os.makedirs(jack_temp)
if debug:
train_data = loaders[loader](train, debug_examples)
logger.info(
'loaded {} samples as debug train/dev/test dataset '.format(
debug_examples))
dev_data = train_data
test_data = train_data
if embedding_file is not None and embedding_format is not None:
emb_file = 'glove.6B.50d.txt'
embeddings = load_embeddings(path.join('data', 'GloVe', emb_file),
'glove')
logger.info('loaded pre-trained embeddings ({})'.format(emb_file))
else:
embeddings = None
else:
train_data = loaders[loader](train, num_train_examples)
dev_data = loaders[loader](dev, num_dev_examples)
test_data = loaders[loader](test) if test else None
logger.info('loaded train/dev/test data')
if embedding_file is not None and embedding_format is not None:
embeddings = load_embeddings(embedding_file, embedding_format)
logger.info(
'loaded pre-trained embeddings ({})'.format(embedding_file))
else:
embeddings = None
if vocab_from_embeddings:
raise ValueError(
"If you want to create vocab from embeddings, embeddings have to be provided"
)
vocab = Vocab(vocab=embeddings.vocabulary if vocab_from_embeddings
and embeddings is not None else None)
if repr_dim_task_embedding < 1 and embeddings is None:
raise ValueError(
"Either provide pre-trained embeddings or set repr_dim_task_embedding > 0."
)
# name defaults to name of the model
if 'name' not in parsed_config or parsed_config['name'] is None:
parsed_config['name'] = reader
shared_resources = SharedResources(vocab, parsed_config, embeddings)
jtreader = readers.readers[reader](shared_resources)
try:
jtrain(jtreader,
train_data,
test_data,
dev_data,
parsed_config,
debug=debug)
finally: # clean up temporary dir
if os.path.exists(jack_temp):
shutil.rmtree(jack_temp)
